I earned the Azure Fundamentals certification about two and a half years ago, but I haven't used Azure much since then. I've been unemployed for more than a year and recently reached a second-round interview with a large consulting firm, so I really want to make the most of this opportunity.
The role combines a technical industry specialty with data and AI consulting. It involves building scalable data and AI solutions, predictive analytics, NLP, generative AI, automation, governance, compliance, data pipelines, data lakes, data models, and integrations. The team uses cloud platforms such as Azure, AWS, and Google Cloud, along with tools including Databricks, Snowflake, Python, and SQL.
I have more than 15 years of experience in the relevant technical specialty, but my Azure knowledge is mostly at the fundamentals level and is rusty. The interview is in less than 48 hours. What should I prioritize besides panicking?
4 Answers
Use some of the remaining time to research the company and prepare thoughtful questions. Ask how much of the role is architecture, implementation, governance, or client strategy; which cloud services and data platforms the team actually uses; what a successful first six months would look like; and how they support people who are deepening their cloud skills.
For the interview itself, take a moment before answering, think out loud when appropriate, and say how you would verify something you don’t know. Calm, structured reasoning will make a better impression than trying to bluff your way through a memorized product list.
Don’t try to become an Azure expert in two days. Refresh the fundamentals and focus on being able to explain the main concepts clearly: subscriptions and resource groups, identity and access, networking, storage, compute, databases, monitoring, security, governance, and the difference between IaaS, PaaS, and SaaS.
A fast AZ-900 review course or exam-cram playlist would be useful. If you have access to Azure, deploy one small resource so the concepts feel practical rather than purely theoretical. It may also be worth skimming the fundamentals material for Azure AI and reviewing how services such as Azure Machine Learning, AI services, data lakes, and Databricks fit together.
Be honest about the certification and your experience. Don’t imply that you’ve been operating Azure environments if you haven’t. Instead, frame it as a foundation you earned previously and a skill you’re actively refreshing. Then emphasize the areas where you’re genuinely strong, especially your technical domain knowledge, consulting experience, leadership, and ability to translate business problems into data and AI solutions.
Interviewers usually care more about how you reason through an unfamiliar problem than whether you can recite every service name.
That’s fair. My Azure experience is limited, but I have more than 15 years in the technical specialty the role supports, so I’ll make sure I clearly connect that experience to the data and AI use cases in the description.
The job description sounds broader than an Azure Fundamentals role. They may care more about how you design data and AI solutions than whether you remember every Azure product detail. Prepare two or three strong examples from your specialty: the business problem, the data involved, the architecture or modeling approach, how you handled governance and risk, and the measurable result.
Be ready to discuss how you would approach a data pipeline, a lakehouse, predictive modeling, generative AI, NLP, automation, and responsible AI. You can explain that the same general architecture principles apply across Azure, AWS, and Google Cloud, while being transparent about where your hands-on Azure experience is limited.
You can also work through selected sections of Learn Azure in a Month of Lunches, then use Microsoft Learn modules for the Azure fundamentals and AI fundamentals topics. Don’t try to finish every lesson—use them to fill obvious gaps.