Culture and Methodologies

In today's rapidly evolving and highly competitive tech landscape, staying ahead of the curve is essential for career growth and success. As we look ahead to 2024, certain tech skills are poised to be in high demand, driving innovation and shaping the future of various industries. In this article, we'll explore the top 5 in-demand tech skills for 2024 and how they can propel your career forward, helping you stay relevant and get hired in the competitive job market. Artificial Intelligence and Machine Learning (AI/ML) Artificial intelligence (AI) and machine learning (ML) are revolutionizing industries ranging from healthcare to finance. Understanding key concepts like supervised learning, unsupervised learning, and reinforcement learning is crucial for harnessing the power of AI/ML. Moreover, proficiency in popular frameworks and libraries such as TensorFlow, PyTorch, and scikit-learn is highly sought after. Continuous learning in this dynamic field is essential to stay relevant and competitive. Check the learning resources at the end of this article to stay updated in the tech world. AI/ML skills are in high demand, with companies across industries looking for professionals who can develop innovative AI solutions to solve complex problems. Focus on Generative AI Solutions With the advancement of generative AI solutions, software engineers should focus on understanding and leveraging these technologies to drive innovation. This includes gaining familiarity with concepts like neural networks, deep learning, and generative adversarial networks (GANs). Hands-on experience with generative AI tools and frameworks such as TensorFlow, PyTorch, and OpenAI's GPT models is essential for developing practical skills in this area. Additionally, software engineers should explore specialization in specific applications of generative AI, such as natural language processing (NLP), computer vision, or creative content generation, to capitalize on emerging opportunities in the field. Cybersecurity With the increasing frequency and sophistication of cyber threats, cybersecurity has never been more important. Cybersecurity professionals play a vital role in protecting organizations from data breaches and ensuring the integrity of sensitive information. Roles such as cybersecurity analyst, penetration tester, and security architect are in high demand. Keeping abreast of the latest cybersecurity trends, including zero trust security and threat intelligence, is essential for safeguarding digital assets. Cybersecurity skills are highly valued by employers, and professionals with expertise in this area are in high demand as organizations prioritize security measures to protect their data and infrastructure. Cloud Computing Cloud computing continues to revolutionize the way organizations manage their IT infrastructures. Cloud platforms like AWS, Azure, and GCP offer scalability, flexibility, and cost-efficiency. Proficiency in cloud services and architectures is essential for building and maintaining modern cloud environments. Cloud certifications validate your skills and demonstrate your expertise to potential employers, making them a valuable asset in today's job market. Cloud computing skills are in high demand as companies migrate their operations to the cloud and seek professionals who can design, implement, and manage cloud-based solutions effectively. Several topics like this are discussed on my YouTube channel. Please visit. Appreciate your support. Data Science and Big Data Analytics Data has become the lifeblood of modern businesses, driving decision-making and innovation. Data science and big data analytics skills are in high demand across industries. From customer segmentation to predictive maintenance, the applications of data analytics are vast and varied. Tools like Tableau and Power BI are invaluable for visualizing data and communicating insights effectively. Developing expertise in data analysis and visualization is key to unlocking the full potential of data-driven decision-making. Data science and analytics skills are in high demand as companies seek professionals who can derive actionable insights from large and complex datasets to drive business growth and innovation. DevOps and Automation DevOps practices enable organizations to deliver software faster and more reliably through collaboration and automation. DevOps professionals play a crucial role in building and maintaining scalable and resilient systems. Tools like Jenkins, Ansible, Docker, and Kubernetes are widely used for automating software development and deployment processes. Embracing a culture of continuous improvement and cross-functional collaboration is essential for success in the world of DevOps. DevOps and automation skills are in high demand as companies adopt agile methodologies and seek professionals who can streamline software development processes and improve operational efficiency. In conclusion, acquiring and developing skills in the top 5 in-demand tech areas for 2024 is essential for career advancement and staying competitive in today's job market. Whether you're a seasoned professional or just starting your tech journey, investing in these skills will position you for success in the years to come. Keep learning, stay curious, and embrace the opportunities that technology has to offer. Must Reads for Continuous Learning System Design Head First Design Patterns Clean Code: A Handbook of Agile Software Craftsmanship Java Concurrency in Practice Java Performance: The Definitive Guide Designing Data-Intensive Applications Designing Distributed Systems Clean Architecture Kafka — The Definitive Guide Becoming An Effective Software Engineering Manager

TL; DR: Getting Hired as a Scrum Master or Agile Coach Are you considering a new Scrum Master or Agile Coach job, but you are not sure that it is the right organization? Don’t worry: there are four steps of proactive research to identify suitable employers or clients for getting hired as a Scrum Master and avoid disappointment later. I have used those four steps for years to identify organizations I would like to work with, and they never failed me. Read on and learn how to employ search engines, LinkedIn’s people search, reach out to peers in the agile community, and analyze the event markets in the quest for your next Scrum Master job. The Scrum Master Job Market Is Challenging We have all heard the news that organizations question the usefulness of employing Scrum Masters and cutting back on offering job opportunities. In some cases, they even laid off all Scrum Masters and Agile Coaches. Times are challenging, and many peers must make ends meet, considering more “tolerance” regarding job opportunities. While I understand the approach, I like to advocate for preparing yourself properly in advance of getting hired as a Scrum Master to avoid future disappointment with new clients or employers. Therefore, if looking for a new Scrum Master job, consider two questions: Do I want to work for a developing agile organization (of the late majority) where my work will likely be met with resistance at multiple levels? Alternatively, how do I identify an organization that established agile practices compatible with my mindset? The two questions are relevant to applying to available positions and identifying suitable employers or clients for a proactive application. How To Get an Idea of an Organization’s Maturity Regarding Scrum or “Agile” While it is impossible to assess an organization’s “agile maturity” — if there is such a thing — solely from the outside, it is possible to acquire enough of an understanding of its agile practices this way. That understanding would allow for asking the right questions at a later stage; for example, during an initial job interview. Or, you may conclude after your research — thus early in the assessment process (see below) — that the organization is not compatible with your expectations of a future employer or client. (Consider the popular saying: "There is no job interesting enough that you just couldn’t walk away from it.") The good news is that all organizations that genuinely embrace agile practices are usually openly talking about their journeys (unless they need to honor compliance rules) and are transparent and actively supporting the agile community. The reason for this support is simple: being transparent and supportive is the best way to pitch the organization (and its agile culture) to prospective new team members. The war for talent is even more imminent for agile practitioners. The necessity of critical information is the basis for all research activity during the three distinct phases of your assessment process prior to getting hired as a Scrum Master: Proactive research Job advertisement Job interview Getting Hired as a Scrum Master, Phase 1: Proactive Research The proactive research comprises four elements: search engines (Google, Bing, YouTube, etc.), LinkedIn people search, reaching out to peers in the industry or communities, and analyzing the event markets. Source 1: An Opportunistic Search via Google, Bing, or YouTube As a first step, always search the organization’s name in combination with a variety of agile-related keywords, such as: Agile Lean Scrum Scrum Master ScrumMaster Product Owner Kanban SAFe LeSS Nexus DevOps Continuous integration Continuous delivery Design thinking Lean startup Tip Use additional search parameters to narrow down the search results. For example, the query "scrum master" site:age-of-product.com will return all articles on Age-of-Product.com that include the term “scrum master.” (Learn more about advanced search on Google.) The purpose of this exercise before getting hired as a Scrum Master is to discover an organization’s use of agile practices and the associated fluency level by shedding some light on questions such as: Scrum, Kanban, XP, Lean UX, Design Thinking — What are they practicing? Are the current Scrum Masters or Agile Coaches working at the organization? How many