A free, open study guide for the AWS Certified Data Engineer – Associate (DEA-C01) exam: revision notes for every domain, side-by-side comparisons of commonly confused services, a glossary, 20 worked sample questions, and the official syllabus as a checklist with a full free lesson for every topic.
Maintained by SaveMyCert, where you can read every lesson free, practice with explained questions and take timed mock exams.
- Exam at a glance
- Exam domains
- What is in this repo
- Syllabus checklist
- How to study for DEA-C01
- Sample questions
- Free resources
| Exam code | DEA-C01 |
| Level | Associate |
| Questions | 65 |
| Time limit | 130 min |
| Passing score | 720 / 1000 |
| Format | Multiple choice & multiple response |
| Exam fee | $150 |
| Valid for | 3 years |
Exam details change. Always confirm them in the official AWS Certified Data Engineer – Associate (DEA-C01) exam guide from Amazon Web Services.
| # | Domain | Weight | Topics |
|---|---|---|---|
| 1 | Data Ingestion and Transformation | 34% | 4 |
| 2 | Data Store Management | 26% | 4 |
| 3 | Data Operations and Support | 22% | 4 |
| 4 | Data Security and Governance | 18% | 5 |
That is 4 domains and 17 topics. Spend your time in proportion to the weights: the heaviest domain decides more of your score than the lightest.
| File | What it gives you |
|---|---|
| Domain 1 notes | Data Ingestion and Transformation: condensed revision notes per topic |
| Domain 2 notes | Data Store Management: condensed revision notes per topic |
| Domain 3 notes | Data Operations and Support: condensed revision notes per topic |
| Domain 4 notes | Data Security and Governance: condensed revision notes per topic |
| Commonly confused services | Side-by-side tables of the services questions set against each other |
| Glossary | Every in-scope term and service in one sentence |
| Sample questions | 20 worked questions with answers and reasoning |
| Exam-day guide | Booking, testing options, scoring, results and retakes |
Tick each topic off once you can explain it without notes. The "Must know" facts are the ones questions turn on. Each lesson link goes to the complete, free lesson.
Weight: 34%. Ingesting streaming and batch data, transforming and processing it, orchestrating ETL pipelines, and applying programming concepts. Official weighting 34%.
📝 Revision notes: Domain 1: Data Ingestion and Transformation
- Perform data ingestion
1). Reading streaming sources (Amazon Kinesis, Amazon MSK, DynamoDB Streams, AWS DMS, AWS Glue, Amazon Redshift) and batch sources (Amazon S3, Glue, Amazon EMR, DMS, Redshift, AWS Lambda, Amazon AppFlow); batch-ingestion configuration; consuming data APIs; setting up schedulers (Amazon EventBridge, Apache Airflow, time-based) for jobs and crawlers; event triggers (S3 Event Notifications, EventBridge); calling a Lambda function from Kinesis; IP allowlists; throttling and rate limits (DynamoDB, Amazon RDS, Kinesis); managing fan-in/fan-out for streaming distribution; replayability of ingestion pipelines; stateful vs stateless transactions.- 📖 Lesson: Data Ingestion on AWS: Kinesis, MSK, DMS, Glue, and Batch Patterns
- Must know: Kinesis Data Streams for AWS-native streaming; MSK only when the scenario requires Apache Kafka compatibility.
- Must know: A Kinesis shard writes 1 MB/s or 1,000 records/s and shares 2 MB/s of reads; enhanced fan-out gives each consumer a dedicated 2 MB/s pipe.
- Transform and process data
Optimizing container usage (Amazon EKS, ECS); connecting via JDBC/ODBC; integrating data from multiple sources; optimizing cost while processing; implementing transformation services by requirement (Amazon EMR, AWS Glue, Lambda, Amazon Redshift); converting formats (CSV to Apache Parquet); troubleshooting transformation failures and performance; creating data APIs; defining volume, velocity, and variety; integrating large language models (LLMs) for data processing.- 📖 Lesson: Transform and Process Data on AWS: Glue, EMR, Lambda, and Redshift
- Must know: Glue for serverless Spark ETL, EMR for scale and framework control, Lambda for sub-15-minute event-driven transforms, Redshift for SQL ELT in the warehouse.
- Must know: Convert CSV/JSON to compressed, partitioned Parquet — Athena and Redshift Spectrum bill by bytes scanned, so columnar format is a direct cost lever.
- Orchestrate data pipelines
Using orchestration services to build ETL workflows (Lambda, EventBridge, Amazon Managed Workflows for Apache Airflow [MWAA], AWS Step Functions, AWS Glue workflows); building pipelines for performance, availability, scalability, resiliency, and fault tolerance; implementing and maintaining serverless workflows; using notification services for alerts (Amazon SNS, Amazon SQS).- 📖 Lesson: Orchestrating Data Pipelines on AWS: Step Functions, MWAA, and Glue Workflows
- Must know: AWS Step Functions is the default serverless orchestrator: direct integrations with Glue, EMR, Lambda and more, per-state Retry/Catch, and no infrastructure — Standard for long-running ETL, Express for short high-volume work.
