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AWS DEA-C01 Exam Questions
Two or three years of building pipelines will get you a long way through DEA-C01, right up until a question asks which of four AWS services costs least for the same throughput. That comparison work, not the engineering, is where the AWS Certified Data Engineer Associate exam separates candidates. The DEA-C01 exam questions below come from the practice bank our candidates use before test day, and they follow the exam guide AWS publishes today. If you already have a booking, start practising and let the domain weightings show you where the gaps are. If the exam is still weeks away, read the topic map first: it will tell you how much of your daily toolset is actually in scope.
| Exam name | AWS Certified Data Engineer - Associate |
| Exam code | DEA-C01 |
| Certification | AWS Certified Data Engineer Associate |
| Practice questions in our bank | 302 |
| Time allowed | 130 minutes |
| Passing score | 720 of 1000 |
| Exam fee | USD 150 |
| Level | Associate |
| Recommended experience | 2 to 3 years in data engineering, 1 to 2 years hands-on with AWS |
DEA-C01 Exam Practice Questions
The questions below are free samples from the same DEA-C01 bank our candidates prepare with, ordered by how heavily AWS weights each domain.
A company needs to use an AWS Glue PySpark job to read specific data from an Amazon DynamoDB table. The company knows the partition key values for the required records. The existing processing logic of the AWS Glue PySpark job requires the data to be in DynamicFrame format. The company needs a solution to ensure that the job reads only the specified data.
Which solution will meet this requirement with the MINIMUM number of read capacity units (RCUs)?
Correct Answer: D
Amazon DynamoDB is optimized for Query operations, which retrieve items efficiently using the partition key and optionally the sort key. Queries consume significantly fewer RCUs than Scan operations because only matching partitions are read.
By performing a Query using the partition key in the key condition expression, the AWS Glue job reads only the required items directly from DynamoDB. This minimizes the number of read capacity units consumed and avoids unnecessary reads.
Using a Scan operation, even when followed by filtering in a DynamicFrame, consumes RCUs for the entire table or index and is highly inefficient. Using only the sort key in a query is invalid without the partition key and would not meet DynamoDB query requirements. The Glue DynamoDB connector filter option still performs a scan under the hood and does not minimize RCUs.
The queried data can be directly loaded into a DynamicFrame, satisfying the existing processing logic requirement without additional transformations.
Therefore, Option D provides the most efficient and cost-effective solution.
A company builds a new data pipeline to process data for business intelligence reports. Users have noticed that data is missing from the reports.
A data engineer needs to add a data quality check for columns that contain null values and for referential integrity at a stage before the data is added to storage.
Which solution will meet these requirements with the LEAST operational overhead?
Correct Answer: B
AWS Glue Data Quality transforms allow you to define built-in rules like IsComplete for null validation and ReferentialIntegrity for relationship validation---all with minimal code and operational overhead.
''Use AWS Glue Data Quality rules such as IsComplete and ReferentialIntegrity within ETL jobs to automatically validate incoming data.''
A data engineer must ingest a source of structured data that is in .csv format into an Amazon S3 data lake. The .csv files contain 15 columns. Data analysts need to run Amazon Athena queries on one or two columns of the dataset. The data analysts rarely query the entire file.
Which solution will meet these requirements MOST cost-effectively?
Correct Answer: D
Amazon Athena is a serverless interactive query service that allows you to analyze data in Amazon S3 using standard SQL. Athena supports various data formats, such as CSV, JSON, ORC, Avro, and Parquet. However, not all data formats are equally efficient for querying. Some data formats, such as CSV and JSON, are row-oriented, meaning that they store data as a sequence of records, each with the same fields. Row-oriented formats are suitable for loading and exporting data, but they are not optimal for analytical queries that often access only a subset of columns. Row-oriented formats also do not support compression or encoding techniques that can reduce the data size and improve the query performance.
On the other hand, some data formats, such as ORC and Parquet, are column-oriented, meaning that they store data as a collection of columns, each with a specific data type. Column-oriented formats are ideal for analytical queries that often filter, aggregate, or join data by columns. Column-oriented formats also support compression and encoding techniques that can reduce the data size and improve the query performance. For example, Parquet supports dictionary encoding, which replaces repeated values with numeric codes, and run-length encoding, which replaces consecutive identical values with a single value and a count. Parquet also supports various compression algorithms, such as Snappy, GZIP, and ZSTD, that can further reduce the data size and improve the query performance.