engineers or engineering teams are working for the organization? What is the size ratio between the product management and engineering teams? Is the organization practicing continuous product discovery? Is the organization practicing DevOps? The initial search results will provide a first impression, directing further searches of blog posts, videos of conferences or local meetups, slide decks, podcasts, or threads in communities. A truly agile organization will leave traces of a large variety of content. The mere quantity of results, though, does not signal that the organization in question has already passed the test, so to speak. There is no way to avoid checking the content. Here’s an example: InfoQ — a community news site facilitating the spread of knowledge and innovation in professional software development — has a rigorous editorial process and focuses on delivering quality content to its audience. Contrary to InfoQ’s standards, there are quite a few articles on Medium.com, for example, that could raise eyebrows for scrutiny. A good rule of thumb when scanning search results is noting the diversity of sources. If you find content only on the company blog, and it has barely been shared or commented upon, it might hint that the content is not relevant enough to be of interest within the Agile community. Advanced Tips Search for the title of a particular content piece on X or Twitter and have a look at the search results: Who from the Agile community is sharing this content? Use sites like BuzzSumo for content research. While BuzzSumo is a paid service, they offer a generous 30-day free trial period. Source 2: LinkedIn’s People Search Another good source for research on the target organization before getting hired as a Scrum Master is LinkedIn’s people search. You can list results by search term and then filter them, for example, by company name and location. (Here is an example of Scrum Masters working for Accenture in North America which lists currently about 2,800 results.) And while you’re at it, why not reach out to someone listed in the search results who is in your LinkedIn network? Or ask someone from your network who may introduce you to a person from the target organization you would like to interview about their Agile mindset? Please note, though, that internal job titles may differ from your vocabulary and impact the accuracy of the search results. Source 3: Ask Peers for Help via Reddit, Hacker News, the Hands-on Agile Slack Community, and LinkedIn Groups It is also beneficial to extend the initial Google search to online communities such as Reddit or Hacker News (HN), to name a couple. Both communities allow for posting articles as well as questions. The archive of HN is of particular interest. It is not just because of the sheer number of available articles or threads there but also the partly heated discussions going on in the comments. Be aware, though, that "Scrum" as a concept is challenged by a lot of outspoken community members (namely, independent developers) both on Reddit and HN. Beyond passively scanning the archives, posting a direct question to peers is an alternative. HN is likely a waste of time, and if using Reddit – choose the Subreddits r/agile and r/scrum for a possibly better outcome. Note: Don’t forget – haters will hate, and trolls just want to play. Do not take it personally if your search on Reddit or HN is not taking the direction you desire. You can probably expect more support by asking the 19,000 members of the "Hands-on Agile" Slack community for help on getting hired as a Scrum Master. This is a worldwide community of Scrum Masters, agile coaches, and Product Owners that has proven to be very supportive. There are also LinkedIn groups available that focus on Scrum and agile practices — some with more than 100,000 members. After having joined them, post your question(s), remembering to be compliant with the group rules. Expect your first posts to be moderated, though. Some recommended LinkedIn groups — in no particular order — getting hired as a Scrum Master are: Agile Clinic Scrum Practitioners Agile World Group Scrum.org Agile Agile Project Management Agile Coaching Scrum Practitioners, Scrum Masters If posting a question to a LinkedIn group, expect to monitor it carefully and interact with answering members in a timely manner: not interacting with responding group members may be considered rude and possibly lead to being banned from posting in the group again. (Read More: Etiquette in technology (Netiquette).) Also, try Quora, directing a question on getting hired as a Scrum Master to Quora members active in the agile realm as to whether the organization of interest has an agile mindset. (Note: In doing so, avoid asking anonymous questions — which tend to have a significantly lower answering rate.) Lastly, the two main Scrum certification bodies — Scrum.org and ScrumAlliance — provide access to directories of certificate holders. In both cases, you need the certificate holder’s email address access to a public profile via the search function. A faster way to access a known individual’s public profile is often the advanced Google search, see above. Source 4: Is the Organization Sponsoring or Organizing Meetups, Barcamps, or Conferences? In my eyes, supporting public or virtual events is the highest form of contribution to the agile community by an organization. There are four different levels of engagement — no matter whether the event is a virtual event or an in-person event: Organizing conferences (or Barcamps) Sponsoring conferences Providing speakers at conferences Sponsoring local Meetups and Barcamps by providing a venue Suppose an organization provides this level of support to the agile community. In that case, the talk about this will undoubtedly be on the company blog, an engineering or product-management-related blog, or in a press release in their public relations section. In the unlikely case that any reference cannot be found, just contact the Public Relations department who will provide the required information. A. Browsing Conference Sites for Sponsors Conference sites are a good ground for identifying prospective organizations when considering applying for a Scrum Master position. Check carefully for two things: sponsors and speakers. Search for sponsors that are practicing agile in their daily operations. Usually, a larger sponsor package will also include a speaking slot at the conference. Attending such a session — whether in person or virtually — will provide direct access to the speaker and thus a first contact in the inner circle of that organization’s agile practitioners. This tends to be valuable: People departments often rely on the private networks of the organization’s available agile practitioners to identify suitable candidates for job openings as a Scrum Master. (Accordingly, attending local Meetups can also be a worthwhile investment for job seekers.) B. Browsing Conference Sites for Speakers Personally, a more promising approach, by comparison, is to search for non-professional speakers who are aligned with an organization that is not sponsoring the conference. These speakers may indicate a suitable prospective employer or client after already having gone through the selection process for speaking proposals and vetting their contribution for originality. The same approach can apply to contributions at Barcamps, although a disadvantage is that the critical information is only available during an event. While the speaker list of a conference is available in advance to stimulate ticket sales, it is the nature of a Barcamp that the schedule, and hence the speaker list, is available only on the day of the Barcamp. If you are already planning to attend a Barcamp, it may just be an inconvenience and not a concern. Timing is crucial, though, so please keep in mind that tickets for Barcamps are often sold out within minutes. (For example, the 600-plus tickets for the UXCamp Europe were regularly gone in a few minutes until the organizers switched to a lottery.) There are numerous conferences regarding agile practices, so here are just some of the listings: Agile Alliance Agile Testing Days Agile on the Beach QCon New York For an additional listing of conferences, check the Top 10 Agile conferences to attend in 2024. Lastly, the big conferences are often considered must-attend events — useful, for example, to gain or improve professional visibility within the agile community. Alternatively, smaller conferences often prove to be more effective by providing information that helps identify a suitable, prospective agile organization. The larger the conference, the more possibility of noise camouflaging that information, complicating getting hired as a Scrum Master. C. Browsing Meetup.com Meetup.com is a great site to discover which events of the Agile community are happening locally and who is organizing them. There are thousands of meetups worldwide covering the topics of agile frameworks and practices, software engineering, and product development in general. Since the pandemic, many Meetup groups switched to virtual events, attracting more members from outside their original reach. For example, the Hands-on Agile Meetup community has grown from about 1,500 members in March 2020 to more than 6,500 members worldwide in February 2024. The new members from all over the globe — from Vietnam to Brazil to the United States — added tremendous expertise and diversity, making the events more inclusive and a much better experience for everyone. Therefore, Meetup.com is an excellent place to look for answers and peer support. Conclusion: Getting Hired as a Scrum Master Suppose you are looking for a Scrum Master job. In that case, it is possible to understand the agile mindset of an organization in advance by applying the research approaches sketched above. Investing a few hours in advance may save you from later disappointment when your Scrum Master job may turn out to be vastly different from what was pitched or promised to you before.

