DEA-C01 Study Guide: AWS Data Engineer

How to Pass the AWS Data Engineer Associate (DEA-C01) Exam: Complete Guide — hero

DEA-C01 (AWS Certified Data Engineer - Associate) has 65 questions in 130 minutes, costs USD 150 (JPY 20,000 in Japan) and passes at 720 out of 1,000. Data ingestion and transformation is the largest of its four domains at 34%, followed by data store management (26%), data operations and support (22%) and data security and governance (18%).

The AWS certification hub lists every AWS exam guide and practice app.

Plan about 6 weeks if you already build data pipelines. Exam facts are from the AWS pages under Sources, checked October 15, 2026. StudyKits has no DEA-C01 app.

What is the DEA-C01 exam format?

The DEA-C01 is an associate-level certification that tests your ability to design, build, secure, and maintain data solutions on AWS. You need a scaled score of 720 out of 1,000 to pass. The exam has 65 questions (50 scored, 15 unscored) and you get 130 minutes.

You need hands-on experience with AWS data services and a good grasp of data pipelines, ETL, SQL and data modeling.

The exam costs USD 150. It puts a lot of weight on serverless data services and data lake architectures.

Why take the Data Engineer certification?

Teams need engineers who can build reliable pipelines to move, transform and deliver data. The DEA-C01 shows you can do this on AWS.

This certification is also a strong complement to the Solutions Architect Associate. If you already hold the SAA-C03, adding the DEA-C01 signals that you can design both general architectures and specialized data solutions. Check our AWS Certification Path 2026 guide to see how DEA-C01 fits into your overall certification strategy.

What are the four DEA-C01 domains?

Domain 1: Data Ingestion and Transformation (34%)

This is the heaviest domain on the exam, and it makes sense — data ingestion and transformation are the core of data engineering.

AWS Glue is the most important service in this domain. You need to understand:

Glue crawlers and the Glue Data Catalog — how crawlers discover schema, how the Data Catalog serves as a centralized metadata repository
Glue ETL jobs — PySpark and Python shell jobs, job bookmarks for incremental processing, DynamicFrames vs DataFrames
Glue Studio — visual ETL authoring, custom transforms, and job monitoring
Glue DataBrew — data profiling and visual data preparation without writing code
Glue Schema Registry — schema versioning, compatibility modes, and integration with Kinesis and Kafka

Amazon Kinesis covers real-time data ingestion:

Kinesis Data Streams — shard management, partition keys, enhanced fan-out, and the difference between shared throughput and dedicated throughput consumers
Kinesis Data Firehose — delivery streams to S3, Redshift, OpenSearch, and Splunk. Know the buffering configuration (size and interval) and how to transform data with Lambda before delivery
Kinesis Data Analytics (now Managed Service for Apache Flink) — SQL queries and Apache Flink applications for real-time stream processing

Other ingestion services:

AWS Database Migration Service (DMS) — full load and CDC (change data capture), source and target endpoints, and migration tasks
AWS Transfer Family — SFTP, FTPS, and FTP for file-based ingestion into S3
Amazon AppFlow — SaaS data integration from Salesforce, Google Analytics, Slack, and other sources
AWS DataSync — high-speed data transfer between on-premises storage and AWS

Study tip: the exam loves questions about choosing the right ingestion method for different scenarios. Batch vs real-time, file-based vs streaming, structured vs semi-structured — know which service fits each use case.

Domain 2: Data Store Management (26%)

This domain tests your ability to choose and manage the right data store for different workloads.

