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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 |
| Questions on the real exam | 0 |
| 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 food delivery company manages thousands of deliveries simultaneously. Each delivery vehicle transmits real-time telemetry data as JSON events. The company wants to accelerate downstream analytics and simplify data processing. The company needs to flatten the telemetry data and then store the data in an Amazon S3 bucket.
Which solution will meet these requirements with the LEAST latency?
Correct Answer: B
The correct answer is B because AWS Glue streaming ETL jobs are built to continuously consume data from streaming sources such as Amazon Kinesis Data Streams, transform the data, and load the results into Amazon S3 data lakes. The requirement is not just delivery to S3; the telemetry JSON must be flattened before storage, and this needs to happen with low latency. Amazon Data Firehose can deliver streaming data, but the option incorrectly assumes automatic flattening without a transformation layer. SQS plus scheduled batch Glue introduces batch delay and does not meet the lowest-latency requirement. Athena is a query engine, not a real-time transformation and ingestion service for writing flattened streaming output to S3. AWS Glue streaming ETL is the correct serverless streaming transformation layer.
A data engineer is configuring an AWS Glue Apache Spark extract, transform, and load (ETL) job. The job contains a sort-merge join of two large and equally sized DataFrames.
The job is failing with the following error: No space left on device.
Which solution will resolve the error?
Correct Answer: C
A sort-merge join generates large shuffle files, leading to ''No space left on device'' errors when both datasets are large. Using a broadcast join sends a smaller dataset to all executors, avoiding shuffle and disk I/O overhead.
''Broadcast joins reduce shuffle I/O by distributing the smaller dataset to all worker nodes, mitigating disk space and shuffle errors.''
-- Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf
This is the most cost-effective and direct fix for large shuffle-stage failures.
A global finance company needs to implement near real-time cross-Region synchronization of trading data between trading centers in the us-east-1 Region, the eu-west-2 Region, and the ap-northeast-1 Region. The company must ensure that data is encrypted in transit. The solution must ensure data ordering and consistency and must support cross-Region disaster recovery. The solution must provide data latency of less than 500 milliseconds.
Which solution will meet these requirements with the LEAST operational effort?
Correct Answer: B
Option B is correct because Amazon MSK Replicator is the AWS-managed feature specifically designed to reliably replicate data across Amazon MSK clusters in different AWS Regions. AWS states that MSK Replicator can be used to build regionally resilient streaming applications for availability and business continuity, which directly supports the disaster recovery requirement. It is also the choice with the least operational effort because it avoids deploying and managing self-hosted replication tools such as Kafka Connect or MirrorMaker 2. AWS documentation for cross-Region setup also shows support for secure connectivity and notes that MSK environments support IAM, TLS, and SASL/SCRAM authentication schemes for private connectivity scenarios.
Option A and C would require the company to deploy, scale, patch, and monitor custom replication infrastructure in every Region, which increases operational overhead. Option D is not appropriate because Kinesis Data Firehose is not the native service for replicating data between MSK clusters. For ordering and consistency, Kafka-based replication preserves partitioned stream semantics better than mixing in a different streaming service. Therefore, a mesh of MSK Replicator relationships between regional MSK clusters is the best AWS-native answer for near real-time, encrypted, cross-Region replication with minimal administration.
A data engineer needs to securely transfer 5 TB of data from an on-premises data center to an Amazon S3 bucket. Approximately 5% of the data changes every day. Updates to the data need to be regularly proliferated to the S3 bucket. The data includes files that are in multiple formats. The data engineer needs to automate the transfer process and must schedule the process to run periodically.
Which AWS service should the data engineer use to transfer the data in the MOST operationally efficient way?
Correct Answer: A
AWS DataSync is an online data movement and discovery service that simplifies and accelerates data migrations to AWS as well as moving data to and from on-premises storage, edge locations, other cloud providers, and AWS Storage services1. AWS DataSync can copy data to and from various sources and targets, including Amazon S3, and handle files in multiple formats. AWS DataSync also supports incremental transfers, meaning it can detect and copy only the changes to the data, reducing the amount of data transferred and improving the performance.AWS DataSync can automate and schedule the transfer process using triggers, and monitor the progress and status of the transfers using CloudWatch metrics and events1.
AWS DataSync is the most operationally efficient way to transfer the data in this scenario, as it meets all the requirements and offers a serverless and scalable solution. AWS Glue, AWS Direct Connect, and Amazon S3 Transfer Acceleration are not the best options for this scenario, as they have some limitations or drawbacks compared to AWS DataSync.AWS Glue is a serverless ETL service that can extract, transform, and load data from various sources to various targets, including Amazon S32.However, AWS Glue is not designed for large-scale data transfers, as it has some quotas and limits on the number and size of files it can process3. AWS Glue also does not support incremental transfers, meaning it would have to copy the entire data set every time, which would be inefficient and costly.
AWS Direct Connect is a service that establishes a dedicated network connection between your on-premises data center and AWS, bypassing the public internet and improving the bandwidth and performance of the data transfer. However, AWS Direct Connect is not a data transfer service by itself, as it requires additional services or tools to copy the data, such as AWS DataSync, AWS Storage Gateway, or AWS CLI. AWS Direct Connect also has some hardware and location requirements, and charges you for the port hours and data transfer out of AWS.
Amazon S3 Transfer Acceleration is a feature that enables faster data transfers to Amazon S3 over long distances, using the AWS edge locations and optimized network paths. However, Amazon S3 Transfer Acceleration is not a data transfer service by itself, as it requires additional services or tools to copy the data, such as AWS CLI, AWS SDK, or third-party software. Amazon S3 Transfer Acceleration also charges you for the data transferred over the accelerated endpoints, and does not guarantee a performance improvement for every transfer, as it depends on various factors such as the network conditions, the distance, and the object size.Reference:
AWS DataSync
AWS Glue
AWS Glue quotas and limits
[AWS Direct Connect]
[Data transfer options for AWS Direct Connect]
[Amazon S3 Transfer Acceleration]
[Using Amazon S3 Transfer Acceleration]
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.
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.