By Stefan Wolpers CORE

Microsoft Research, a key player in the technology research landscape, has established a unique lab structure that fosters tech leadership and innovation. In this article, we delve into the various aspects of Microsoft Research Labs' management approach, highlighting data-driven insights that showcase their success in fostering innovation and leadership. Encouraging Autonomy and Flexibility: Impact on Research Output Researchers at Microsoft Research enjoy a high level of autonomy and flexibility in selecting their research projects. This freedom nurtures creativity, risk-taking, and groundbreaking ideas. As a result, Microsoft Research has published over 20,000 peer-reviewed publications and filed more than 10,000 patents since its inception in 1991. Flat Organizational Structure: Accelerating Decision-Making Microsoft Research's flat organizational structure facilitates direct access to senior management and decision-makers. By reducing bureaucracy and streamlining decision-making, researchers can quickly pivot their projects and respond to new opportunities. This agility has contributed to the rapid development and integration of innovations like Microsoft Azure Machine Learning, HoloLens, and Xbox Kinect into commercial products. Goal Setting and Performance Evaluation: Fostering Collaboration Microsoft Research emphasizes setting clear goals and expectations for researchers, focusing on both short-term objectives and long-term visions. By tracking metrics such as collaboration levels, knowledge sharing, and interdisciplinary research, Microsoft Research has successfully fostered a culture of teamwork and innovation. For example, the company's annual internal TechFest event brings together researchers from different labs to showcase their work and collaborate on new ideas. Collaboration Tools and Platforms: Facilitating Global Connectivity Microsoft Research leverages various tools and platforms to facilitate seamless collaboration among researchers, both within and across labs. Researchers have access to cutting-edge resources like Microsoft Teams, SharePoint, and Azure DevOps, enabling them to work together efficiently across geographical boundaries. This global connectivity has led to numerous cross-lab collaborations, such as Project Premonition, which combines expertise from the Redmond, Cambridge, and New York labs to develop early warning systems for disease outbreaks. Internal Knowledge Sharing Events: Promoting Continuous Learning Microsoft Research organizes various internal events, such as conferences, workshops, and lecture series, where researchers can share their work, learn from others, and build relationships. These events have proven effective in fostering a culture of knowledge sharing and continuous learning. For instance, the Microsoft Research Faculty Summit brings together hundreds of academic researchers each year to discuss emerging trends and collaborate on new projects. Emphasis on Diversity and Inclusion: Driving Innovation Microsoft Research recognizes the value of diversity and inclusion in driving innovation. The organization actively promotes a diverse workforce, ensuring that researchers from different backgrounds, cultures, and perspectives can contribute their unique insights. As a result, Microsoft Research has received numerous accolades for its commitment to diversity, including being named one of the "Top 50 Companies for Diversity" by DiversityInc. Continuous Learning and Skill Development: Preparing Researchers for the Future Microsoft Research is committed to supporting the continuous learning and skill development of its researchers. The organization offers various resources, such as training programs, workshops, and access to online courses to help researchers stay up-to-date with the latest advancements in their fields and develop new skills. As an example, Microsoft Research's partnership with MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) allows researchers to participate in joint research projects and gain exposure to cutting-edge techniques. Conclusion Microsoft Research's lab structure plays a crucial role in fostering tech leadership and driving innovation. By encouraging autonomy, promoting collaboration, setting clear goals, leveraging modern tools, and emphasizing diversity and continuous learning, Microsoft Research creates an environment where researchers can thrive and contribute to the cutting edge of technology. The data-driven insights presented in this article demonstrate the effectiveness of Microsoft Research's management approach, serving as a model for other organizations looking to foster innovation and develop tech leaders of the future.

By Arun Pandey CORE

Artificial intelligence (AI) is one of the twenty-first century's most exciting and rapidly developing fields. Artificial intelligence has the potential to transform a variety of industries, including education, healthcare, retail, e-commerce, public relations, small businesses, recruitment, services, and manufacturing. AI can also improve the quality of life for millions of people worldwide by solving complex problems, increasing efficiency, and developing novel solutions. But how do you become an AI expert and enter this exciting field? What skills, education, and career paths should you pursue? In this blog post, we will answer these questions and provide you with a detailed guide on how to become an AI expert. What Is an AI Expert? An AI expert is a professional who understands the fundamentals, methods, and applications of artificial intelligence. An AI expert can create, develop, and deploy intelligent systems capable of performing tasks that would normally require human intelligence, such as natural language processing, computer vision, speech recognition, machine learning, and robotics. An AI expert can work in a variety of fields, including research, engineering, consulting, and teaching. An AI expert can also focus on a specific subfield of AI, such as machine learning, deep learning, computer vision, natural language processing, or robotics. What Are the Skills and Education Required To Become an AI Expert? To become an AI expert, you must have a solid foundation in mathematics, statistics, computer science, and programming. You also need a solid understanding of AI fundamentals such as algorithms, data structures, data analysis, machine learning, and deep learning. Depending on your desired career path, you may also need a bachelor's, master's, or doctoral degree in AI, computer science, or a related subject. Alternatively, you can take online courses, certifications, or boot camps to learn the fundamental skills and concepts of artificial intelligence. Coursera, Udemy, edX, and Udacity are some of the most popular online platforms for AI courses. Some popular AI certifications include the IBM AI Engineering Professional Certificate, Google TensorFlow Developer Certificate, and Microsoft Azure AI Engineer Associate. What Are the Career Paths and Opportunities for AI Experts? There are numerous career paths and opportunities for AI experts across industries and sectors. Some of the most popular and lucrative AI jobs are: AI engineer: An AI engineer is a professional who uses AI and machine learning techniques to create applications and systems that improve organizational efficiency. An AI engineer creates the tools, systems, and processes that allow AI to be applied to real-world problems. An AI engineer may also work with data scientists, machine learning engineers, and other AI professionals to develop and implement AI solutions. The average salary for an AI engineer is $113,000. Machine learning engineer: A machine learning engineer is a professional who conducts research, builds, and designs the artificial intelligence (AI) that powers machine learning. A machine learning engineer maintains and improves existing AI systems while also conducting experiments and tests to assess and optimize their performance. A machine learning engineer collaborates closely with data scientists and AI engineers to create machine learning models and algorithms. The average salary for a machine learning engineer is $123,000. Data engineer: A data engineer is a professional who creates systems for collecting, managing, and converting raw data into information that data scientists, business analysts, and other data professionals can interpret. A data engineer makes data accessible and reliable, allowing organizations to evaluate and improve their performance. A data engineer works with a variety of data sources, including databases, APIs, web scraping, and cloud services. The average salary for a data engineer is $104,000. Robotics engineer: A robotics engineer is a professional who creates robotic applications for a variety of industries, including automobiles, manufacturing, defense, and medicine. A robotics engineer creates, tests, and implements new products or prototypes that incorporate robotic technology. A robotics engineer also works with a wide range of hardware and software components, including sensors, actuators, controllers, and programming languages. The average salary for a robotics engineer is $96,000. AI researcher: An AI researcher is a professional who conducts scientific research into the theory and application of artificial intelligence. An AI researcher investigates new ideas, methods, and applications of AI and publishes their findings in academic journals, conferences, and books. An AI researcher collaborates with other researchers, academics, and industry partners to further the field of AI. The average annual salary for an AI researcher is $119,000. AI consultant: An AI consultant is a professional who offers expert advice and guidance on the use and implementation of artificial intelligence to a variety of clients and organizations. An AI consultant analyzes the client's needs and goals and recommends the best AI solutions to help them achieve their goals. An AI consultant also assists clients with the planning, execution, and evaluation of their AI projects. The average annual salary for an AI consultant is $102,000. How To Become an AI Expert: A Summary Becoming an AI expert is a rewarding and challenging career path that can lead to numerous opportunities and possibilities for you. To become an AI expert, you must have a solid background in mathematics, statistics, computer science, and programming. You should also have a solid understanding of the fundamentals and applications of artificial intelligence. You can learn these skills and knowledge through formal education, online courses, certifications, or boot camps. There are numerous career paths and opportunities for AI experts across industries and sectors. AI engineer, machine learning engineer, data engineer, robotics engineer, AI researcher, and AI consultant are among the most sought-after and lucrative AI positions. You can choose a career path that aligns with your interests, skills, and goals. Suppose you are interested in artificial intelligence and want to become an AI expert. In that case, you can begin your journey today by enrolling in one of the many online courses or certifications that will teach you the fundamental skills and concepts of AI. You can also look into the various resources and communities available to help you learn more about AI and keep up with the latest trends and developments in the field. We hope this blog post provided you with a comprehensive guide on how to become an AI expert. If you have any questions or comments, please post them below. We'd love to hear from you.