Amazon S3 is the foundation of every data lake:

Storage classes and lifecycle policies — know when to use Standard, Intelligent-Tiering, Glacier Instant Retrieval, and Glacier Deep Archive
S3 data organization — partitioning strategies using Hive-style prefixes (year=/month=/day=), and how partition design affects query performance
Data formats — Parquet vs ORC vs Avro vs JSON. Understand columnar vs row-based formats and when to use each
S3 Select and S3 Object Lambda — filtering data at the storage layer

Amazon Redshift is the data warehouse service:

Cluster architecture — leader node and compute nodes, node types (RA3 vs DC2)
Redshift Serverless — automatic scaling without managing clusters
Distribution styles — EVEN, KEY, ALL, and AUTO. Know how distribution affects join performance
Sort keys — compound vs interleaved sort keys and their impact on query performance
Redshift Spectrum — querying data directly in S3 without loading it into Redshift
Materialized views and data sharing across Redshift clusters

Amazon DynamoDB for NoSQL workloads:

Partition key and sort key design for access patterns
DynamoDB Streams for change data capture
Export to S3 for analytics workloads

Amazon RDS and Aurora:

Aurora zero-ETL integration with Redshift
Read replicas for analytics queries
RDS snapshot export to S3 in Parquet format

AWS Lake Formation:

Building and managing data lakes with fine-grained access control
Table-level and column-level permissions
Data filters for row-level and cell-level security
Cross-account data sharing
Integration with Glue Data Catalog

Domain 3: Data Operations and Support (22%)

This domain covers automating, orchestrating, and monitoring data pipelines.

AWS Step Functions for pipeline orchestration:

Standard vs Express workflows
Error handling with Retry and Catch
Integration with Glue, Lambda, ECS, and other services
Parallel and Map states for concurrent processing

Amazon Managed Workflows for Apache Airflow (MWAA):

DAG authoring and scheduling
When to use MWAA vs Step Functions
Environment sizing and scaling

AWS EventBridge:

Event-driven pipeline triggers
Schedule-based rules for periodic pipeline runs
Cross-account event routing

Monitoring and troubleshooting:

CloudWatch metrics and alarms for data pipeline health
Glue job run metrics and error handling
CloudTrail for auditing data access
AWS CloudFormation and CDK for infrastructure as code

Data quality:

Glue Data Quality rules and recommendations
Data validation within pipelines
Handling schema evolution and schema drift

Domain 4: Data Security and Governance (18%)

Security and governance questions focus on protecting data and controlling access.

Encryption — KMS integration with S3, Redshift, Glue, and Kinesis. Client-side vs server-side encryption. Key policies and grants.
IAM for data services — Glue job roles, Redshift IAM roles for COPY/UNLOAD, Lake Formation permissions vs IAM policies.
Data masking and tokenization — protecting PII in data pipelines using Glue transforms or custom Lambda functions.
Compliance — VPC endpoints for data services, S3 bucket policies for cross-account access, and audit logging with CloudTrail.
Lake Formation governance — centralized permissions model, tag-based access control, and how Lake Formation permissions override IAM policies for Glue Data Catalog resources.

How much SQL and ETL do you need?

Unlike other AWS associate exams, the DEA-C01 assumes you have strong SQL and ETL knowledge. You will not see raw SQL questions, but many scenarios require you to understand:

JOIN types and their performance implications
Window functions for running totals, rankings, and aggregations
Partitioning and bucketing for query optimization
Slowly changing dimensions (SCD Type 1 and Type 2)
Star schema vs snowflake schema for data warehouse design
Data normalization and denormalization trade-offs
Incremental vs full load strategies
Idempotent pipeline design — why pipelines should produce the same output when run multiple times

If your SQL skills are rusty, spend the first week of your study plan refreshing them. You do not need to be an expert, but you need to understand these concepts well enough to evaluate architectural decisions.

What does a 6-week study plan look like?

Week 1: Foundations and SQL Refresh

Review data engineering fundamentals: data lakes, data warehouses, ETL vs ELT
Refresh SQL skills: JOINs, aggregations, window functions, CTEs
Set up an AWS account with free tier services
Read the DEA-C01 exam guide from AWS
Start answering 20 practice questions per day (AWS Skill Builder has an official practice question set)

Week 2: Data Ingestion with Glue and Kinesis

Deep dive into AWS Glue: crawlers, ETL jobs, Data Catalog, job bookmarks
Learn Kinesis Data Streams and Firehose
Hands-on lab: create a Glue crawler, run an ETL job to transform CSV to Parquet
Hands-on lab: set up a Kinesis Firehose delivery stream to S3
25 practice questions per day

