Operations | Monitoring | ITSM | DevOps | Cloud

Amazon Web Services (AWS) powers everything from scrappy startups to Fortune 500 giants. So, AWS interview questions are relevant for all kinds of roles, from infrastructure design to cost governance. Now, while some interviews test definitions and acronyms, the best ones dig deeper. They explore how well you understand AWS trade-offs—think of performance vs. cost, scalability vs. complexity, or security vs. usability.

Cloud computing empowers organizations to access IT resources on demand, over the Internet, and on a pay-per-use basis. Thus, your company does not need to purchase, install, operate, and upgrade hardware for physical data centers. Instead, you can rent resources as needed from cloud service providers such as Amazon Web Services (AWS). For instance, AWS provides compute, storage, database, networking, machine learning, data lake, analytics, security, and IoT resources/services.

As your applications scale across cloud and SaaS providers, allocating costs and optimizing workloads become increasingly important—and challenging. Without access to cost data in their daily workflows, engineering teams can’t easily understand the cost of their resources and identify where they can reduce their spend. And while FinOps teams have access to cost data, they often review this information in silos.

As a data platform engineer, you’re tasked with running complex workloads—Apache Spark jobs, AI/ML pipelines, batch ETL—across dynamic Kubernetes environments. Performance matters. Time spent tuning matters. And so does cost. But if you’re still relying on manual resource tuning to optimize your workloads, you’re playing a losing game. Sure, you can tweak CPU and memory requests by hand. You can comb through Prometheus metrics, look at job logs, estimate peaks.

We all want growth, but often find ourselves unequipped to deal with it. It’s a bit like going to the gym, lifting weights, and seeing real results, only to realize that you no longer fit into your old clothes. Now you have to decide whether to modify them or buy new clothes. We can use this very simple analogy to understand horizontal vs. vertical scaling.

Google Cloud Platform (GCP) might not always be the loudest name in the cloud room. But it’s gradually become a powerhouse for organizations running data-intensive, AI/ML, and global-scale applications. We also can’t ignore that GCP offers a backbone powered by Google’s own infrastructure (the same one that runs YouTube, Gmail, and Search).

Cloud computing was already booming before 2020. But in the following two years, remote work flourished, and cloud adoption soared. The trend continues to accelerate — even faster now. Some companies have since returned to the office. Others are adopting hybrid models, balancing work-from-home and in-office. Yet, there’s more to the rise of cloud computing than remote working.

As AI adoption increases, the race to real, durable AI value intensifies. Almost every organization that can use AI is using AI — but one of the most troubling trends I’ve observed revolves around talent. Right now, most organizations use AI to increase internal efficiencies — do quick research, write quick emails, start a new project at the 50% mark rather than the 0% mark, etc. But some executives I’ve talked to are taking a more aggressive approach.