I knew a Chief Software Architect from a major financial organization who was an anomaly: he had never developed software professionally. His undergraduate degree is in Accounting and Finance, and his most hands-on technology role was as a DBA. [His LinkedIn profile lists an early Programmer role, though he insisted he didn’t.] Even so, he was well-respected within his organization for his thought leadership and solutions, but nevertheless, it seemed an unusual career path. Since I last worked with him, he has moved into C-level roles at other organizations, confirming his abilities as a technology leader. Then I thought of others I have worked with who are non-technical but positioned to impact technical direction and realized their lack of understanding impacted (and continues to impact) the quality of the software solutions we, as engineers, are expected to deliver. Chief Non-Technical Officer This CTO has been with her/his company for many years in many roles: Director of Support, Chief Strategy Officer, Chief Cultural Officer, and Chief Technical Officer. S/he does not deny that s/he is not a strong technologist – and at times a badge of honor – yet confidently states decisions and direction that they become a fait accompli: alternatives that challenge her/his understanding are not often well received. At times, her/his inner circle helps to form a more nuanced understanding, but only to a point: overcoming her/his existing preconceived notions is difficult, and blatant opposition results in being sidelined from future discussions. By no means is s/he a total technical novice, but fundamental change requires extensive effort and time. Her/his oft-repeated mantra went something like this: Don’t tell me you’re refactoring; refactoring brings no value to our customers. Harking back to her/his strategy days, where feature-feature-feature is the overwhelming driver, this mantra confirmed her/his denial or lack of understanding of the current state of the product. The growing and maturing customer base made clear that areas of the product needed love and attention, but proposed efforts to address them were not prioritized because – in her/his view of the world – there was no visible benefit to their customers, at least when focused on customers asking for new or extended features. The real technologists of the company understood the potential benefits to both the customer and company: performance and scaling improvements, reduced cloud costs, faster deployments, fewer outages, faster feature delivery, reduced technology stack, and consistent and intuitive user experience. Regardless of potential benefits, nothing called out as refactoring would survive planning. The problems continued to grow, and the problems continued to be ignored. Sigh. Product To be clear, I have no interest in becoming a product owner: the wide-ranging responsibilities require a breadth of knowledge and experience not often found in a single person, while their many stakeholders – both internal and external – have contradictory goals and agendas that need to be balanced. I view it as a political role, finding compromises that please (appease) most, with no one getting everything s/he desired. This role is not for the weak and timid. Once we accept that product owners are unlikely to have the background or experiences necessary to handle all responsibilities, we can then understand why the focus is on those responsibilities understood or deemed important by their leaders. Outside of organizations offering technical solutions, product owners often have a stronger business understanding than technology understanding based on their work experience. Perhaps not surprisingly, the product is defined by business expectations more so than technical requirements: future features and functionality are defined by understanding industry trends, reviewing customer feedback, interpreting — sales and usage analytics, defining the user experience, etc. In essence, the product owner is an overclocked business analyst. Real-World Example A particular product manager focused only on rapidly releasing new features regardless of technical stability. Over time, the issues rose to the point where outages – not processing failures, actual outages – occurred daily and could no longer be ignored. She continued to view the work as unnecessary and not beneficial to the product, resulting in this exchange during quarterly planning: The result is product owners often eschew – whenever possible – technology and technical viability aspects of the product, reducing the impact of technology during product planning. Instead of top-down planning, individual engineers attempt to push technical issues bottom-up, which is very difficult and often unsuccessful. Organizations require a strong engineering discipline and culture to offset the business focus of product owners, but it remains a frustrating challenge. [Of course, production technology issues do arise that demand immediate attention, but the resulting work is stressful, particularly for the engineers who are responsible for implementing the changes required; the result is often a one-off effort rather than fundamentally changing the overall culture.] The Not-Ready-For-Prime-Time Implementation This is less about an individual or role but rather an organizational culture problem: proof-of-concepts assumed to be production-ready. Software proofs-of-concept (POCs) are created to test new business concepts or determine the usefulness or applicability of new technology. POCs should be created with minimal engineering rigor that allows a quick and cheap implementation to be discarded without guilt once the results are evaluated. Most important, it is not intended to be a workable product. Despite these clear expectations, too often, I’ve seen the business get excited at seeing the POC and want it available to customers immediately. The POC might be slightly enhanced or it might be unaltered, but it’s out there for the world (internal or external) to use. And when the problems start appearing – because, by definition, it was not intended for real-world usage – the finger-pointing begins. Agile advocates snigger and say You needed an MVP, silly! but my experiences are much the same as POCs: poor. By definition, an MVP is a complete application without the bells and whistles, but corners are inevitably cut: crawling (of crawl/walk/run paradigm) when current volumes require walk, run, or even fly; minimal/non-existent observability; non-standard user experience; incomplete or incorrect API definitions; security through obscurity; incomplete error handling. When leaders decide to move forward after a successful MVP, the expectation is to expand and enhance the MVP implementation; in fact, it may be better to start over. [I am not disavowing MVPs’ usefulness but rather am clarifying that organizations misuse/abuse the term and are, in fact, creating glorified POCs that are not complete, are not ready for users, and are not production ready. Just saying…] So when you next hear of an access application that is integrated into the enterprise supply chain workflow, don’t say I didn’t warn you. Organizations who make ignorant decisions on the production-readiness of applications shouldn’t know why failures occur later, yet they do, and the engineers are left to pick up the pieces. What Can You Do? It’s not hopeless, really. It isn’t …. not necessarily fun, but there are strategies that you can attempt. Gather Create a personal archive of articles, use cases, scenarios, and data that allows you to tell stories to non-technical people, helping them understand the tradeoffs present in all organizations. Internally, you might be interested in estimated vs. actual effort for feature delivery, production failure rates, or implementation costs mapped to the customer base. Are cloud costs increasing faster than customer growth? Did assumptions made during initial implementation impact the ability to deploy future features, whether positive or negative? Is supposedly important work upended by unknown and unplanned initiatives? Did a potential security breach impact customer confidence? What was the cost of researching a potential security breach? Is data quality affecting your reporting, analytics, and billing? There are many different ways to try and understand what’s happening within your organization. Almost daily, there are new articles online that highlight the issues and problems other organizations experience: Southwest’s 2022 holiday meltdown, a ransomware attack on Vital Care Providers, and Cloudfare’s bad software deployment. Not every organization publishes postmortems, but details often leak through other channels. Perhaps more importantly, your organization doesn’t want to appear in those