Building Data Lakes on AWS

What This Actually Tests

Before diving into practice questions, it’s worth being clear about what “Building Data Lakes on AWS” is. It’s a one-day AWS instructor-led classroom course, not a proctored certification exam like AWS Certified Solutions Architect or AWS Certified Data Engineer. It ends with a short post-course knowledge check used to reinforce what you’ve learned — AWS doesn’t publish a duration, question count, or passing score for it, because it isn’t scored pass/fail the way a certification exam is.

That means the practice questions on this page serve a slightly different purpose than a traditional cert-prep practice test: they’re built to mirror the course’s own topics and labs — AWS Lake Formation, AWS Glue, Amazon Athena, and data lake security models — so you walk into the course (or review it afterward) with the concepts already solid, rather than hearing them for the first time in a one-day session.

If you’re actually studying toward a scored AWS certification that happens to cover similar ground — most commonly AWS Certified Data Engineer – Associate (DEA-C01) — the underlying service knowledge transfers directly, but that’s a separate, formally proctored exam with its own blueprint, and this page isn’t a substitute for that exam’s official guide.

What the Course Covers

The Concepts Candidates Consistently Underestimate

The difference between a data lake and a data warehouse isn’t just storage format. It’s tempting to reduce this to “structured vs. unstructured data,” but the course spends time on when a data lake architecture is the right call vs. when a warehouse pattern serves the use case better — schema-on-read vs. schema-on-write, cost model, and query pattern all factor in. Memorizing a one-line definition doesn’t hold up against a scenario question.

Lake Formation permissions are not the same model as IAM policies alone. Lake Formation layers fine-grained, tag-based, and row/column-level access control on top of standard IAM, specifically for data lake resources. People who’ve only worked with S3 bucket policies and IAM roles tend to assume access control here works the same way — it doesn’t, and that gap shows up fast once you’re actually configuring permissions in the lab.

Glue crawlers populate the Data Catalog, but they don’t transform data. A common mix-up is treating Glue crawlers as doing the same job as a Glue ETL job. Crawlers infer schema and populate catalog metadata; actual data transformation is a separate Glue component. Conflating the two leads to confusion in both the course labs and any later certification study.

How to Use Practice Questions Effectively Here

Because this course is hands-on-lab-heavy rather than pure lecture, the most useful practice questions are the ones that ask you to reason through a small architecture decision — “you need row-level security on a shared dataset, which service feature handles that” — rather than pure vocabulary recall. If you’re coming in with zero AWS data services background, spend time on questions that force you to distinguish between S3, Glue, Lake Formation, and Athena’s respective jobs in the pipeline, since mixing those up is the single most common point of confusion walking into the labs.

Common Gaps That Show Up in the Labs

• Confusing AWS Glue (cataloging and ETL) with AWS Lake Formation (governance and permissions layer) — they work together but do different jobs.

• Underestimating how much of the course is lab time rather than lecture; showing up without basic S3 and IAM familiarity makes the labs take longer than the allotted time.

• Assuming Athena requires data to be loaded into a database first — it queries data directly in S3 using the Glue Data Catalog’s schema, which is a different mental model than a traditional SQL database.

Getting the Most Out of a One-Day Format

Because this is a single intermediate-level day rather than a multi-week program, the course assumes you’ve already completed AWS Technical Essentials and have some hands-on exposure to building data analytics pipelines. If you’re starting from zero AWS experience, spend a few hours beforehand getting comfortable navigating S3 and basic IAM concepts — the course moves quickly through setup so it can spend its limited time on Lake Formation and Glue specifics. If you’ve already got a year or so of pipeline experience, the day is  about seeing how AWS’s specific services fit together rather than learning new concepts from scratch.