AWS Certified Data Engineer - Associate (DEA-C01) is the Associate-level certification for engineers who design and operate data pipelines on AWS. Across the whole journey, from data arriving to being transformed, stored, and used for analysis, it asks which services you should connect, and how, given the characteristics of the data and the requirements.
This post is the guide to our 19-part DEA-C01 domain deep-dive series. Before getting into individual topics, it maps out the exam structure, who should take it, and where this certification sits on the AWS career path.
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Who Is DEA-C01 For?
DEA-C01 resembles designing a water system. From drawing water at the source to treating it, storing it, and delivering it to homes, you have to weigh volume, quality, and cost together at every stage. The exam likewise demands, at each stage of ingestion, transformation, storage, operations, and security, the choice that fits the volume, velocity, and shape of the data.
This certification is a particularly good fit for the following people.
| Target Audience | Why It Fits | |------|------| | Data engineers | Formal validation of design skills across the whole pipeline, from ingestion to storage and operations | | Data analysts and BI engineers | Widen your scope by understanding the ingestion and transformation that happen upstream of analysis | | Back-end developers | Learn how to move application data into an analytics environment | | Cloud engineers moving into data roles | Add data service expertise on top of SAA-C03 infrastructure knowledge | | Aspiring ML engineers | Build the data pipeline foundation that model training depends on |
There is no prerequisite certification. AWS recommends the equivalent of two to three years of data engineering experience, but it is not required. The exam is offered in English, Japanese, Korean, and Simplified Chinese. Preparation is much easier if you can read and write SQL and have worked with data processing code in a language such as Python.
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Exam at a Glance
| Item | Detail | |------|------| | Exam Code | DEA-C01 | | Full Name | AWS Certified Data Engineer - Associate | | Level | Associate | | Passing Score | 720 (scale 100–1,000) | | Questions | 65 (50 scored + 15 unscored) | | Duration | 130 minutes | | Exam Fee | $150 USD | | Validity | 3 years | | Question Types | Multiple choice, multiple response | | Languages | English, Japanese, Korean, Simplified Chinese | | Delivery | Pearson VUE testing center or online proctored exam | | Recommended Experience | Equivalent of 2–3 years of data engineering experience (not required) |
The 15 unscored questions are there so AWS can evaluate them for future exams, and they are not identified on the exam. Unanswered questions are scored as incorrect and there is no penalty for guessing, so answer every question. Scoring is compensatory: you do not need to pass each domain, only reach 720 overall.
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Domain Breakdown
DEA-C01 is made up of four domains. The weights below are the official figures for scored content.
| Domain | Topic | Weight | Deep dives | |------|------|------|------| | Domain 1 | Data Ingestion and Transformation | 34% | 8 posts | | Domain 2 | Data Store Management | 26% | 4 posts | | Domain 3 | Data Operations and Support | 22% | 4 posts | | Domain 4 | Data Security and Governance | 18% | 3 posts |
Domain 1 (Data Ingestion and Transformation) is 34%, more than a third of the exam. The services that actually move a pipeline, such as Kinesis, Glue, EMR, and Step Functions, are concentrated here, so it also needs the most study time. Add Domain 2 (Data Store Management) and you reach 60%, which makes a solid grasp of the ingest, transform, and store flow the core of passing.
!DEA-C01 exam domain weight breakdown across 4 domains
Domain 1: Data Ingestion and Transformation (34%)
This domain is about taking data in, processing it, and tying those steps together into a pipeline.
Key topics: Streaming ingestion (Kinesis Data Streams, Amazon Data Firehose, MSK) and batch ingestion (DMS, AppFlow, DataSync); Spark processing with Glue ETL and EMR; Lambda-based transformation; orchestration with Step Functions, MWAA, and Glue workflows; SQL and Python; IaC and CI/CD.
Exam tip: "Load incoming real-time data into S3 or Redshift without writing code" → Amazon Data Firehose. "Several consumers must each read the same stream and be able to replay it" → Kinesis Data Streams. "Run Spark ETL with no servers to manage" → AWS Glue. "We need fine-grained control over the cluster configuration" → EMR.
Related posts: Streaming and Batch Data Ingestion - Kinesis, MSK, DMS, AppFlow, Data Transformation and Processing, Data Pipeline Orchestration, Programming, IaC, and CI/CD, AWS Data Ingestion Services Deep Dive, Kinesis Deep Dive, AWS Glue Complete Guide, EMR Deep Dive
Domain 2: Data Store Management (26%)
This domain is about choosing the store that fits the data's characteristics and access patterns, and managing catalogs, lifecycles, and schemas.