articles! Educate As most non-technical folks appear unable or unwilling to accept that software is hard, our responsibility – for better or worse – is to show and explain. Unique situations require adjusting the story told, but it is necessary – and never-ending – to have any chance to get the organization to understand: explaining how software is developed and deployed, demonstrating how a data-driven organization requires quality data to make correct decisions, explaining the advantages and disadvantages of leveraging open source solutions; showing examples of how open source licenses impact your organization’s intellectual property. Look for opportunities to inject background and substance when appropriate, as education is open-ended and never-ending. Often, it will appear no one is listening as you repeat yourself, but eventually – hopefully – someone will parrot what you’ve been saying for months. Negotiate Aside from those employed in purely research and development roles, engineering/technology for engineering/technology's sake is not feasible, as technology concerns must be balanced with business concerns: product and its competitors, sales pipeline, customer support and feature requests, security, privacy, compliance, etc. Each decision has its short- and long-term impacts, and it is very unlikely that all involved will be pleased. Sorry, but that’s corporate politics. That does not mean you roll over and play dead, but rather horse trade, often with management and product, to ensure the technical concerns aren’t forgotten: Ensure that changes in business priorities are coupled with impact analysis on in-process development efforts; Accept less-than-optimal initial implementations with the agreement of fast-follow work to address compromises; Define metrics that identify when technology-focused work should be prioritized over feature work. These ideas may or may not apply to your organization or situation, but hopefully, they will give you ideas that may be pursued. Conclusion The problems I’ve discussed are age-old and have seemed to become worse in recent decades, so I’m not sure if any of what I’ve discussed is a surprise. Perhaps this is only the latest incarnation of the problem and post-Agile a new approach will reap benefits. Perhaps leaders will acknowledge that engineers really do understand the problems and are trusted to implement a solution rather than given solutions that fit an arbitrary (and often unrealistic) timeline. It’s a tug-of-war that I don’t yet see resolved. Image Credits “Pointy Hair Boss” © Scott Adams “Productivity: Putting the Kanban Display Together” by orcmid is licensed under CC BY 2.0. “Analog circuit board prototype” by mightyohm is licensed under CC BY-SA 2.0.

By Scott Sosna CORE

Test automation is essential for ensuring the quality of software products. However, test automation can be challenging to maintain, especially as software applications evolve over time. Self-healing test automation is an emerging concept in software testing that uses artificial intelligence and machine learning techniques to enable automated tests to detect and self-correct issues. This makes test automation more reliable and cost-effective and reduces the amount of time and resources required to maintain test scripts. In this article, we will discuss the benefits of self-healing test automation, how it works, and how to implement it in your organization. What Is Self/Auto-Healing Test Automation? Self-healing test automation is a new approach to test automation that uses artificial intelligence (AI) and machine learning (ML) to make test scripts more robust and adaptable. With self-healing test automation, test scripts can automatically detect and repair themselves when changes are made to the application under test, including shifting layouts and broken selectors. This makes it possible to automate tests for complex applications with frequently changing user interfaces without having to constantly maintain and update the test scripts. Why Is Self-Healing Test Automation Necessary? Test automation scripts can easily break when changes are made to the user interface. This is because test automation scripts are typically designed to interact with specific elements on the screen, such as buttons, text fields, and labels. When these elements change, the script may no longer be able to find them or interact with them correctly. This can lead to test failures and false positives, which can be time-consuming and frustrating to resolve. Also, the user interfaces are constantly evolving, with new features and bug fixes being added frequently. This means that test automation scripts need to be updated regularly to adapt to these changes. However, updating test automation scripts can be a manual and time-consuming process, which can be challenging to keep up with the pace of change. Self-healing test automation addresses this fragility of traditional test automation scripts by adapting to changes in the user interface automatically to make test scripts more robust and adaptable. Self-healing test scripts can automatically detect and repair themselves when changes are made to the application under test, which can help to reduce test maintenance costs, improve test quality, and increase test coverage. How Does Self-Healing Mechanism Work? Step 1: The self-healing mechanism gets triggered whenever “NoSuchElement” or a similar error occurs for an element mentioned in the automation script. Step 2: The algorithm analyzes the test script to identify the root cause of the error. Step 3: The algorithm uses AI-powered data analytics to identify the exact object in the test script that has changed. An object can be any interface item like a webpage, navigation button, or text box. Step 4: The algorithm updates the test script with the new identification parameters for the affected object(s). Step 5: The updated test case is re-executed to verify that the remediation has been successful. How Self-Healing Test Automation Adds Value to Your Software Delivery Process Leveraging self-healing capabilities allows test automation to adapt to changes, improving test coverage, reducing maintenance efforts, and enabling faster feedback. Saves Time and Effort Self-healing test automation can save organizations a significant amount of time and effort in software testing. Traditional test automation approaches require manual intervention to fix errors or failures that occur during test execution. This can be a time-consuming and error-prone process, especially when dealing with large and complex test suites. Self-healing test automation eliminates the need for manual intervention, allowing tests to recover automatically from failures or errors. Improves Test Coverage Self-healing test automation can help to improve test coverage by allowing testers to focus on writing new tests rather than maintaining existing tests. This is because self-healing tests can automatically adapt to changes in the software under test, which means that testers do not need to spend time updating their tests every time the software changes. As a result, testers can focus on writing new tests to cover new features and functionality. Self-healing automation can improve test coverage by 5-10% by eliminating unnecessary code, resulting in shorter delivery times and higher returns on investment. Prevents Object Flakiness Object flakiness is a common problem in test automation, especially for GUI testing. Object flakiness occurs when a test fails because it is unable to locate an object on the page. This can happen for a variety of reasons, such as changes to the UI, changes to the underlying code, or network latency. Self-healing test automation can detect and prevent object flakiness by analyzing test results and identifying patterns that indicate flaky tests. By preventing object flakiness, teams can reduce the number of false positives and negatives, improving the overall accuracy and reliability of test results. Faster Feedback Loop Self-healing test automation also enables a faster feedback loop. With traditional test automation approaches, tests are often run manually or through a continuous integration pipeline. However, with self-healing test automation, tests can be run continuously, providing immediate feedback on the quality of the application under test. This enables teams to identify and fix issues faster, improving the overall quality and reliability of the application. Conclusion In Agile methodology, applications are continuously developed and tested in short cycles. This can make it difficult to maintain test cases, as the application is constantly changing. Self-healing test automation can help to overcome this challenge by automatically updating test cases when the application under test changes.

