Amazon Web Services · 2026 Edition
A complete preparation guide written by Amazon Web Services-certified engineers. Covers the exam format,all 4 blueprint domains, a week-by-week study plan, and proven tips for passing first time.
3–4 months
Prep time
Intermediate
Difficulty
65
Exam questions
720/1000
Pass mark
Exam code
DEA-C01
Full name
AWS Data Engineer Associate
Vendor
Amazon Web Services
Duration
130 minutes
Questions
65 items
Passing score
720/1000 (scaled)
Domains covered
4 blueprint domains
Recommended experience
1–2 years of data engineering experience; familiarity with SQL and Python; AWS Solutions Architect Associate helpful
Typical prep time
3–4 months
DEA-C01 earns the AWS Certified Data Engineer – Associate designation. It validates the skills to ingest, transform, store, and deliver data at scale on AWS — a role in sustained high demand as organisations build cloud data pipelines.
Job roles this opens
Domain percentage weights are not currently available for this exam. The checklist below is still useful for planning your study.
Weeks 1–3
Data Ingestion and Transformation: Kinesis, Glue, Lambda, EventBridge Pipes
Tip: Kinesis services have distinct roles: Kinesis Data Streams (real-time data collection, you manage shards and consumers), Kinesis Data Firehose (managed delivery to S3/Redshift/OpenSearch, no shard management), Kinesis Data Analytics (SQL or Apache Flink on streaming data). Know which Kinesis service fits which scenario.
Weeks 4–6
Data Storage and Management: S3, Lake Formation, Glue Data Catalog, DynamoDB, Redshift
Tip: AWS Glue Data Catalog is the centralised metadata store for all data assets in a data lake. Know how Glue Crawlers discover schemas from S3 data and populate the Catalog, how Glue ETL jobs read from the Catalog, and how Athena and Redshift Spectrum query Catalog tables without loading data.
Weeks 7–9
Data Operations: Glue Workflows, Step Functions, MWAA, Lake Formation governance
Tip: AWS Step Functions orchestrate data pipeline steps as state machines. Know the difference between Standard Workflows (exactly-once, runs up to 1 year, auditable) and Express Workflows (at-least-once, runs up to 5 minutes, high throughput, lower cost). Data pipelines typically use Standard Workflows for durability.
Weeks 10–12
Data Security and Governance: Lake Formation permissions, encryption, Macie, data quality
Tip: AWS Glue Data Quality evaluates data freshness, completeness, and accuracy using DQDL (Data Quality Definition Language) rules. Know that data quality results can be stored in S3 for auditing and can trigger EventBridge rules to alert on failures — this is a common DEA-C01 architecture scenario.
Amazon Redshift is the most tested analytical database on DEA-C01. Know: Redshift Spectrum (query S3 data without loading it), Redshift Serverless (on-demand capacity, no cluster management), COPY command (bulk load from S3), and UNLOAD command (export query results to S3).
S3 Select allows querying a subset of S3 object data using SQL without retrieving the entire object. Know the supported formats (CSV, JSON, Parquet) and that S3 Select reduces data transfer cost when you only need a few fields from large objects.
Amazon Athena charges per terabyte of data scanned. Optimisation techniques that are directly testable: partition projection (avoid listing S3 partitions in Glue Catalog), columnar formats (Parquet/ORC significantly reduce data scanned vs CSV), and result reuse (cache query results for 1–60 minutes).
AWS Lake Formation row-level and cell-level security restrict which rows and specific cells (column values) a user can see in queries through Athena, Redshift Spectrum, or Glue jobs. Know that this is more granular than S3 bucket-level permissions.
Data mesh architecture patterns appear on DEA-C01: know that a data mesh distributes data ownership to domain teams, uses a centralised governance layer (Lake Formation), and enables self-service analytics without centralising all data in a single repository.
Apply everything in this guide with adaptive practice questions, detailed answer explanations, and domain analytics.
Deep-dive explanations of the key topics tested on DEA-C01 — with exam key points and common misconceptions.