- Must know: Amazon MWAA is for Apache Airflow: Python DAGs, the operator ecosystem, and migrations of existing Airflow deployments — but its environment bills while idle, so it is never the answer to a cost or least-overhead question for simple pipelines.
- Apply programming concepts
Optimizing code to reduce runtime; configuring Lambda concurrency and performance; using languages and frameworks (Python, SQL, Scala, R, Java, Bash); software-engineering best practices (version control, testing, logging, monitoring); Infrastructure as Code (AWS CloudFormation, AWS CDK, AWS SAM); CI/CD for data pipelines; distributed computing; data structures and algorithms.- 📖 Lesson: Programming Concepts for AWS Data Engineering: IaC, CI/CD, and Lambda Tuning
- Must know: Reduce runtime by processing less data first: Parquet over CSV, partitioned S3 layouts, predicate pushdown, and fixing the small-files problem — before adding workers.
- Must know: Lambda memory scales CPU and network proportionally; reserved concurrency caps a function to protect downstream systems, and provisioned concurrency pre-warms environments for latency-sensitive work.
Weight: 26%. Choosing and configuring data stores, cataloging data, managing data lifecycle, and designing data models with schema evolution. Official weighting 26%.
📝 Revision notes: Domain 2: Data Store Management
- Choose a data store
1). Selecting and configuring storage for cost, performance, and access patterns (Amazon Redshift, EMR, AWS Lake Formation, Amazon RDS, DynamoDB, Kinesis Data Streams, Amazon MSK); applying storage to use cases (HNSW indexing with Aurora PostgreSQL, Amazon MemoryDB for fast key/value); migration tools (AWS Transfer Family); remote access (Redshift federated queries, materialized views, Redshift Spectrum); managing locks; open table formats (Apache Iceberg); vector index types (HNSW, IVF).- 📖 Lesson: Choosing an AWS Data Store: Redshift, DynamoDB, Aurora, S3, and MemoryDB
- Must know: Read the access pattern first: analytics → Redshift or S3+Athena, OLTP → RDS/Aurora, key/value at scale → DynamoDB, microsecond in-memory → MemoryDB, replayable events → Kinesis/MSK.
- Must know: S3 + Athena is the cost answer for infrequently queried data; an always-on Redshift cluster for occasional queries is the classic trap.
- Understand data cataloging systems
Using data catalogs to consume data at source; building and referencing a technical data catalog (AWS Glue Data Catalog, Apache Hive metastore); discovering schemas and using Glue crawlers; synchronizing partitions with a catalog; creating source/target connections; creating and managing business data catalogs (Amazon SageMaker Catalog).- 📖 Lesson: AWS Glue Data Catalog: Crawlers, Partitions, and the Hive Metastore
- Must know: The Glue Data Catalog is the single metadata source Athena, Redshift Spectrum, EMR, and Glue jobs all share — register a table once, query it at its source from every engine.
- Must know: The Glue Data Catalog is a managed, serverless, Hive metastore-compatible catalog; on EMR it is the least-overhead persistent metastore choice and the one shared with Athena.
- Manage the lifecycle of data
Load and unload operations between Amazon S3 and Amazon Redshift; managing S3 Lifecycle policies to change storage tier and to expire data by age; S3 versioning and DynamoDB TTL; deleting data to meet business and legal requirements; protecting data with appropriate resiliency and availability.- 📖 Lesson: Managing the Data Lifecycle: S3 Storage Tiers, Expiration, and Redshift
- Must know: Use Redshift COPY to load from S3 in parallel (split, compressed files, manifest, IAM role) and UNLOAD to export — never row-by-row INSERTs for bulk loads.
- Must know: Pick storage classes by access frequency and retrieval need: Standard for hot data, Intelligent-Tiering for unknown patterns, Standard-IA for monthly access, One Zone-IA only for re-creatable data, and the Glacier tiers for archives.
- Design data models and schema evolution
1). Designing schemas for Amazon Redshift, DynamoDB, and Lake Formation; addressing changes to data characteristics; schema conversion (AWS DMS Schema Conversion); establishing data lineage (Amazon SageMaker ML Lineage Tracking, SageMaker Catalog); indexing, partitioning, compression, and optimization; vectorization concepts (Amazon Bedrock knowledge base).- 📖 Lesson: Data Modeling on AWS: Redshift, DynamoDB, Lake Formation & Schema Evolution
- Must know: Redshift distribution styles: KEY for collocated joins on high-cardinality columns, ALL for small dimensions, EVEN for large unjoined tables, AUTO when unsure — and skewed DISTKEYs are the classic anti-pattern.
