AWS Glue Catalog Views with Lake Formation and CloudTrail

DEA-C01 By islever Editorial Last updated

Transform and process data. Ensure data encryption and masking. Automate data processing by using AWS services.

Short answer

B Use AWS Glue Data Catalog views to perform analysis, AWS CloudTrail logs to audit data access, AWS Lake Formation to manage access permissions, and security definer views to mask PII.

Question

A company needs to implement a data mesh architecture in which domains for trading, risk, and compliance teams each have own their data. The teams need to share specific views with one another. The teams have over 1,000 tables across 50 databases in AWS Glue Data Catalog. All three teams use Amazon Athena to perform on-demand analysis. The teams use Amazon Redshift to generate complex reports. The compliance team must audit all data access. Access to personally identifiable information (PII) data must be restricted.
The company requires a scalable solution to meet the team requirements. The solution must provide the ability to perform analysis across team domains.
Which solution will meet these requirements?
  1. Create views in Athena for on-demand analysis. Use the Athena views in Amazon Redshift to perform cross-domain analytics. Use AWS CloudTrail to audit data access. Use AWS Lake Formation to establish fine-grained access control.
  2. Use AWS Glue Data Catalog views to perform analysis. Use AWS CloudTrail logs to audit data access. Use AWS Lake Formation to manage access permissions. Use security definer views to mask PII. Correct Answer
  3. Use AWS Lake Formation to set up cross-domain access to tables. Set up fine-grained access controls.
  4. Create materialized views and enable Amazon Redshift datashares for each domain. Configure cross-domain access policies.

Key Takeaways

The main trap is underestimating the role of AWS Glue Data Catalog's multi-dialect views in enabling seamless analysis across both Amazon Athena and Amazon Redshift while maintaining governance through Lake Formation.

This question tests implementing a data mesh architecture using AWS Glue Data Catalog multi-dialect views for cross-engine analytics, Lake Formation for PII restriction, and CloudTrail for auditing.

Common mistake: Choosing Option A is common because it mentions Athena views, but it fails to leverage the unified catalog views that simplify cross-engine access and lacks the explicit PII masking strategy provided by security definer views in Lake Formation.

Expert Analysis

The scenario describes a data mesh architecture where domain teams (trading, risk, compliance) own their data but need to share specific views. The key constraints are: using AWS Glue Data Catalog with over 1,000 tables across 50 databases, using Amazon Athena for on-demand analysis, using Amazon Redshift for complex reports, requiring audit of all data access via CloudTrail, and restricting PII access.

Option A is incorrect because it suggests creating views in Athena and using them in Redshift. While Athena can query S3 data, cross-engine view usage is not the primary scalable pattern for sharing curated datasets between domains in this context. Furthermore, relying solely on Athena views doesn't address the fine-grained access control needed for PII as effectively as Lake Formation's native capabilities when integrated with the catalog.

Option B is the correct approach. AWS Glue Data Catalog supports multi-dialect views, which allow you to create a single logical view that can be queried by both Athena and Redshift. This directly addresses the requirement to perform analysis across team domains using both engines. Using AWS Lake Formation to manage access permissions allows for fine-grained access control (FGAC), which is essential for restricting PII. Additionally, AWS CloudTrail logs API calls, providing the necessary audit trail for data access. The comment supporting Option B highlights the combination of Glue Catalog Multi-Dialect Views, Lake Formation, and CloudTrail, which perfectly aligns with the technical requirements.

Option C is incomplete. While Lake Setting up cross-domain access and FGAC is part of the solution, it lacks the mechanism for sharing views between Athena and Redshift and does not explicitly mention the audit capability via CloudTrail, making it insufficient compared to B.

Option D focuses on Redshift Data Shares, which is good for sharing Redshift data, but it doesn't address the Athena use case or the PII masking strategy as comprehensively as the Glue Catalog + Lake Formation approach described in B.

Official Reference

These links open the official vendor documentation in a new tab.

Exam Strategy

When questions involve multiple query engines (Athena/Redshift) accessing the same cataloged data, look for solutions leveraging 'multi-dialect' or unified catalog features. Always pair data access mechanisms with the appropriate audit (CloudTrail) and security (Lake Formation) services mentioned in the requirements.

Frequently Asked Questions

What are AWS Glue Data Catalog multi-dialect views?

They are virtual tables that allow different SQL dialects (like HiveQL for Athena and ANSI SQL for Redshift) to query the same underlying data without duplicating metadata.

How does Lake Formation handle PII masking?

Lake Formation uses security definer views to mask sensitive columns like PII, ensuring users only see authorized data based on their permissions.

More DEA-C01 FAQ →

Community Votes

B
50%
A
50%

50% of anonymous learners picked answer B. Votes are pick records left by other test-takers — they are not the verified answer.

Discussion 2 contributions

Compiled from public community threads and reader submissions, lightly edited for clarity. Handles and vote tallies are shown as recorded, and comments reflect the views of individual contributors.

From public community threads 2

  1. AWS82siast 👍 1 Answered B
    Glue Data Catalog Multi-Dialect Views + Lake Formation + CloudTrail
  2. learner2023 👍 1 Answered A
    A is correct, c is incomplete.

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