Therefore, creating an AWS Glue extract, transform, and load (ETL) job to read from the .csv structured data source and writing the data into the data lake in Apache Parquet format will meet the requirements most cost-effectively. AWS Glue is a fully managed service that provides a serverless data integration platform for data preparation, data cataloging, and data loading. AWS Glue ETL jobs allow you to transform and load data from various sources into various targets, using either a graphical interface (AWS Glue Studio) or a code-based interface (AWS Glue console or AWS Glue API). By using AWS Glue ETL jobs, you can easily convert the data from CSV to Parquet format, without having to write or manage any code. Parquet is a column-oriented format that allows Athena to scan only the relevant columns and skip the rest, reducing the amount of data read from S3. This solution will also reduce the cost of Athena queries, as Athena charges based on the amount of data scanned from S3.
The other options are not as cost-effective as creating an AWS Glue ETL job to write the data into the data lake in Parquet format. Using an AWS Glue PySpark job to ingest the source data into the data lake in .csv format will not improve the query performance or reduce the query cost, as .csv is a row-oriented format that does not support columnar access or compression. Creating an AWS Glue ETL job to ingest the data into the data lake in JSON format will not improve the query performance or reduce the query cost, as JSON is also a row-oriented format that does not support columnar access or compression. Using an AWS Glue PySpark job to ingest the source data into the data lake in Apache Avro format will improve the query performance, as Avro is a column-oriented format that supports compression and encoding, but it will require more operational effort, as you will need to write and maintain PySpark code to convert the data from CSV to Avro format.Reference:
Amazon Athena
Choosing the Right Data Format
AWS Glue
[AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide], Chapter 5: Data Analysis and Visualization, Section 5.1: Amazon Athena
A company has a data warehouse in Amazon Redshift. To comply with security regulations, the company needs to log and store all user activities and connection activities for the data warehouse.
Which solution will meet these requirements?
Correct Answer: A
Problem Analysis:
The company must log all user activities and connection activities in Amazon Redshift for security compliance.
Key Considerations:
Redshift supports audit logging, which can be configured to write logs to an S3 bucket.
S3 provides durable, scalable, and cost-effective storage for logs.
Solution Analysis:
Option A: S3 for Logging
Standard approach for storing Redshift logs.
Easy to set up and manage with minimal cost.
Option B: Amazon EFS
EFS is unnecessary for this use case and less cost-efficient than S3.
Option C: Aurora MySQL
Using a database to store logs increases complexity and cost.
Option D: EBS Volume
EBS is not a scalable option for log storage compared to S3.
Final Recommendation:
Enable Redshift audit logging and specify an S3 bucket as the destination.
:
Amazon Redshift Audit Logging
Storing Logs in Amazon S3
A data engineer is using an AWS Glue ETL job to remove outdated customer records from a table that contains customer account information. The data engineer is using the following SQL command:
MERGE INTO accounts t USING monthly_accounts_update s
ON t.customer = s.customer
WHEN MATCHED THEN DELETE
What will happen when the data engineer runs the SQL command?
Correct Answer: A
In AWS Glue's SQL implementation (Spark SQL-compatible), the MERGE INTO statement supports conditional actions.
The clause WHEN MATCHED THEN DELETE deletes matching records from the target table (accounts) where the join condition is true.
''A MERGE INTO statement can perform updates, inserts, or deletes based on the match condition between source and target tables.''
-- Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf
DEA-C01 Exam Domains and Weightings
Domains and weightings follow the official AWS Certified Data Engineer - Associate exam guide.
Data Ingestion and Transformation
34%Batch and streaming ingestion, ETL orchestration, and the programming concepts a pipeline needs. Glue, EMR, Kinesis, Managed Service for Apache Flink, Lambda, and Step Functions all live here. Questions typically fix a throughput or latency requirement and ask which combination meets it without over-provisioning.
Data Store Management
26%Choosing a data store, modelling data, cataloguing schemas, and managing lifecycles across S3, Redshift, DynamoDB, and the Glue Data Catalog. Partitioning, file formats, and compression appear constantly, usually framed as a query that has become too slow or too expensive.
