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AWS Certified Data Engineer – Associate (DEA-C01) study guide

What the DEA-C01 exam tests, domain by domain, and how to tell the right answer from the nearly right one. Each topic links to fuller study notes and free practice questions.

Last checked against the official exam guide and AWS documentation · How we write our content

About the DEA-C01 exam

The AWS Certified Data Engineer – Associate exam checks whether you can build and run data pipelines on AWS: ingest batch and streaming data, transform it, choose and model data stores, keep pipelines healthy and the data trustworthy, and secure and govern it. AWS aims it at people with the equivalent of 2 to 3 years of data engineering experience and 1 to 2 years of hands-on AWS.
Questions 65: 50 scored and 15 unscored, which aren’t marked
Time 130 minutes
Question types Multiple choice (one answer from four) and multiple response (two or more from five or more)
Score Scaled from 100 to 1,000; 720 to pass
Scoring model Compensatory: you need to pass overall, not in each domain
Guessing An unanswered question counts as wrong, and there is no penalty for a wrong answer, so answer everything

Check the official certification page for the current price, languages and booking options before you book.

What the exam really tests

Questions describe a data flow (sources, volumes, latency needs, consumers) and ask which service, setting or fix fits. The same few decisions come up across all four domains:

  • Batch or streaming? Minutes of delay and files suggest Glue, EMR or Athena; seconds and events suggest Kinesis, MSK or Firehose.
  • Serverless or cluster? “Least operational overhead” favors Glue, Athena, Firehose, Redshift Serverless and EMR Serverless over clusters you size and patch.
  • How is the data laid out? Partitioning, columnar formats (Parquet, ORC), compression and file sizes decide both speed and cost.
  • Who can see what? Lake Formation for fine-grained lake permissions, IAM for services, KMS for encryption, masking for PII.

Troubleshooting questions are common too: a hot shard, a skewed Spark join, a Redshift query waiting on a lock, a crawler that made a hundred tables. Learn the symptom that points to each cause.

How to read a question

  1. Note the data’s volume, format and arrival pattern, and how fresh the result must be.
  2. Note the qualifier: lowest cost, least operational overhead, fastest queries, least privilege.
  3. Rule out options that miss the latency requirement or that need a cluster when the question wants serverless.
  4. Between the rest, prefer native features (Firehose format conversion, Glue job bookmarks, Redshift COPY) over custom code.

How the domains add up

Data Ingestion and Transformation is the largest domain at about a third of the exam. Data Store Management is next. Operations and security together make up the remaining 40%, and their questions often reuse the services from the first two domains.

Domain 1: Data Ingestion and Transformation 34%

The largest domain covers getting data in and reshaping it: streaming with Kinesis, Firehose and MSK, batch ingestion and migration with DMS, AppFlow and DataSync, scheduling and event triggers, transforming with Glue, EMR and Lambda, file formats, orchestration with Step Functions, MWAA and Glue workflows, pipeline code and CI/CD, and preparing data for generative AI.

A data lake pipeline from streaming and database sources to query engines Apps and devices send records to Kinesis Data Streams, which feeds Amazon Data Firehose. An on-premises database replicates through AWS DMS. Both land in an S3 raw zone. An AWS Glue ETL job cleans the data and writes it to an S3 curated zone as partitioned Parquet, which Athena and Redshift Spectrum query. Apps and devices On-prem database Kinesis Data Streams real-time records AWS DMS full load + changes Data Firehose buffers and batches S3 raw zone data as it arrived AWS Glue ETL job clean, convert to Parquet S3 curated zone partitioned by date Athena Redshift Spectrum
A typical lake pipeline. Streaming records flow through Kinesis Data Streams and Firehose, and database changes arrive through DMS, both landing raw in S3. A Glue job cleans the data and writes compressed, partitioned Parquet to a curated zone, which Athena and Redshift Spectrum query in place using the Glue Data Catalog.

Domain 2: Data Store Management 26%

Data Store Management asks you to choose the right store (S3, Redshift, DynamoDB, RDS, OpenSearch and others), design Redshift and DynamoDB tables, use open table formats such as Apache Iceberg, catalog data with Glue, partition and compress it for fast, cheap queries, manage its lifecycle and retention, and evolve schemas safely.

