The correct choice is AWS Lake Formation with tag-based access control because it enables fine-grained access control for S3 data lake with Lake Formation by using LF-tags to define attribute-based access control (ABAC) policies at the column, row, and cell level. This approach eliminates the need to manage individual user-to-resource mappings, allowing you to scale permissions across multiple analytics services by simply tagging Data Catalog resources and assigning permissions based on those tags. On the AWS Certified Data Engineer Associate DEA-C01 exam, this scenario tests your understanding of how Lake Formation’s tag-based access control differs from IAM policies or S3 bucket policies, which lack the granularity for tag-driven, column-level restrictions. A common trap is choosing AWS Glue with IAM, which cannot enforce row or cell-level security. Remember the mnemonic “LF-TBAC” — Lake Formation’s Tags Beat All Controls for granular data lake security.
DEA-C01 Data Store Management Practice Question
This DEA-C01 practice question tests your understanding of data store management. 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 engineer is designing a data lake on Amazon S3. The data is frequently accessed by multiple analytics services, and the company needs to enforce fine-grained access control based on data tags. Which combination of AWS services should be used?
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 with tag-based access control
AWS Lake Formation with tag-based access control (TBAC) is the correct choice because it provides fine-grained, attribute-based access control (ABAC) at the column, row, and cell level across a data lake on S3. By assigning LF-tags to Data Catalog resources and defining permissions based on those tags, you can enforce granular access policies that scale without managing individual user-to-resource mappings. This directly meets the requirement for tag-driven, fine-grained access for multiple analytics services.
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.
✗
S3 Block Public Access settings
Why it's wrong here
Block Public Access is a security setting, not fine-grained control.
✓
AWS Lake Formation with tag-based access control
Why this is correct
Lake Formation provides fine-grained access control using tags.
Related concept
Read the scenario before looking for a memorised answer.
✗
S3 Access Points with bucket policies
Why it's wrong here
Access Points restrict network access, not tag-based access.
✗
S3 Object Lambda with IAM policies
Why it's wrong here
S3 Object Lambda is for data transformation, not access control based on tags.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse S3 Access Points (which provide network-level or prefix-level restrictions) with the fine-grained, tag-driven access control that Lake Formation TBAC uniquely offers, leading them to pick Option C despite its inability to enforce column- or row-level security based on tags.
Detailed technical explanation
How to think about this question
Under the hood, Lake Formation TBAC works by associating LF-tags (key-value pairs) with Data Catalog databases, tables, and columns, then granting IAM principals permissions like SELECT or DESCRIBE based on tag expressions. When a query engine like Athena or Redshift Spectrum accesses the data, Lake Formation intercepts the request and dynamically rewrites the query to filter rows or columns based on the principal's tag-based permissions, ensuring only authorized data is returned. A subtle behavior is that LF-tags are inherited hierarchically (from database to table to column), but explicit grants at a lower level can override inheritance, which requires careful planning to avoid unintended access.
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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
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 Store Management — This question tests Data Store Management — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: AWS Lake Formation with tag-based access control — AWS Lake Formation with tag-based access control (TBAC) is the correct choice because it provides fine-grained, attribute-based access control (ABAC) at the column, row, and cell level across a data lake on S3. By assigning LF-tags to Data Catalog resources and defining permissions based on those tags, you can enforce granular access policies that scale without managing individual user-to-resource mappings. This directly meets the requirement for tag-driven, fine-grained access for multiple analytics services.
What should I do if I get this DEA-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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Question Discussion
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