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DEA-C01 Data Store Management Practice Question

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?

⚠ Common exam trap

A common mix-up: candidates 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.

Answer choices

Why each option matters

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.

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 only overrides bucket and account policies to prevent public exposure; it cannot evaluate tag-based conditions, so per-tag access for analytics services is unenforceable. It is tempting because it hardens a bucket against accidental public reads, which is the right control when the requirement is simply blocking anonymous access.

  • ✓

    AWS Lake Formation with tag-based access control

    Why this is correct

    Lake Formation tag-based access control enforces column, row and table permissions using LF-Tags across analytics services, satisfying the fine-grained, tag-driven requirement. Plain S3 bucket policies or IAM alone cannot express tag-based governance at that granularity.

  • ✗

    S3 Access Points with bucket policies

    Why it's wrong here

    Access Points with bucket policies scope permissions to a named endpoint and network origin, but the stem requires tag-conditioned authorisation, which needs IAM or bucket policy conditions keyed on resource tags. Access Points are correct when many applications need distinct, isolated entry points to one shared bucket.

  • ✗

    S3 Object Lambda with IAM policies

    Why it's wrong here

    S3 Object Lambda transforms objects during retrieval, and IAM policies alone cannot grant or deny access per object tag; tag-based authorisation requires attribute-based policy conditions on the bucket or access point. Object Lambda is correct when responses must be redacted or reformatted per caller, not for tag-driven authorisation.

Quick reference

AWS S3 Storage Class Comparison

Storage ClassMin DurationRetrievalUse Case
S3 StandardNoneImmediateFrequently accessed data
S3 Standard-IA30 daysImmediateInfrequent access, rapid retrieval
S3 One Zone-IA30 daysImmediateNon-critical infrequent data
S3 Intelligent-TieringNoneImmediate–hoursUnknown or changing access patterns
S3 Glacier Instant90 daysMillisecondsArchive with instant retrieval
S3 Glacier Flexible90 daysMinutes–hoursArchive, flexible retrieval
S3 Glacier Deep Archive180 daysHoursLong-term compliance archive

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Written by Johnson Ajibi, MSc IT Security

Senior Network & Security Engineer · founder of Courseiva

This DEA-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 DEA-C01 exam.