Key topics: S3 data lakes and Redshift data warehouses; criteria for choosing DynamoDB or RDS; columnar file formats such as Parquet and ORC, and partitioning; the Glue Data Catalog and crawlers; S3 lifecycle and storage classes; schema evolution; Redshift distribution keys and sort keys.
Exam tip: "Join and query data sitting in S3 without loading it into Redshift" → Redshift Spectrum. "We need to cut the cost and run time of Athena queries" → convert to a columnar format such as Parquet and partition the data. The amount of data scanned is the cost.
Related posts: Data Store Selection, Data Cataloging Systems, Data Lifecycle and Schema Evolution, Redshift Deep Dive
Domain 3: Data Operations and Support (22%)
This domain is about automating pipelines, analyzing data, and keeping watch over quality and health.
Key topics: Automating data processing (EventBridge schedules, Lambda, Step Functions); analysis with Athena and Redshift; visualization with QuickSight; CloudWatch logs and alarms; CloudTrail; data quality checks with Glue Data Quality and DataBrew.
Exam tip: "Query data in S3 directly with SQL and no servers" → Athena. "We need to be notified when the pipeline fails" → CloudWatch alarms with SNS. "Check that incoming data satisfies our rules" → Glue Data Quality.
Related posts: Data Processing Automation, Data Analysis and Visualization, Pipeline Monitoring and Data Quality, Athena Deep Dive
Domain 4: Data Security and Governance (18%)
This domain covers who can access the data, and how it is encrypted and audited.
Key topics: Authentication and authorization with IAM roles and policies; fine-grained permissions in Lake Formation; KMS encryption; data masking and anonymization; Secrets Manager; CloudTrail audit logs; sensitive data discovery with Amazon Macie; data privacy and data sharing.
Exam tip: "Control access at the table and column level in a data lake" → Lake Formation. "Automatically find personally identifiable information stored in S3" → Amazon Macie. "Track who called which API" → CloudTrail.
Related posts: Authentication and Authorization Mechanisms, Data Encryption and Masking, Audit Logs and Data Privacy Governance
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Where DEA-C01 Fits in the AWS Certification Path
DEA-C01 is the data-focused certification at the AWS Associate level. Its focus becomes clear when you set it beside the neighboring certifications at the same level.
| Category | SAA-C03 | DEA-C01 | MLA-C01 | |------|------|------|------| | Level | Associate | Associate | Associate | | Full Name | Solutions Architect - Associate | Data Engineer - Associate | Machine Learning Engineer - Associate | | Focus | Designing architectures to fit requirements | Designing and operating data pipelines | Building, deploying, and operating ML models | | Main Audience | Engineers who design AWS solutions | Engineers who ingest, transform, and store data | Engineers who run ML workloads | | Exam Format | 130 minutes · 65 questions | 130 minutes · 65 questions | 130 minutes · 65 questions |
The data pipeline skills you build for DEA-C01 extend in two directions. To round out the design view of the wider infrastructure, see the SAA-C03 guide; to carry curated data on into model training and deployment, see the MLA-C01 guide. If the AWS service names themselves are unfamiliar, the Cloud Practitioner guide is the place to start.
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Study Roadmap for Beginners
A step-by-step study order for people learning data engineering on AWS for the first time.
Step 1 (Weeks 1–2): Start with stores and formats. Get the differences among an S3 data lake, Redshift, and DynamoDB, plus Parquet and partitioning, down first. Store the same data as CSV and as Parquet and query both with Athena, and you will feel the difference.
Step 2 (Weeks 3–5): Ingestion and transformation. Load streaming data into S3 with Kinesis and Firehose, then transform it with a Glue job. This is the heaviest stretch, so give it plenty of time.
Step 3 (Weeks 6–7): Orchestration and operations. Chain the jobs together with Step Functions or Glue workflows, and attach CloudWatch alarms and data quality rules.
Step 4 (Weeks 8–9): Security, governance, and practice questions. Go over Lake Formation permissions, KMS encryption, and Macie, then use the official practice question set on AWS Skill Builder to find your weak domains.
The most common mistake is memorizing the services one at a time. Most DEA-C01 questions describe a whole flow running from ingestion → transformation → storage → analysis. Practice until you can draw two or three representative pipelines end to end, and each individual service falls naturally into place.
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Exam Day Checklist
You take the exam at a Pearson VUE testing center or as an online proctored exam. Before you book, get used to the wording and length of the questions with the official practice question set on AWS Skill Builder.