Get ready for a special journey with "Code Wisdom 2024," a guide for developers who want to shine in the world of software development. Enlightenment in development is realising that every bug is a lesson, every error is an opportunity, and every line of code is a chance to create something meaningful. Imagine your year full of wise thoughts, where every month brings a new idea to light up your coding path. These twelve quotes are not just words; they're like a compass showing you the way to becoming an excellent coder, and a leader. Your journey and your code are about to shine with brilliance. Let's see how these ideas can help developers improve their skills, be more creative, and work better together. Take this challenge, set your goals, and be ready for a year of coding like never before! January: #CodeGoals2024 In the realm of coding, set achievable goals for the year. Each line of code is a step toward mastery. Setting achievable goals is like creating a roadmap for your coding journey. If you consider your project as an epic, then divide it into small stories which are achievable. Plan realistic deadlines, and never forget to celebrate success. The achievement you will get is a sense of accomplishment and this will keep your motivation high. Achievements You will improve your time management skills. Focus and productivity will be enhanced. Motivation will be high as the wins will be consistent February: #TeamCollaboration Like the code you write, relationships in a team need constant debugging and collaboration. "Be a team player." You might have got this advice many times. Just like debugging the code, always debug the team dynamics by active listening, regular communication, and prompt conflict resolution. There should be harmony and you should be one of the biggest contributors to that. Achievements Collaboration within the team will strengthen. Fewer conflicts; more celebration of success Job satisfaction March: #TechEvolution As the seasons change, so should your tech stack. Embrace new technologies for continuous growth. Change is the only constant in your world of variables. Embrace change and stay abreast of the tech trends. Some ways to do so can be attending communities, writing blogs, taking sessions, and sharing knowledge. There is always a better way to solve a problem, so go for it. Achievements Enhance skill set Efficient problem solver Stepping towards an industry leader April: #CodePoetry Code is like poetry – elegant and expressive. Strive for code that tells a story and captivates its readers. Be a storyteller through your code. Add clarity to your code and be a hero for future readers for same. Clean code is a great way to learn and implement the same. Believe me, maintenance is easier for code that is clean because of the readability. Achievements Improved collaboration with code readers Faster onboarding of new members of the team Fewer errors and hence, low maintenance May: #CodeRefactoring In the garden of development, refactor your code like pruning branches – for a healthier, more robust project. Keep reviewing your codebase and follow the Boy Scout rule to make the codebase better every time you visit the code. Remove unnecessary complexities and dead code. Make sure the code base is robust as it grows. Achievements Improved code maintainability Less technical debt Scalability also will be affected positively June: #SkillSetExpansion As the days lengthen, so should your skill set. Dive into new frameworks and languages. Be a lifelong learner, and explore new languages and frameworks to broaden your skill set. You might end up learning to solve a problem in many ways and this will make you versatile and help in adapting various project requirements. Achievements Broader range of project opportunities Improved perspective on problem-solving You can market yourself more July: #QualityCode Hot fixes are like ice cream – necessary, but too many can lead to a sticky situation. Aim for quality code. Testing what you have written will save you from small quick fixes. Targeting the root cause of issues, going through thorough testing, and implementing solutions will improve the robustness of the codebase. Achievements Less chance of new bugs Stability of the project A confident codebase August: #CodeExplorer In the vast landscape of development, be an explorer. Seek challenges, solve problems, and grow. You always look for a comfort zone but make sure it does not affect you in your growth. Go for that new blog you wanted to read, the new tech/tools you want to explore, and build that one POC. Each challenge is an opportunity for growth and learning. Achievements Personal and professional development Problem-solving skills Confident to take on diverse challenges September: #PatienceInCoding Debugging is like solving a puzzle. Approach it with patience, and the solution will reveal itself. Analyze a problem systematically, have patience, and be cool and calm. A good debugging tool is a must but at the same time never forget to talk to your colleague about the issue. You might end up with more effective solutions. Achievements Less stress Problem-solving skills Better at RCA (Root Cause Analysis) October: #CodeScalability Like a seasoned architect, design your codebase with scalability in mind. Lay the foundation for future success. When you wear the hat of an architect, you should always think about all the abilities and performance, and anticipate future requirements. A robust foundation gives you long-term success in anything you build Achievements Address NFRs (Non-Functional Requirements) Reduce the chaos Adaptability increases for changing requirements. November: #TeamGratitude In the spirit of gratitude, appreciate your team. A collaborative environment fosters innovation. When it comes to saying thank you, never leave any stones untouched. Be grateful. When you build a collaborative environment and ideas are shared freely, each member feels valued. Creativity and innovation are the result of a positive team culture. Achievements Improved team morale Job satisfaction Inner growth December: #YearReflections As the year concludes, reflect on your journey. Celebrate victories, learn from defeats, and enter the new year wiser. Never forget to acknowledge how far you have come and what progress you've made. Celebrate your successes - I mean every bit of them. Use this learning from the 12 steps that you have taken, be grateful, and start designing your next year. Set a vision for your career and take small steps again. Nothing succeeds like success. Achievements Self-awareness Personal growth Increase resilience Conclusion 2024 is a year filled with opportunities for your growth! set goals, work together, try new things in tech, write code with grace, always improve, learn more skills, care about quality, be an explorer, handle challenges patiently, think about scalability, value your team, and take a moment to look back on your journey. Your commitment to growing will not only make you a senior member but also inspire others in their coding journeys. I wish you joy in your coding endeavors and a great new year with new opportunities ahead. “One of my most productive days was throwing away 1000 lines of code.” - Ken Thompson Enjoy these other related articles: 16 Best Programming Quotes Quotes Every Software Engineer Should Know More Good Programming Quotes Happy coding! It's February already! :)

By Manas Dash CORE

The history of DevOps is definitely worth reading in a few good books about it. On that topic, “The Phoenix Project,” self-characterized as “a novel of IT and DevOps,” is often mentioned as a must-read. Yet for practitioners like myself, a more hands-on one is “The DevOps Handbook” (which shares Kim as an author in addition to Debois, Willis, and Humble) that recounts some of the watershed moments around the evolution of software engineering and provides good references around implementation. This book actually describes how to replicate the transformation explained in the Phoenix Project and provides case studies. In this brief article, I will use my notes on this great book to regurgitate a concise history of DevOps, add my personal experience and opinion, and establish a link to Cloud Development Environments (CDEs), i.e., the practice of providing access to and running, development environments online as a service for developers. In particular, I explain how the use of CDEs concludes the effort of bringing DevOps “fully online.” Explaining the benefits of this shift in development practices, plus a few personal notes, is my main contribution in this brief article. Before clarifying the link between DevOps and CDEs, let’s first dig into the chain of events and technical contributions that led to today’s main methodology for delivering software. The Agile Manifesto The creation of the Agile Manifesto in 2001 sets forth values and principles as a response to more cumbersome software development methodologies like Waterfall and the Rational Unified Process (RUP). One of the manifesto's core principles emphasizes the importance of delivering working software frequently, ranging from a few weeks to a couple of months, with a preference for shorter timescales. The Agile movement's influence expanded in 2008 during the Agile Conference in Toronto, where Andrew Shafer suggested applying Agile principles to IT infrastructure