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For learners studying for AWS Certified Data Engineer - Associate (DEA-C01)
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Practice pack content
Domain 1 - Data Ingestion and Transformation
Domain 2 – Data Store Management
Domain 3 – Data Operations and Support
Domain 4 – Data Security and Governance
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Try 3 Data Engineer - Associate (DEA-C01) practice questions
Choose an answer, explore the explanation, and see why the other options are less suitable. Three short scenarios. No sign-in required.
1. Optimizing raw CSV data for analytics
A company stores several terabytes of daily CSV files in Amazon S3 and queries them frequently with Amazon Athena. Query costs and scan times are increasing, and analysts usually select only a subset of columns. Which transformation best improves analytics efficiency?
Answer and full explanation
Correct answer: B — Convert the data to Apache Parquet with AWS Glue ETL.
Why B is correct
Apache Parquet is a columnar format that lets analytical engines read only the columns required by a query instead of scanning every field in each row. AWS documents Parquet and ORC as optimized columnar formats for Athena because they support efficient compression, parallel reads, and data pruning. DEA-C01 Task 1.2 explicitly includes implementing transformation services such as AWS Glue and converting data between formats such as CSV and Parquet. AWS Glue ETL can perform that conversion as part of the data pipeline. Organizing the resulting files for common query patterns can further reduce the amount of data Athena scans and improve both cost and performance.
Why the other options are less suitable
- A — Larger CSV files: Combining many small files can reduce some file-management overhead, but CSV remains a row-oriented text format. Athena still cannot obtain the same column-pruning and compression benefits provided by Parquet.
- C — Move the data to EBS: Amazon EBS is block storage for compute instances and is not the appropriate replacement for an S3-based analytics data lake. This change would also add infrastructure management without solving the columnar-query requirement.
- D — Base64-encode the data: Encoding changes the representation of the files but does not make them more efficient for analytical scans. It can increase storage size and would require decoding before useful analysis.
Key takeaway: Columnar formats such as Parquet reduce unnecessary analytical I/O and are a strong fit for large S3 datasets queried by selected columns.
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2. Enforcing reusable data-quality checks
A data engineering team maintains an AWS Glue Data Catalog table for customer records. Before downstream processing, the team must repeatedly check that customer IDs are complete and unique and record a measurable quality result. Which solution best meets the requirement?
Answer and full explanation
Correct answer: C — Evaluate an AWS Glue Data Quality DQDL ruleset.
Why C is correct
AWS Glue Data Quality lets a team define reusable rules and evaluate them against datasets represented in the AWS Glue Data Catalog. Data Quality Definition Language (DQDL) includes rule types such as completeness and uniqueness, which directly match the customer-ID requirements in the scenario. DEA-C01 Task 3.4 requires candidates to run quality checks, define quality rules, investigate consistency, and handle related data-quality concerns. When a ruleset runs, AWS Glue evaluates the rules and produces a data quality score based on the proportion of rules that pass. This provides both repeatable validation and a measurable result for downstream controls.
Why the other options are less suitable
- A — File-size alarms: File size can help detect ingestion anomalies, but it does not verify that specific customer identifiers are complete or unique. The requirement is about row-level data characteristics rather than infrastructure volume alone.
- B — S3 Lifecycle: Lifecycle rules manage storage-class transitions and expiration based on object criteria. They do not inspect individual records or evaluate data-quality constraints such as uniqueness.
- D — CloudTrail data events: CloudTrail can record access activity for auditing, but a successful object read says nothing about whether the dataset meets completeness or uniqueness requirements. Audit logging is not a substitute for data-quality validation.
Key takeaway: Use explicit data-quality rules when a pipeline must validate dataset characteristics such as completeness, uniqueness, consistency, or integrity.
3. Centralizing fine-grained data-lake permissions
A company stores analytics data in Amazon S3 and catalogs it with the AWS Glue Data Catalog. Analysts query the data with Athena and Redshift Spectrum, but different teams must see only approved tables and columns. Which solution best centralizes this authorization?
Answer and full explanation
Correct answer: A — Use AWS Lake Formation for centralized fine-grained permissions.
Why A is correct
AWS Lake Formation provides centralized access controls for data lakes and integrates with the AWS Glue Data Catalog. It can grant permissions at table and column granularity and is integrated with analytics services including Amazon Athena and Amazon Redshift Spectrum. DEA-C01 Task 4.2 explicitly includes managing permissions through Lake Formation and applying authorization methods that satisfy business requirements. This lets the company keep shared data in Amazon S3 while defining which principals may query specific catalog resources and columns. The approach is more scalable and governable than duplicating datasets or granting broad access.
