The correct answer is AWS Lake Formation and AWS Glue Data Catalog. Lake Formation is the right choice because it provides centralized fine-grained access control at the column and row level, allowing you to define permissions on specific columns or rows of data in your data lake without duplicating or moving the underlying data. The Glue Data Catalog serves as the central metadata repository, making data discoverable and queryable by services like Amazon Athena and Amazon EMR, while Lake Formation enforces the security policies on top of that catalog. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding of how to secure sensitive ML data without sacrificing accessibility—a common trap is to select Amazon S3 bucket policies alone, which cannot enforce row-level filtering. Remember the memory tip: “Lake Formation locks the rows and columns, Glue catalogs the clues.”
MLS-C01 Data Engineering Practice Question
This MLS-C01 practice question tests your understanding of data engineering. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A data engineering team is designing a data lake on AWS for machine learning workloads. The data includes structured, semi-structured, and unstructured data. The team needs to ensure that the data is cataloged, easily discoverable, and can be queried by Amazon Athena and Amazon EMR. The team also wants to enforce fine-grained access control at the column and row level for sensitive data. Which combination of AWS services should the team use? (Select TWO.)
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
AWS Lake Formation
AWS Lake Formation is correct because it provides a centralized service to build, secure, and manage data lakes on AWS. It enables fine-grained access control at the column and row level for sensitive data, which directly meets the requirement for enforcing such controls. Additionally, Lake Formation integrates with Amazon Athena and Amazon EMR for querying and processing the cataloged data.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✓
AWS Lake Formation
Why this is correct
Lake Formation provides fine-grained access control and integrates with Glue Catalog.
Related concept
Read the scenario before looking for a memorised answer.
✗
AWS Identity and Access Management (IAM)
Why it's wrong here
IAM provides identity and access management but not column/row-level granularity.
✓
AWS Glue Data Catalog
Why this is correct
Glue Data Catalog is the central metadata repository for Athena, EMR, and other services.
Related concept
Read the scenario before looking for a memorised answer.
✗
Amazon RDS for PostgreSQL
Why it's wrong here
RDS is a relational database, not a data catalog for the data lake.
✗
Amazon DynamoDB
Why it's wrong here
DynamoDB is a key-value store and not used for data lake cataloging.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often assume IAM alone can handle fine-grained data access control, but IAM lacks the column- and row-level filtering capabilities that Lake Formation provides through its integration with the Glue Data Catalog and query engines.
Detailed technical explanation
How to think about this question
AWS Lake Formation uses the AWS Glue Data Catalog as its underlying metadata repository, meaning that when you register data sources in Lake Formation, the metadata is stored in the Glue Data Catalog and can be queried by Athena and EMR. Lake Formation implements fine-grained access control by applying row- and column-level filters at query time through integration with the catalog and the query engines, using IAM policies and LF-Tags or named resource permissions. A real-world scenario is a healthcare data lake where patient records must restrict access to specific columns (e.g., SSN) and rows (e.g., only records for a given clinic) without duplicating data.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A company's IT admin needs to give a contractor read-only access to production logs without sharing account credentials. Using role-based access control (RBAC) and temporary scoped permissions — not a permanent shared password — is the correct pattern. Questions like this test whether you can apply least-privilege access across cloud identity services.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Data Engineering — This question tests Data Engineering — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: AWS Lake Formation — AWS Lake Formation is correct because it provides a centralized service to build, secure, and manage data lakes on AWS. It enables fine-grained access control at the column and row level for sensitive data, which directly meets the requirement for enforcing such controls. Additionally, Lake Formation integrates with Amazon Athena and Amazon EMR for querying and processing the cataloged data.
What should I do if I get this MLS-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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This MLS-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLS-C01 exam.
Question Discussion
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