Data Operations and Support
22%Running pipelines day to day: automation, monitoring with CloudWatch, logging, data quality checks, and analysis with Athena, Redshift, and QuickSight. Expect items describing a job that failed overnight, where the task is to find the diagnostic signal that identifies why.
Data Security and Governance
18%Authentication and authorisation, encryption in transit and at rest, privacy controls, data masking, and governance through Lake Formation and IAM. The smallest domain by weight, though it overlaps the other three: a pipeline question can turn on a KMS key policy rather than on the pipeline itself.
Official Revisions by AWS
No revision log published — : AWS lists no change log entries for the DEA-C01 exam guide to date. AWS states that guide revisions are published at least one month before they appear on the exam, so any update will be visible ahead of test day.
Source: the AWS Certified Data Engineer - Associate exam guide. Our question bank is updated on its own cycle and re-checked whenever AWS revises the guide.
What the DEA-C01 Exam Really Tests, and How
Look at the verbs the exam guide uses and the pattern is unmistakable: implement, choose, operationalize, troubleshoot. This is an exam about decisions taken under constraints, delivered in only two question formats, which means the difficulty is carried entirely by the scenarios.
Choosing between overlapping services
Tested via: scenario-based multiple-choice questionsGlue against EMR, Kinesis Data Streams against Firehose, Redshift against Athena. AWS builds several services that can each solve the same problem, and the exam decides between them on cost, operational overhead, or latency. The requirement that settles it is usually one clause in the stem, such as 'with the least operational effort' or 'in near real time'.
Diagnosing a pipeline that misbehaves
Tested via: troubleshooting scenario questionsA job runs slowly, a query scans too much data, a stream falls behind. The exam guide explicitly covers troubleshooting and optimisation, and these items reward knowing the usual causes: unpartitioned data, small-file problems, the wrong file format, an under-provisioned shard count.
Selecting several correct controls at once
Tested via: multiple-response questionsTwo or more correct answers from five or more options, most often on security and governance topics where encryption, IAM, and Lake Formation permissions interact. Credit requires every correct selection, so these items punish partial understanding of how AWS layers access control over data.
Optimising for cost rather than capability
Tested via: scenario-based multiple-choice questionsMany options in this exam will technically work. The exam guide names cost and performance optimisation as a core ability, so the correct answer is regularly the cheapest design that still meets the requirement: a storage class transition, a compression change, or moving from provisioned to on-demand capacity.
How to Pass DEA-C01 on Your First Attempt
Working data engineers usually arrive at this exam with strong skills in one stack and blind spots around everything AWS offers next to it. That, rather than a lack of study time, is what most often costs a first attempt.
Start with a full set before revising anything. Sixty-five questions cold will show you which AWS services you have been avoiding in production. Practitioners who live in Spark commonly find their weakness sits in streaming or in governance, not in transformation.
Turn every miss into a service comparison. Read the documentation for both the option you picked and the correct one, focusing on cost model and operational overhead. DEA-C01 returns to the same service rivalries repeatedly, so one comparison learned properly pays out across several later questions.
Put your remaining hours into ingestion and storage. Data Ingestion and Transformation is 34% of scored content, Data Store Management another 26%. Sixty percent of the exam sits in those two. Security and governance carries 18%, so a gap there is worth roughly half as much of your time per hour spent.
Rehearse the clock once, fully. Sixty-five questions in 130 minutes leaves two minutes each, and scenario stems on this exam run long. A single timed full-length attempt is enough to learn whether you read too slowly, and the result becomes your final revision list.
What to Look for in DEA-C01 Practice Questions
and Why Prefer Pass4Success Practice Material
A failed DEA-C01 attempt means another USD 150 and a mandatory wait before rebooking. Measured against that, what practice material costs matters far less than whether it matches the exam AWS is running. Here is what to check before you trust a bank.
Built against the current exam guide
AWS revises guides periodically, with a month of published notice. Questions written for an earlier revision still look credible, and that plausibility is the risk.
✓ Ours: the bank is updated on a regular cycle to reflect how AWS actually tests, plus a targeted re-check when the exam guide changes. The current date appears in the exam details above.
Scenarios long enough to be realistic
Real DEA-C01 stems describe an architecture and bury the deciding constraint inside it. One-line questions train recall, which is not what this exam measures.