- Must know: Compound sort keys (usually leading with date) drive Redshift block pruning; interleaved keys are rarely the right modern answer.
Weight: 22%. Automating data processing, analyzing data, maintaining and monitoring pipelines, and ensuring data quality. Official weighting 22%.
📝 Revision notes: Domain 3: Data Operations and Support
- Automate data processing by using AWS services
Orchestrating pipelines (Amazon MWAA, AWS Step Functions); troubleshooting managed workflows; calling SDKs; using service features to process data (Amazon EMR, Redshift, AWS Glue); consuming and maintaining data APIs; preparing data for transformation (AWS Glue DataBrew, Amazon SageMaker Unified Studio); querying data (Amazon Athena); using AWS Lambda to automate processing; managing events and schedulers (Amazon EventBridge).- 📖 Lesson: Automate Data Processing on AWS: Step Functions, MWAA, Lambda & EventBridge
- Must know: Step Functions is the serverless default orchestrator: native integrations with Glue, EMR, Athena, and Lambda, per-state Retry/Catch, and a run-a-job pattern that waits for completion.
- Must know: Choose Amazon MWAA when the scenario says complex DAGs, backfills, existing Airflow expertise, or orchestration beyond AWS — and remember its environment is always on and always billed.
- Analyze data by using AWS services
Visualizing data (AWS Glue DataBrew, Amazon QuickSight); verifying and cleaning data (Lambda, Athena, QuickSight, Jupyter notebooks, Amazon SageMaker Data Wrangler); using SQL in Amazon Redshift and Athena to query and create views; using Athena notebooks with Apache Spark; tradeoffs between provisioned and serverless services; data aggregation, rolling averages, grouping, and pivoting.- 📖 Lesson: Analyzing Data on AWS: Athena, Redshift SQL, QuickSight & Spark Notebooks
- Must know: Athena is serverless, pay-per-data-scanned SQL over S3 for ad hoc and spiky workloads; Redshift earns its cost on frequent, concurrent, latency-sensitive queries — Spectrum bridges warehouse SQL to the lake.
- Must know: WHERE filters rows before grouping; HAVING filters aggregated groups after — and ROLLUP, CUBE, and GROUPING SETS produce multi-level subtotals in one query.
- Maintain and monitor data pipelines
Extracting logs for audits; deploying logging and monitoring for traceability; using notifications to send alerts; troubleshooting performance; tracking API calls with AWS CloudTrail; troubleshooting and maintaining pipelines (AWS Glue, Amazon EMR); using Amazon CloudWatch Logs; analyzing logs (Athena, EMR, Amazon OpenSearch Service, CloudWatch Logs Insights).- 📖 Lesson: Monitoring Data Pipelines: CloudWatch, CloudTrail and Log Analysis
- Must know: Match the telemetry source to the question: CloudWatch metrics for performance thresholds, CloudWatch Logs for application behavior, CloudTrail for who-called-which-API audits.
- Must know: CloudWatch Logs Insights queries log groups in place with no infrastructure; OpenSearch Service requires an ingestion pipeline and a domain but delivers real-time full-text search and dashboards.
- Ensure data quality
Running data quality checks while processing (for example, checking for empty fields); defining data quality rules (AWS Glue DataBrew); investigating data consistency; data sampling techniques; implementing data skew mechanisms.- 📖 Lesson: Data Quality on AWS: Glue Data Quality, DataBrew and Skew Handling
- Must know: Memorize the quality dimensions — completeness, accuracy, consistency, validity, uniqueness, timeliness, integrity — and translate scenario symptoms into a dimension, then into the rule that enforces it.
- Must know: Run checks while processing: detect empty fields in-pipeline and choose fail fast, quarantine to a separate S3 path, or tag-and-continue based on how tolerant downstream consumers are.
Weight: 18%. Applying authentication and authorization, encrypting and masking data, preparing audit logs, and managing data privacy and governance. Official weighting 18%.
📝 Revision notes: Domain 4: Data Security and Governance
- Apply authentication mechanisms
1). Updating VPC security groups; creating and updating IAM groups, roles, endpoints, and services; creating and rotating credentials (AWS Secrets Manager); setting up IAM roles for access (Lambda, Amazon API Gateway, AWS CLI, CloudFormation); applying IAM policies to roles, endpoints, and services (S3 Access Points, AWS PrivateLink); managed vs unmanaged services; using domains, domain units, and projects for SageMaker Unified Studio.- 📖 Lesson: Applying Authentication Mechanisms: IAM, Secrets Manager, and Networks
- Must know: Authentication proves identity; authorization decides permissions — IAM resolves every signed request to a principal before any policy is evaluated.
