Browse AWS Certification Guides

AWS DEA-C01 Sample Questions with Explanations

April 25, 2026

AWS DEA-C01 sample questions with explanations, traps, topic labels, and IT Mastery route links.

On this page

These original sample questions are designed to help you check how the exam topics appear in decision-style prompts. They are not taken from the live exam.

Use these sample questions as a guided self-assessment for AWS Certified Data Engineer - Associate (DEA-C01) topics such as batch and streaming ingestion, durable landing zones, Glue metadata, Athena and Redshift query paths, orchestration, data quality, Lake Formation governance, encryption, monitoring, and replayable pipeline design. The prompts emphasize production data-platform judgment rather than isolated service definitions.

Where these questions fit in the DEA-C01 guide

The sample set below is part of the AWS DEA-C01 guide path:

DEA-C01 data engineering sample questions

Work through each prompt before opening the explanation. DEA-C01 questions usually reward answers that make pipelines replayable, governed, observable, and cost-aware.

Question 1

Topic: Replayable ingestion for late-arriving data

A retail company receives hourly order files from several partners. Some files arrive late or are corrected after delivery. The analytics team needs to reprocess a date range without losing the original input files or duplicating rows in curated tables. Which design is strongest?

  • A. Load each file directly into the final analytics table and delete the source file after the load succeeds.
  • B. Land all files in an immutable S3 raw zone, track processed files and watermarks, make transformations idempotent, and write curated outputs with partition-aware overwrite or merge logic.
  • C. Append every delivered file to a single CSV file in S3 and run Athena queries directly against that file.
  • D. Use Amazon QuickSight ingestion as the primary pipeline and manually refresh dashboards when corrections arrive.

Best answer: B

Explanation: DEA-C01 data pipeline questions often reward durable raw landing plus controlled replay. Keeping immutable inputs, tracking what was processed, and using idempotent writes lets the team backfill or correct date ranges without guessing which version of the data was used.

Why the other choices are weaker:

  • A removes the replay source and makes late corrections risky.
  • C creates performance, concurrency, and correctness problems because one growing CSV is not a strong analytics layout.
  • D treats a BI ingestion layer as the pipeline of record and does not solve durable processing or replay.

What this tests: Raw zones, idempotency, backfills, late-arriving data, and curated-table reliability.

Related topics: Ingestion; S3 data lake; Backfill; Idempotency

Question 2

Topic: Choosing the query layer for S3 data

A team stores compressed Parquet files in S3, partitioned by event date and region. Analysts need occasional ad hoc SQL over the lake data. They do not need a provisioned warehouse cluster or high-concurrency dashboard serving. Which first choice is most appropriate?

  • A. Amazon Redshift provisioned cluster with all data copied from S3 before every query.
  • B. Amazon DynamoDB because it provides low-latency key-value reads.
  • C. Amazon Athena with a Glue Data Catalog table and partition pruning.
  • D. AWS Lambda that scans every S3 object and builds SQL results in memory.

Best answer: C

Explanation: Athena is a strong fit for ad hoc SQL directly over S3, especially when data is columnar and partitioned. Glue Data Catalog metadata plus partition filters can reduce scanned data and cost.

Why the other choices are weaker:

  • A may fit warehouse workloads, but the prompt says occasional ad hoc SQL and no provisioned cluster requirement.
  • B is for key-value or document access patterns, not SQL analytics over lake files.
  • D is operationally fragile and ignores managed query engines and partition pruning.

What this tests: Athena versus Redshift, Glue Catalog metadata, Parquet, partition pruning, and cost-aware query design.

Related topics: Athena; Glue Data Catalog; Parquet; Partitioning

Question 3

Topic: Governed cross-service lake access

A company has sensitive customer data in an S3 data lake. Different teams query the same tables through Athena, Redshift Spectrum, and Spark jobs. The data platform team wants centralized table permissions, column restrictions, and audit-friendly access control across those engines. What should it use?

  • A. Only S3 bucket policies, because every analytics engine reads from S3 eventually.
  • B. Public S3 object ACLs combined with application-side filtering.
  • C. Amazon CloudFront signed URLs for all analytics reads.
  • D. AWS Lake Formation with Glue Data Catalog permissions and least-privilege IAM around the supporting resources.

Best answer: D

Explanation: Lake Formation is the governance lane for S3-based data lakes when access must be managed at the catalog/table/column level across supported analytics services. IAM still matters, but IAM alone is not the full cross-engine data-governance answer.

Why the other choices are weaker:

  • A is too coarse for centralized table and column permissions across analytics engines.
  • B is insecure and pushes enforcement into application code.
  • C is a content-delivery access pattern, not a data lake governance model.

What this tests: Lake Formation, Glue Data Catalog permissions, least privilege, and governance across query engines.

Related topics: Lake Formation; Governance; Glue Catalog; Least privilege

Question 4

Topic: Pipeline failure after schema drift

A Glue ETL job started failing after a partner added new fields and changed a nullable field to sometimes contain malformed values. The business wants bad records isolated, the valid records loaded, and the team alerted with enough evidence to fix the source contract. What is the strongest approach?

  • A. Add data quality checks, route invalid records to a quarantine location with error metadata, emit metrics and logs, and alert the owning team.
  • B. Disable validation so every row loads and analysts can decide later.
  • C. Delete the new fields from the raw files before the Glue job reads them.
  • D. Move the entire pipeline to a relational database so schema drift cannot happen.

Best answer: A

Explanation: The requirement combines quality enforcement, partial progress for valid data, evidence, and alerting. Quarantine plus observability is stronger than either failing silently or accepting corrupted rows.

Why the other choices are weaker:

  • B sacrifices data quality and makes downstream errors harder to diagnose.
  • C mutates raw evidence and can hide the real source-contract issue.
  • D changes the platform without addressing validation, quarantine, or source accountability.

What this tests: Data quality, schema drift response, quarantine design, monitoring, and source-contract troubleshooting.

Related topics: Data quality; Glue ETL; Quarantine; Monitoring

Independent study note

Tech Exam Lexicon and IT Mastery are independent study tools. They are not affiliated with, endorsed by, or sponsored by Amazon Web Services, AWS, or any certification body.

Revised on Sunday, May 10, 2026
Cheat Sheet
FAQ