✓ Ours: questions are written at scenario length so the skill being practised is finding the constraint, not recognising a keyword.
A free demo before purchase
The data engineering market has no shortage of recycled question sets. A provider that stands behind its own material can show you some of it first.
✓ Ours: a free demo of both formats, the PDF and the practice test, available before any payment.
Coverage across all four domains
Twenty sample questions cannot tell you whether ingestion or governance is your weak point. You need enough per domain for a pattern to emerge from your misses.
✓ Ours: 302 DEA-C01 questions spanning all four domains.
Sensible against the cost of a retake
The comparison that matters is not provider against provider. It is preparing thoroughly once against paying the exam fee twice.
✓ Ours: the full question bank is $69 less than half of the $150 exam fee. A retake means paying that $150 again, on top of the time already spent preparing. Getting it right the first time costs a fraction of what a second attempt does.
DEA-C01 Exam Discussion: What Recent Candidates Say
The notes below come from candidates who have already sat DEA-C01: which service comparisons came up more than expected, where the cost-optimisation framing caught them out, and what they would revise differently with hindsight. If you have taken the exam, add yours for the next reader.
Ready to Pass DEA-C01 on Your First Attempt?
Get Premium AccessFrequently Asked Questions
What skills are measured on the DEA-C01 exam?
Four domains: Data Ingestion and Transformation (34%), Data Store Management (26%), Data Operations and Support (22%), and Data Security and Governance (18%). Together they cover building pipelines, choosing and modelling data stores, running and monitoring workloads, and securing the data throughout.
What are the prerequisites for taking the DEA-C01 exam?
None are required. AWS recommends two to three years of data engineering experience plus one to two years hands-on with AWS, and expects familiarity with ETL pipelines, SQL, Git, and data lake concepts. No prior AWS certification is needed to book it.
How hard is the DEA-C01 exam?
Genuinely associate-level, and harder than its scope suggests for candidates whose experience sits in one toolset. The challenge is comparative: several services can solve each problem, and you must pick the one that fits the stated cost, latency, or operational constraint. Hands-on breadth across AWS data services matters more than depth in any one.
What types of questions are on the DEA-C01 exam?
Two: multiple choice with one correct answer among four, and multiple response with two or more correct answers among five or more options. There are no labs or case studies. Fifteen of the 65 questions are unscored and indistinguishable from the rest, so treat every question as if it counts.
What are the most common mistakes on the DEA-C01 exam?
Choosing the most capable service rather than the cheapest one that meets the requirement, missing 'least operational overhead' in the stem, underestimating partitioning and file-format questions, and losing multiple-response items on one wrong selection. Governance answers also catch people out, since access can be controlled at the IAM, Lake Formation, or KMS layer.
How do the DEA-C01 topics connect in a real project workflow?
Exactly as a pipeline runs in production. Data is ingested and transformed, lands in a store you selected and modelled, gets monitored and queried day to day, and is governed and encrypted at every stage. The exam mirrors that overlap: an ingestion question can be decided by a security control.
What is an effective final-week review plan for DEA-C01?
Focus on ingestion and storage, which carry 60% of scored content between them. Take one timed full-length test midweek, review every miss as a service comparison, then stop adding new material. Spend the final day on cost models and encryption options, which are quick to refresh and easy to confuse.
Is DEA-C01 worth it in 2026?
For data engineers working on AWS, yes. It is the associate-level credential that maps most directly to the daily job, and it signals breadth across the AWS data stack rather than familiarity with one framework. At USD 150 it is mid-priced among AWS associate exams.
Where does DEA-C01 fit in the AWS certification path?
At the associate tier, with no prerequisite. Candidates often come from CLF-C02 at the foundational level, though many experienced engineers start here. After it, the professional-level and specialty certifications are the usual direction, and AIF-C01 pairs well for engineers moving toward AI workloads.
Related AWS Certification Exams
Reviews of the DEA-C01 bank happen on a set internal schedule instead of being triggered by AWS, since the emphasis a topic receives shifts even when the published guide does not. AWS commits to announcing exam guide revisions at least a month ahead of the live exam, and when one is announced the affected questions and answers are checked again against current AWS documentation in addition to the scheduled pass. The date that work last completed is listed in the exam details table near the top of this page.