- Must know: Prefer IAM roles with temporary STS credentials for all workloads; long-lived access keys in code or config are almost always the wrong answer.
- Apply authorization mechanisms
Creating custom IAM policies when managed policies fall short; storing application and database credentials (Secrets Manager, AWS Systems Manager Parameter Store); providing database users, groups, and roles access (Amazon Redshift); managing permissions through AWS Lake Formation (Redshift, EMR, Athena, S3); authorization methods (role-based, tag-based, attribute-based); constructing least-privilege policies.- 📖 Lesson: Applying Authorization Mechanisms: Lake Formation, IAM, and Redshift
- Must know: IAM authorizes API actions; Lake Formation authorizes data — its grants are enforced identically by Athena, Redshift Spectrum, EMR, and Glue via vended temporary S3 credentials.
- Must know: Write customer managed IAM policies when managed policies are too broad; explicit Deny always overrides any Allow, making it the tool for hard guardrails.
- Ensure data encryption and masking
Applying data masking and anonymization per compliance laws or company policy; using encryption keys to encrypt/decrypt data (AWS KMS); configuring encryption across AWS account boundaries; enabling encryption in transit or before transit.- 📖 Lesson: Data Encryption and Masking on AWS: KMS, TLS, and Anonymization
- Must know: KMS uses envelope encryption: data keys encrypt the data locally, the KMS key encrypts the data keys, and disabling the KMS key instantly revokes access without re-encrypting anything.
- Must know: AWS managed keys (aws/s3, aws/redshift) can never be shared across accounts — any cross-account encrypted-data scenario requires a customer managed key.
- Prepare logs for audit
Tracking API calls with AWS CloudTrail; storing application logs with Amazon CloudWatch Logs; centralized logging queries with AWS CloudTrail Lake; analyzing logs (Amazon Athena, CloudWatch Logs Insights, Amazon OpenSearch Service); integrating services for logging (Amazon EMR for large log volumes).- 📖 Lesson: Preparing Logs for Audit: CloudTrail, CloudTrail Lake, and CloudWatch Logs
- Must know: CloudTrail management events cover control-plane actions and are on by default (90 days in Event history); data events like S3 GetObject and Lambda invocations must be explicitly enabled on a trail or event data store, in advance, at extra cost.
- Must know: An organization trail logs every member account — including future ones — to one bucket that member accounts cannot tamper with; it is the default answer for centralized multi-account audit capture.
- Understand data privacy and governance
1). Granting permissions for data sharing (Amazon Redshift data sharing); implementing PII identification (Amazon Macie with Lake Formation); preventing backups/replications to disallowed AWS Regions; viewing configuration changes (AWS Config); maintaining data sovereignty; managing data access through SageMaker Catalog projects; governance data frameworks and data-sharing patterns.- 📖 Lesson: Data Privacy and Governance: Macie, Redshift Sharing, Config, and Sovereignty
- Must know: Amazon Macie discovers sensitive data in S3 (managed and custom data identifiers, automated discovery, policy findings) but never enforces access — enforcement is Lake Formation's job.
- Must know: The detect-tag-enforce loop is the tested pattern: Macie findings trigger EventBridge automation that applies Lake Formation LF-tags, and tag-based access control restricts every table or column carrying the pii tag.
- Read the lesson for each topic in the checklist above, starting with the heaviest domain. Every lesson is free on the DEA-C01 revision notes.
- Practice straight after reading. Answer DEA-C01 practice questions on the topic you just read. Each option comes with an explanation of why it is right or wrong.
- Review what you got wrong, re-read that lesson section, and tick the topic off only when you get its questions right.
- Take a full-length DEA-C01 mock exam under the real time limit. Aim to pass mocks comfortably before you book.
- On the last day, skim the DEA-C01 cheat sheet instead of starting anything new.
sample-questions.md has 20 worked DEA-C01 questions with the answer, why each option is right or wrong, and the reasoning steps.
- AWS Certified Data Engineer – Associate (DEA-C01) exam guide: the official source (Amazon Web Services)
- DEA-C01 certification overview
- DEA-C01 revision notes: every lesson, free to read
- DEA-C01 practice questions: with an explanation on every option
- DEA-C01 mock exams: full-length and timed
- DEA-C01 cheat sheet: the key facts on one page
- All certification study guides
Spotted an error or an out-of-date fact? Open an issue with the topic and a link to the official source. See CONTRIBUTING.md.
This guide is licensed under CC BY 4.0. You can reuse and adapt it, including commercially, as long as you credit SaveMyCert with a link to https://www.savemycert.com/.
This is an independent study resource. It is not affiliated with or endorsed by Amazon Web Services. AWS Certified Data Engineer – Associate and DEA-C01 are trademarks of their respective owner. Exam domains and weights are taken from the official exam guide linked above.