Week 3: Data Stores — S3, Redshift, and DynamoDB

Master S3 data organization and partitioning strategies
Learn Redshift architecture, distribution styles, and sort keys
Understand Redshift Spectrum and serverless
Study DynamoDB design patterns for analytics
Hands-on lab: load data into Redshift and query with Spectrum
30 practice questions per day

Week 4: Data Operations and Orchestration

Learn Step Functions for pipeline orchestration
Study MWAA basics and when to choose it over Step Functions
Understand EventBridge for event-driven pipelines
Learn Lake Formation setup and permissions
Hands-on lab: build a multi-step pipeline with Step Functions calling Glue jobs
30 practice questions per day

Week 5: Security, Governance, and Advanced Topics

Deep dive into encryption for data services
Master Lake Formation permissions and tag-based access control
Study data quality tools and schema evolution handling
Review DMS for migration scenarios
40 practice questions per day

Week 6: Review and Practice Exams

Take full-length practice exams (65 questions, timed)
Review all incorrect answers and study the underlying concepts
Focus on your weakest domain
Re-do hands-on labs for services you are less confident about
50 practice questions per day
Schedule your exam for the end of this week

How does DEA-C01 differ from other AWS exams?

If you have taken other AWS certifications, here is what makes the DEA-C01 different:

More specialized than SAA-C03. The Solutions Architect exam covers broad architectural patterns. The DEA-C01 goes deep into data-specific services that SAA-C03 only touches on, like Glue, Redshift internals, and Kinesis consumer patterns.

More hands-on than CLF-C02. The Cloud Practitioner tests conceptual knowledge. The DEA-C01 assumes you have built data pipelines and expects you to troubleshoot real-world scenarios.

Assumes SQL knowledge. No other AWS associate exam assumes this level of data modeling and SQL knowledge. If you come from a pure infrastructure background, budget extra time for this.

Heavy on Glue. If you learn one service deeply for this exam, make it AWS Glue. It appears in questions across all four domains.

How should you practise for DEA-C01?

The DEA-C01 questions are scenario-based and often present complex data pipeline architectures. Here is how to approach them:

Identify the data source and destination first. Is data moving from an on-premises database to S3? From S3 to Redshift? From a real-time stream to an analytics dashboard?
Determine if the scenario requires batch or real-time processing. This immediately narrows your service choices.
Look for keywords: “cost-effective” favors serverless options, “lowest latency” favors streaming, “least operational overhead” favors managed services.
Eliminate answers that use services for the wrong use case. If the scenario needs real-time processing, any answer using Glue batch ETL is likely wrong.

Practice these patterns daily. Start with untimed practice to build understanding, then switch to timed practice in weeks 5-6 to build exam-day stamina.

What should you do on exam day?

The DEA-C01 questions can be long. Read the scenario carefully and identify the core requirement before looking at the answers.
When stuck between two answers, consider which one requires less operational overhead. AWS almost always prefers managed and serverless solutions.
Pay attention to data formats mentioned in the question. If the scenario involves analytical queries, Parquet or ORC is almost always the right format.
Time management matters. With 65 questions in 130 minutes, you have exactly 2 minutes per question. Flag complex questions and move on.

What is the fastest route to passing DEA-C01?

Use this guide as your roadmap, follow the 6-week study plan, and practice consistently with questions written to the DEA-C01 exam guide.

StudyKits doesn’t have a DEA-C01 app; if you’re also considering SAA-C03, see our SAA-C03 guide.

Frequently asked questions

How much does the DEA-C01 exam cost?

USD 150. In Japan it is JPY 20,000.

What is the DEA-C01 passing score?

720 on a scale of 100-1,000.

What is the largest DEA-C01 domain?

Data Ingestion and Transformation, at 34%.

Is DEA-C01 the replacement for the old Data Analytics Specialty?

The Data Analytics - Specialty exam is no longer in AWS’s exam guide index. DEA-C01 is now AWS’s data exam, at associate level.

Keep reading

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Sources

Checked October 15, 2026:

How to Pass the AWS Data Engineer Associate (DEA-C01) Exam: Complete Guide