Why the other options are less suitable
- B — Separate buckets and copies: Duplicating datasets for every analyst creates storage, synchronization, and governance overhead. It is unnecessary when Lake Formation can enforce permissions against shared cataloged data.
- C — Administrator access: Broad administrator permissions violate least-privilege principles and do not enforce the required restrictions. Query conventions are not an authorization control.
- D — Route 53 policies: Route 53 controls DNS routing behavior. It does not grant or deny table- or column-level permissions in an AWS analytics data lake.
Key takeaway: Lake Formation centralizes fine-grained authorization for cataloged data used by integrated AWS analytics services.
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Requirements
- AWS describes the target candidate as having 2–3 years of data engineering experience and at least 1–2 years of hands-on AWS experience, including data pipelines, storage, SQL, data quality, and governance. Amazon Web Services, Inc.
- If some objectives are unfamiliar, combine question practice with hands-on work using AWS Glue, S3, Redshift, Athena, Kinesis, Lambda, Step Functions, Lake Formation, CloudWatch, and data modeling or pipeline exercises.
- You will need an internet-connected device, a current web browser, and an ExamsDigest account to use the online practice product; this does not imply offline availability or a separate mobile application.
Know the exam you’re studying for
These details describe AWS’s official Solutions Architect - Associate (SAA-C03) exam.
What our students say
“The DEA-C01 practice exams helped me find weak areas in AWS Glue, Athena, data pipelines, and Lake Formation. The explanations made it much easier to understand why one AWS service or architecture was a better fit for each scenario.”
Jordan Keating
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“The questions covered a good range of ingestion, transformation, storage, data quality, monitoring, and security topics. Going back through the questions I missed helped me focus my study time instead of reviewing everything again.”
Isabel Hart
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“I had already worked through AWS training material before trying ExamsDigest. What I liked most was how the explanations compared the answer choices and explained the trade-offs around services like Glue, Redshift, S3, Athena, and Lake Formation.”
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Your questions, answered.
Which AWS Data Engineer exam do these practice questions target?
These questions target AWS Certified Data Engineer – Associate DEA-C01, the exam currently listed by AWS for this certification. Before purchasing or scheduling the official exam, compare the DEA-C01 code with your booking because AWS periodically revises exam guides. The current guide is Version 1.1, published December 12, 2025, and AWS says guide updates are published before the corresponding exam changes take effect.
Who is DEA-C01 practice intended for?
It is intended for data engineers and related professionals who work with ingestion, transformation, data stores, pipelines, quality, security, and governance on AWS. AWS describes the target candidate as having roughly 2–3 years of data engineering experience and at least 1–2 years of hands-on AWS experience. The guide also expects familiarity with ETL pipelines, SQL, source control, data lakes, networking, storage, compute, and AWS security concepts.
How should I combine DEA-C01 practice with hands-on study?
Use practice questions alongside the official guide and your own AWS data-engineering exercises. Useful activities include building Glue ETL jobs, loading data into S3 and Redshift, querying with Athena, working with Kinesis, orchestrating pipelines with Step Functions or MWAA, cataloging schemas, configuring lifecycle policies, applying Lake Formation permissions, and monitoring jobs with CloudWatch. DEA-C01 emphasizes operational decisions as well as service knowledge.
What should I do when I answer a DEA-C01 question incorrectly?
Use the explanation to identify the specific engineering decision you missed, then revisit the matching task statement. Determine whether the mistake involved ingestion, transformation, storage selection, partitioning, schema design, data quality, monitoring, security, or governance. Compare every distractor with the scenario’s performance, cost, operational, and compliance requirements instead of memorizing an answer letter. A practice score can highlight weak areas but should not be treated as a guaranteed exam prediction.
What question formats appear on the official DEA-C01 exam?
AWS lists multiple-choice and multiple-response questions. The guide says 50 questions affect the score and 15 additional questions are unscored; AWS does not identify the unscored items during the exam. These three preview questions intentionally use single-answer multiple-choice because that is the interaction supported by this widget, so they do not reproduce multiple-response items or establish which additional formats exist in the paid ExamsDigest product.
Does completing this practice earn AWS Certified Data Engineer - Associate?
No. Completing an ExamsDigest practice product does not award the AWS certification or replace AWS’s official examination process. Practice access, registering for DEA-C01, taking the exam through AWS’s authorized testing options, and earning the credential are separate activities. AWS currently allows DEA-C01 scheduling through Pearson VUE testing centers or online proctoring and states that the certification is valid for three years after it is earned. Amazon Web Services, Inc.
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