Which AWS Services Implement a Data Mesh with Centralized Governance?

Choose a data store. Understand data cataloging systems. Apply authorization mechanisms.

Answer Correct answer: B, E — Use Amazon S3 for data storage and Amazon Athena for data analysis, with AWS Lake Formation providing centralized data governance and access control.

A financial company wants to implement a data mesh. The data mesh must support centralized data governance, data analysis, and data access control. The company has decided to use AWS Glue for data catalogs and extract, transform, and load (ETL) operations. Which combination of AWS services will implement a data mesh? (Choose two.)

Use Amazon Aurora for data storage. Use an Amazon Redshift provisioned cluster for data analysis.
Use Amazon S3 for data storage. Use Amazon Athena for data analysis. Correct Answer
Use AWS Glue DataBrew for centralized data governance and access control.
Use Amazon RDS for data storage. Use Amazon EMR for data analysis.
Use AWS Lake Formation for centralized data governance and access control. Correct Answer

Community Votes

100%

100% of anonymous learners picked answer BE. Votes are pick records left by other test-takers — they are not the verified answer.

Community Insight

The question tests whether you can map data mesh building blocks to storage, analytics, and centralized governance; the trap is choosing Glue DataBrew because it shares the Glue brand but is a data-preparation tool, not a governance layer.

A data mesh on AWS is built with Amazon S3 as the scalable storage layer, Athena for serverless analysis, and Lake Formation for centralized governance and access control. This page confirms why options B and E are correct and why DataBrew, Aurora, RDS, and EMR do not satisfy the stated requirements.

The most common wrong answer is C: AWS Glue DataBrew sounds governance-adjacent because it is in the Glue family, but it is a visual data-preparation service, while Lake Formation provides the centralized access control and governance the scenario requires.

Community Discussion (12 comments)

hsnin 👍 7 Selected: BE

The answer is B and E. The data mesh implementation uses Amazon S3 and Athena for data storage and analysis, and AWS Lake Formation for centralized data governance and access control. When combined with AWS Glue, you can efficiently manage your data.

ninomfr64 👍 1 Selected: BE

S3 (storage) LakeFormation (governance) Athena (analytics) can be used to implement data mesh. In real life you would use DataZone or nowadays SageMaker Unified Studio

Shubham1989 👍 1 Selected: BE

S3 is best storage for data lake, and AWS lake formation is best for management.

nanaw770 👍 2 Selected: BE

BE are correct answer.

Josa2 👍 3 Selected: BE

Sometimes I think examtopics uses us to calibrate the right answers hehehe, by the goal statement and the services outlines and objectives there are no way the answer be different then B,E

pypelyncar 👍 1 Selected: BE

A: Cost-effective storage: Amazon S3 is a highly scalable and cost-effective object storage service perfect for storing large datasets commonly found in financial institutions. Centralized data lake: S3 acts as the central data lake where all data from different domains can reside in its raw or processed form. Easy data access: Athena provides a serverless interactive query service that allows data analysts to directly query data stored in S3 using standard SQL. This simplifies data exploration and analysis without managing servers. B: Data governance: Lake Formation helps establish data ownership, access control, and lineage for data products within the data mesh. It ensures data quality, security, and compliance with regulations. Fine-grained access control: Lake Formation allows you to define granular access policies for each data domain, ensuring only authorized users can access specific data sets. This aligns with the need for centralized control in a data mesh.

Fredrik1 👍 1

Must be B and E

minhtien1707 👍 1 Selected: BE

i thing so

alexua 👍 2

B and E . C - is not correct "AWS Glue DataBrew is a visual data preparation tool that makes it easier for data analysts and data scientists to clean and normalize data to prepare it for analytics and machine learning (ML)"

Alcee 👍 1

B and E

TonyStark0122 👍 2

BE Given the requirements for implementing a data mesh architecture with centralized data governance, data analysis, and data access control, the two better choices from the options provided would be: B. Use Amazon S3 for data storage. Use Amazon Athena for data analysis. E. Use AWS Lake Formation for centralized data governance and access control.

milofficial 👍 4 Selected: BE

Textbook question, the keyword data mesh means S3, the keyword data governance means LakeFormation

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Expert Analysis

Why the Answer Is Correct

A data mesh requires domain-oriented data storage, a query and analytics layer, and centralized governance and access control. Amazon S3 is the durable, scalable storage layer for a data lake, and Athena provides serverless query over S3, which fits the analysis requirement without provisioning a cluster. AWS Glue Data Catalog can catalog S3 data, while Lake Formation provides centralized permissions, fine-grained access control, and governance across the lake. Together B and E complete the architecture alongside Glue ETL.

Why the Other Options Are Wrong

A uses Aurora and a provisioned Redshift cluster; Aurora is relational and OLTP-oriented, and a provisioned Redshift cluster is not the serverless analytics layer described, while neither option addresses centralized governance. C wrongly casts Glue DataBrew as governance and access control; as alexua noted, "AWS Glue DataBrew is a visual data preparation tool" for cleaning and normalizing data. D uses RDS and EMR; RDS is relational and EMR is a big-data processing framework, but the pair still leaves centralized governance and access control unaddressed. B and E are the only pair that explicitly covers storage, analysis, and centralized governance in the scenario.

Community Comment Notes

Learners overwhelmingly landed on B and E, with milofficial summarizing the exam heuristic as "the keyword data mesh means S3, the keyword data governance means LakeFormation". alexua reinforced the elimination of DataBrew by describing it as visual data preparation rather than governance. ninomfr64 added a real-world caveat that DataZone or newer unified tooling may be used in practice, but for this exam scenario S3, Athena, and Lake Formation align with the stated services. The consensus is useful context, but the answer rests on the requirement mapping, not the vote count.

Official Reference

Exam Strategy

When a data mesh question pairs Glue with governance, separate the three asks: storage, analytics, and centralized permissions. S3 plus Athena plus Lake Formation is the canonical AWS data-lake trio; reject Glue DataBrew unless the stem explicitly mentions visual data preparation or cleaning.

Frequently Asked Questions

Why is AWS Glue DataBrew wrong for centralized governance?

DataBrew is a visual data-preparation service for cleaning and normalizing data; it does not provide the centralized access-control and governance layer that Lake Formation does.

Why are Aurora and Amazon RDS not the storage layer in this data mesh?

They are relational databases suited to transactional workloads, while a data mesh data lake needs scalable object storage such as Amazon S3 for diverse domain datasets.

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