rather than just to the application code. This idea was further propelled by a 2009 presentation at the Velocity Conference, where a paper from Flickr demonstrated the impressive feat of "10 deployments a day" using Dev and Ops collaboration. Inspired by these developments, Patrick Debois organized the first DevOps Days in Belgium, effectively coining the term "DevOps." This marked a significant milestone in the evolution of software development and operational practices, blending Agile's swift adaptability with a more inclusive approach to the entire IT infrastructure. The Three Ways of DevOps and the Principles of Flow All the concepts that I discussed so far are today incarnated into the “Three Ways of DevOps,” i.e., the foundational principles that guide the practices and processes in DevOps. In brief, these principles focus on: Improving the flow of work (First Way), i.e., the elimination of bottlenecks, reduction of batch sizes, and acceleration of workflow from development to production, Amplifying feedback loops (Second Way), i.e., quickly and accurately collect information about any issues or inefficiencies in the system and Fostering a culture of continuous learning and experimentation (Third Way), i.e., encouraging a culture of continuous learning and experimentation. Following the leads from Lean Manufacturing and Agile, it is easy to understand what led to the definition of the above three principles. I delve more deeply into each of these principles in this conference presentation. For the current discussion, though, i.e., how DevOps history leads to Cloud Development Environments, we just need to look at the First Way, the principle of flow, to understand the causative link. Chapter 9 of the DevOps Handbook explains that the technologies of version control and containerization are central to implementing DevOps flows and establishing a reliable and consistent development process. At the center of enabling the flow is the practice of incorporating all production artifacts into version control to serve as a single source of truth. This enables the recreation of the entire production environment in a repeatable and documented fashion. It ensures that production-like code development environments can be automatically generated and entirely self-serviced without requiring manual intervention from Operations. The significance of this approach becomes evident at release time, which is often the first time where an application's behavior is observed in a production-like setting, complete with realistic load and production data sets. To reduce the likelihood of issues, developers are encouraged to operate production-like environments on their workstations, created on-demand and self-serviced through mechanisms such as virtual images or containers, utilizing tools like Vagrant or Docker. Putting these environments under version control allows for the entire pre-production and build processes to be recreated. Note that production-like environments really refer to environments that, in addition to having the same infrastructure and application configuration as the real production environments, also contain additional applications and layers necessary for development. Developers are encouraged to operate production-like environments (Docker icon) on their workstations using mechanisms such as virtual images or containers to reduce the likelihood of execution issues in production. From Developer Workstations to a CDE Platform The notion of self-service is already emphasized in the DevOps Handbook as a key enabler to the principle of flow. Using 2016 technology, this is realized by downloading environments to the developers’ workstations from a registry (such as DockerHub) that provides pre-configured, production-like environments as files (dubbed infrastructure-as-code). Docker is often a tool to implement this function. Starting from this operation, developers create an application in effect as follows: They access and copy files with development environment information to their machines, Add source code to it in the local storage, and Build the application locally using their workstation computing resources. This is illustrated in the left part of the figure below. Once the application works correctly, the source code is sent (“pushed) to a central code repository, and the application is built and deployed online, i.e., using Cloud-based resources and applications such as CI/CD pipelines. The three development steps listed above are, in effect, the only operations in addition to the authoring of source code using an IDE that is “local,” i.e., they use workstations’ physical storage and computing resources. All the rest of the DevOps operations are performed using web-based applications and used as-a-service by developers and operators (even when these applications are self-hosted by the organization.). The basic goal of Cloud Development Environments is to move these development steps online as well. To do that, CDE platforms, in essence, provide the following basic services, illustrated in the right part of the figure below: Manage development environments online as containers or virtual machines such that developers can access them fully built and configured, substituting step (1) above; then Provide a mechanism for authoring source code online, i.e., inside the development environment using an IDE or a terminal, substituting step (2); and finally Provide a way to execute build commands inside the development environment (via the IDE or terminal), substituting step (3). Figure: (left) The classic development data flow requires the use of the local workstation resources. (right) The cloud development data flow replaced local storage and computing while keeping a similar developer experience. On each side, operations are (1) accessing environment information, (2) adding code, and (3) building the application. Note that the replacement of step (2) can be done in several ways. For example, for example, the IDE can be browser-based (aka a Cloud IDE), or a locally installed IDE can implement a way to remotely author the code in the remote environment. It is also possible to use a console text editor via a terminal such as vim. I cannot conclude this discussion without mentioning that, often multiple containerized environments are used for testing on the workstation, in particular in combination with the main containerized development environment. Hence, cloud IDE platforms need to reproduce the capability to run containerized environments inside the Cloud Development Environment (itself a containerized environment). If this recursive process becomes a bit complicated to grasp, don’t worry; we have reached the end of the discussion and can move to the conclusion. What Comes Out of Using Cloud Development Environments in DevOps A good way to conclude this discussion is to summarize the benefits of moving development environments from the developers’ workstations online using CDEs. As a result, the use of CDEs for DevOps leads to the following advantages: Streamlined Workflow: CDEs enhance the workflow by removing data from the developer's workstation and decoupling the hardware from the development process. This ensures the development environment is consistent and not limited by local hardware constraints. Environment Definition: With CDEs, version control becomes more robust as it can uniformize not only the environment definition but all the tools attached to the workflow, leading to a standardized development process and consistency across teams across the organization. Centralized Environments: The self-service aspect is improved by centralizing the production, maintenance, and evolution of environments based on distributed development activities. This allows developers to quickly access and manage their environments without the need for Operations manual work. Asset Utilization: Migrating the consumption of computing resources from local hardware to centralized and shared cloud resources not only lightens the load on local machines but also leads to more efficient use of organizational resources and potential cost savings. Improved Collaboration: Ubiquitous access to development environments, secured by embedded security measures in the access mechanisms, allows organizations to cater to a diverse group of developers, including internal, external, and temporary workers, fostering collaboration across various teams and geographies. Scalability and Flexibility: CDEs offer scalable cloud resources that can be adjusted to project demands, facilitating the management of multiple containerized environments for testing and development, thus supporting the distributed nature of modern software development teams. Enhanced Security and Observability: Centralizing development environments in the Cloud not only improves security (more about secure CDEs) but also provides immediate observability due to their online nature, allowing for real-time monitoring and management of development activities. By integrating these aspects, CDEs become a solution for modern, in particular cloud-native software development, and align with the principles of DevOps to improve flow, but also feedback, and continuous learning. In an upcoming article, I will discuss the contributions of CDEs across all three ways of DevOps. In the meantime, you're welcome to share your feedback with me.