Domain 3: Data Operations and Support 22%

This domain is about running pipelines and using the data: querying with Athena, preparing data with DataBrew and SageMaker, analyzing and visualizing with SQL and Amazon Quick Sight, monitoring and logging pipelines, troubleshooting failures and slow jobs, and checking data quality before bad data spreads.

Domain 4: Data Security and Governance 18%

Security and governance questions cover IAM roles for pipeline services, private network access to sources, secrets, fine-grained permissions with Lake Formation, Redshift users, roles, masking and data sharing, encryption with KMS (including across accounts), audit logging, PII discovery and data governance.

Lake Formation as one permissions layer in front of a data lake An analyst role uses Athena and an engineer role uses Glue and EMR. Both go through Lake Formation permissions, which control access at database, table, column, row and cell level. Behind Lake Formation sit the Glue Data Catalog, holding table definitions, and Amazon S3, holding the data files. Analyst role no PII columns Engineer role full access Athena Glue and EMR Lake Formation permissions database, table, column, row and cell Glue Data Catalog table definitions Amazon S3 the data files
Lake Formation puts one permissions layer in front of the data lake. You grant access to databases, tables, columns, rows or cells once, and the integrated engines (Athena, Redshift Spectrum, EMR, Glue) enforce it. That is the answer when a question asks to hide PII columns from analysts without copying data or writing a bucket policy for each prefix.

A 6-week study plan

This plan assumes about an hour on weekdays and a longer, hands-on session at the weekend. If you build data pipelines on AWS already, four weeks may be enough.

Week Focus Do this
1 Baseline and ingestion Take the free practice exam in timed mode. Then Kinesis Data Streams (shards, keys, fan-out, retention), Firehose, MSK, DMS, AppFlow, DataSync, and event triggers and schedules.
2 Transformation and orchestration Glue (DynamicFrames, bookmarks, worker types, Flex), EMR and EMR Serverless, Lambda for data, file formats, Step Functions, MWAA and Glue workflows.
3 Data stores Choosing stores, Redshift design (distribution and sort keys, COPY, Spectrum, materialized views), DynamoDB modeling, Iceberg, Glue Data Catalog and crawlers, partitioning, lifecycle.
4 Operations Athena (partitions, CTAS, SerDes, Iceberg), DataBrew, Quick Sight, CloudWatch for pipelines, troubleshooting Spark, Redshift and Kinesis, Glue Data Quality.
5 Security and governance IAM for Glue, Redshift and EMR, VPC endpoints, secrets, Lake Formation (LF-tags, data filters, cross-account), Redshift masking and data sharing, KMS, CloudTrail, Macie.
6 Exam practice Full timed practice exams. Review every wrong answer and rebuild the weakest pieces by hand.

How to use practice questions well

  • Link symptoms to causes. Keep a list like “one busy Redshift node → distribution skew” and “Lambda iterator age rising → consumer can’t keep up”.
  • Read the wrong options. They are usually real features that solve a neighboring problem.
  • Don’t memorize questions. The real exam won’t reuse them.
  • Aim for 80% or more across a few full timed practice exams before you book.

Get hands-on

In a personal account with a budget alert set: land CSV files in S3, crawl them, convert them to partitioned Parquet with a Glue job, query both with Athena and compare data scanned; send records to a Kinesis stream and deliver them with Firehose; load a table into Redshift Serverless with COPY. Delete everything afterward.

Frequently asked questions

Do I need to know Spark or Python?

You need to read and reason about them, not write them from memory. Expect questions about Spark concepts (partitions, skew, shuffles), SQL (window functions, joins, CTAS) and pipeline code practices, but not long code listings.

How does this compare with the Solutions Architect exam?

It goes much deeper on analytics services (Glue, Redshift, Kinesis, Athena, Lake Formation) and less deep on general architecture. Some people take SAA first for the broader AWS foundation, but it isn’t required.

Is there an analytics specialty exam instead?

AWS retired the Data Analytics – Specialty exam; Data Engineer – Associate covers much of the same ground.

Are the practice questions the real exam questions?

No. Every question on this site is original, written from the public exam guide and checked against AWS documentation. Using real exam questions breaks the AWS Certification Program Agreement.

Test yourself

The free DEA-C01 practice exam has 65 questions with an explanation for every option. No sign-up needed.

Take the free practice examReview questions untimed

Want to be sure you're ready? The 5 full-length practice sets add 325 new questions, none of them in the free exam. One payment of $14.99, no subscription. See the practice sets