The Azure Solutions Architect Associate certification is a highly regarded credential for Microsoft Azure professionals. Microsoft Azure is a premier cloud platform that offers a diverse set of cloud services. The Azure applications Architect Associate certification validates your knowledge in designing Azure-based applications. We’ll walk you through the certification test in this post, including advice, resources, and a study plan to help you succeed. Understanding the Azure Solutions Architect Associate Exam Before we dive into the preparation process, it’s essential to understand the Azure Solutions Architect Associate exam format and content. The certification exam assesses your knowledge and practical skills in the following areas: Deploy and Configure Infrastructure: Design and implement solutions that deploy and configure infrastructure. Implement Workloads and Security: Implement and manage workloads and security in Azure. Create and Deploy Apps: Create and deploy applications in Azure. Implement Authentication and Secure Data: Implement authentication and secure data solutions. The Azure Solutions Architect Associate exam is designed to evaluate your ability to design solutions that meet specific business objectives in Azure. It consists of multiple-choice questions, case studies, and practical tasks. To pass the exam, you need to score at least 700 out of 1000 points. Preparing for the Azure Solutions Architect Associate Exam Effective preparation is key to passing the Azure Solutions Architect Associate exam. Here’s a step-by-step guide to help you get ready: 1. Understand the Exam Curriculum Review the Azure Solutions Architect Associate curriculum. Understand the topics and skills required for the certification. Take note of the specific domains and their weightage in the exam. 2. Set Up an Azure Environment You’ll need access to an Azure environment to practice. If you don’t have access to an Azure subscription, you can sign up for a free Azure account. Azure provides a set of free services each month, allowing you to practice and gain hands-on experience. 3. Learn Azure Fundamentals Before diving into exam-specific tasks, ensure you have a strong understanding of Azure fundamentals. Microsoft provides free learning resources through Azure Learn, which includes documentation, tutorials, and interactive modules. Start with the official Azure documentation. 4. Take Online Courses Consider enrolling in online courses that specifically prepare you for the Azure Solutions Architect Associate exam. Platforms like Udemy, Pluralsight, Coursera, and edX offer comprehensive courses that cover the required skills. 5. Practice, Practice, Practice The Azure Solutions Architect Associate exam assesses your practical skills. Practice by working on hands-on labs, case studies, and real-world scenarios. Create and deploy resources on Azure to reinforce your knowledge. 6. Use Azure Documentation The official Azure documentation is a valuable resource during the exam. Learn how to navigate and search the Azure documentation to quickly find the information you need. 7. Understand Azure Services Become familiar with Azure services, their use cases, and their capabilities. You should be comfortable with various Azure services, including Virtual Machines, Azure App Service, Azure Kubernetes Service (AKS), Azure Functions, and more. 8. Simulate Exam Conditions Set up a practice environment that mirrors the Azure Solutions Architect Associate exam conditions. Time yourself and practice under time constraints to get used to the real exam’s pressure. 9. Take Practice Exams Many online platforms offer practice exams for the Azure Solutions Architect Associate certification. These practice tests simulate the real exam and help you assess your readiness. Take multiple practice exams to gauge your progress. 10. Learn Test-Taking Strategies During the exam, you’ll need to manage your time effectively. Learn test-taking strategies such as reading questions carefully, eliminating incorrect options, and budgeting your time wisely. 11. Join Study Groups Online study groups, forums, and communities can be beneficial for sharing knowledge and tips. Join Azure Solutions Architect Associate study groups on platforms like Reddit, LinkedIn, or the Microsoft Learn Community to connect with others preparing for the exam. 12. Take Breaks and Rest Studying for the Azure Solutions Architect Associate exam can be intense. Take regular breaks to relax and avoid burnout. A well-rested mind performs better during the exam. Azure Solutions Architect Associate Exam Day Tips On the day of the Azure Solutions Architect Associate exam, here are some tips to keep in mind: Read the Instructions: Carefully read the instructions provided at the beginning of the exam. Understand the requirements and constraints. Start with Easier Questions: Begin with questions you are confident about to build momentum and boost your confidence. Manage Your Time: Keep an eye on the clock and allocate time for each question or task. Don’t get stuck on a single question for too long. Flag Questions: If you’re unsure about a question, flag it for later review and move on to the next. You can come back to it. Check Your Work: Before submitting your answers, review your work. Ensure that you’ve completed all questions and that they meet the requirements. Stay Calm: Don’t panic if you encounter challenging questions. Take a deep breath; if necessary, skip a challenging question and return later. Use Azure Portal: During the exam, you can access the Azure portal and Azure documentation. Learn how to use these resources effectively. Submit Answers Carefully: When you’re confident in your answers, submit them. Ensure that you don’t accidentally submit incomplete or incorrect solutions. Azure Solutions Architect Associate Resources To help you prepare effectively for the Azure Solutions Architect Associate exam, here are some recommended resources: Azure Solutions Architect Associate Curriculum: The official curriculum from Microsoft. Azure Documentation: The official Azure documentation is a valuable reference for exam preparation. Azure Learn: Microsoft’s free learning resources with documentation, tutorials, and interactive modules. Online Courses on Platforms like Udemy, Pluralsight, Coursera, and edX. Practice Exams from reputable sources to assess your readiness. Advanced Study Resources For those looking to delve deeper into Azure and excel in the Solutions Architect role, consider exploring these advanced resources: Microsoft Certified: Azure Solutions Architect Expert The Azure Solutions Architect Associate is a stepping stone to the Azure Solutions Architect Expert certification. This advanced certification focuses on more complex solutions and architectural designs. Earning this certification can open doors to higher-level roles and responsibilities in cloud architecture. Azure Architecture Center The Azure Architecture Center is a comprehensive resource for designing and implementing solutions on Azure. It provides architecture best practices, reference architectures, and design patterns to help you build robust and scalable solutions. Azure Training Paths Microsoft offers a range of Azure training paths to help professionals acquire deeper expertise. These training paths cover specific areas like AI and machine learning, IoT, and more. Azure Community Join the Azure community to connect with other professionals, architects, and experts in the field. Platforms like GitHub, Stack Overflow, and LinkedIn offer opportunities to share knowledge and collaborate on Azure projects. Conclusion Obtaining the Azure Solutions Architect Associate certification is a key step in your cloud architecture career. It displays your proficiency in designing and implementing solutions on Microsoft Azure, a major cloud platform. Passing the exam requires thorough preparation, which includes extensive study, practical experience, and good test-taking tactics. You may successfully prepare for the Azure Solutions Architect Associate test, demonstrate your abilities, and advance your career by following the methods and advice indicated in this book. Microsoft Azure is a prominent cloud provider, and qualified personnel are in high demand. You may position yourself for intriguing career prospects and contribute to the success of organizations that use Azure for their cloud solutions by getting the Azure Solutions Architect Associate certification.

Have you ever found yourself in the position of a test engineer embedded in one of the Agile engineering teams? While you have daily interactions with peers, connecting with them on a profound level for the successful execution of job duties might be challenging. Although there is a shared goal to release features successfully, we often experience isolation, especially while others, like developers, find comfort within the team. In the realm of dispersed Agile teams with time zones adding an extra layer of complexity, the longing for a team to resonate with, connect with, and brainstorm on all test automation challenges is prevalent. In the expansive landscape of test automation, the creation of an automation guild is more than just collaboration; it stands as a testament to the resilience of SDETs working across diverse time zones and Agile teams. Through this guide, I aim to share the benefits and challenges overcome, the enrichment of test engineers or SDETs, and the establishment of a collective force dedicated to advancing excellence in testing. Breaking Silos In a world where time zones separate teams and Agile methodologies dictate the rhythm of development, test engineers face a unique challenge. Even though they are part of an Agile team with a shared goal, i.e., successful release, they must navigate independently without a clear direction or purpose. The guild, however, becomes a bridge across these temporal gaps, offering a platform for asynchronous collaboration. It not only allows them to demo their progress, accomplishments, and new utility that can be leveraged by others but also their challenges and blockers. It will surprise you to see how often those obstacles are common among other guild members. Now that they have each other, all heads come together to brainstorm and find common, effective solutions for any testing problem. Fostering Through Training and Contribution As important as regular guild meet-ups and collective commitment are, continuous learning and training initiatives are equally vital to empower test engineers to contribute effectively. From workshops on emerging testing methodologies to skill-building webinars, the guild evolves into a learning haven where members grow together, ensuring each test engineer is equipped to make a meaningful impact. It enhances members’ efficiency by reducing redundant efforts. Understanding what others are working on and what tools are available for use, such as common utilities and shared definitions, enables them to save time by avoiding duplication of efforts and contribute more effectively. This isn’t just about individual efficiency; it’s a strategic move toward collective empowerment. Grow Your Network and Your Profile Within the guild, networking is not confined to individual teams. It offers the creation of a network that spans across Agile teams, allowing Test Engineers to understand overall solutions from diverse perspectives. This isn’t just about sharing knowledge; it’s about broadening domain knowledge. Turning new members into seasoned members who can then mentor new juniors, ensuring that the guild is not just a community but a mentorship ecosystem that thrives on collective wisdom. If there’s one aspect that has been repeatedly demonstrated in the guild, it would be that challenges are not roadblocks but opportunities for innovation and collaboration. The guild stands as a testament to the fact that, even in the world of test automation, where distances and time zones pose challenges, excellence can be achieved through collective strength. Automation guild is not just about crafting code; it’s about crafting a community that advances excellence in testing, collectively and collaboratively. The future, as envisioned through the chronicles, is one where Test Engineers, regardless of time zones, work seamlessly in a guild that stands as a beacon of innovation, knowledge-sharing, and collective growth.