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About the AWS DEA-C01 Exam

Recommended Prerequisites: SAA-C03 or DVA-C02 foundation recommended. AWS suggests 2+ years data pipeline development experience.

The AWS Certified Data Engineer - Associate (DEA-C01) exam validates your ability to design, build, and maintain data pipelines and data stores on AWS. Launched in 2023, this certification addresses the growing demand for data engineering professionals who can implement modern data architectures using services like Glue, Redshift, Kinesis, EMR, and Lake Formation.

The exam consists of 65 questions (multiple-choice and multiple-response) that you need to complete in 130 minutes. AWS uses a scaled scoring model from 100-1000, with a passing score of 720. The exam costs $150 USD and is available at Pearson VUE testing centers or via online proctoring. Your certification remains valid for three years from the date you pass.

Exam Domains and Weighting:

  • Domain 1: Data Ingestion and Transformation (34%) - Designing data ingestion solutions with Kinesis, Glue, and DMS. Building ETL pipelines with Glue ETL and Spark. Implementing data transformation workflows, data validation, and error handling. Managing streaming and batch data ingestion patterns.
  • Domain 2: Data Store Management (26%) - Choosing appropriate data stores (S3, Redshift, DynamoDB, RDS, DocumentDB). Designing data lakes with Lake Formation. Implementing data partitioning strategies, compression, and file formats (Parquet, ORC, Avro). Managing Redshift clusters, workload management, and query optimization.
  • Domain 3: Data Operations and Support (22%) - Automating data pipeline workflows with Step Functions and EventBridge. Monitoring data pipelines with CloudWatch and Glue Data Quality. Implementing data lifecycle policies, archiving with Glacier, and backup strategies. Optimizing costs with S3 Intelligent-Tiering and Redshift Reserved Instances.
  • Domain 4: Data Security and Governance (18%) - Implementing encryption at rest (KMS) and in transit (TLS). Managing access control with IAM and Lake Formation permissions. Ensuring data quality and lineage tracking with Glue Data Catalog. Implementing data classification, masking, and compliance with AWS Macie and Config.

This exam is designed for data engineers with 2-3 years of hands-on experience building data pipelines on AWS. Strong understanding of SQL, Python/Scala, data modeling, and ETL concepts is essential. New to AWS data services? Start with the AWS Cloud Practitioner (CLF-C02) to learn cloud fundamentals first.

Why Take This Certification?

  • High-Demand Data Engineering Role: AWS Data Engineers with DEA-C01 certification earn an average of $125,000 annually in the United States (Source: Global Knowledge IT Skills Report 2024-2025), with senior professionals commanding $140,000-$160,000. The certification validates modern data engineering skills - ETL pipelines, data lakes, and streaming - that every cloud-native company needs.
  • Purpose-Built for Modern Data Architectures: Launched in 2023, DEA-C01 is AWS's newest data certification, replacing the older Big Data Specialty. It focuses on modern data engineering tools like Glue, Lake Formation, and Redshift Serverless - the actual services companies use today, not legacy Hadoop ecosystems.
  • Bridge Between Engineering and Analytics: Data engineers sit at the intersection of software engineering and data science, building the pipelines that enable analytics and ML. The DEA-C01 proves you can design scalable data architectures, implement data quality controls, and optimize pipeline performance - skills that translate directly to business impact.
  • Gateway to Data-Intensive Industries: Companies in finance, healthcare, e-commerce, and media rely heavily on data engineering. The DEA-C01 certification is specifically sought by companies like Capital One, Zillow, and Expedia for their data platform teams, making this a direct pathway to high-growth industries.

What You'll Learn in the DEA-C01 Exam

The AWS DEA-C01 exam covers the entire data engineering lifecycle on AWS, from ingesting raw data through building production-ready data pipelines and data lakes. You'll demonstrate proficiency across data ingestion patterns, ETL design, data store selection, and data governance implementation.

Core AWS Data Services

  • Data Ingestion: Kinesis Data Streams and Firehose for real-time streaming, AWS Glue for batch ETL, Database Migration Service (DMS) for database replication, DataSync for file transfers, and Snow Family for large-scale data migration.
  • Data Storage: S3 for data lakes, Redshift for data warehousing, DynamoDB for NoSQL, RDS and Aurora for relational data, DocumentDB for document databases, and Timestream for time-series data.
  • Data Processing: AWS Glue ETL with Spark, EMR for big data processing, Athena for SQL queries on S3, Lake Formation for data lake management, and Glue DataBrew for visual data preparation.
  • Data Orchestration and Governance: Step Functions for workflow orchestration, EventBridge for event-driven pipelines, CloudWatch for monitoring, Glue Data Catalog for metadata management, and IAM and Lake Formation for access control.

Data Engineering Concepts

  • Designing scalable data ingestion pipelines for streaming (Kinesis) and batch (Glue) workloads
  • Implementing ETL transformations using Glue ETL jobs, Python Shell scripts, and Spark
  • Optimizing data lake architectures with partitioning, compression (Snappy, GZIP), and columnar formats (Parquet, ORC)
  • Managing Redshift clusters including distribution keys, sort keys, VACUUM, and ANALYZE operations
  • Implementing data quality checks, schema evolution, and error handling in data pipelines
  • Securing data with KMS encryption, Lake Formation permissions, and Macie for data classification

How to Prepare for the DEA-C01 Exam

  1. Master AWS Glue and Data Lakes (4-5 weeks): Work through the official AWS DEA-C01 exam guide and focus heavily on Glue - ETL jobs, crawlers, Data Catalog, and Glue DataBrew. Build at least 3-4 complete ETL pipelines using Glue. Learn Lake Formation permissions and row-level security. Practice designing data lakes with proper partitioning and file formats.
  2. Learn Redshift and Data Warehousing (2-3 weeks): Create a Redshift cluster and practice loading data, writing queries, and optimizing performance with distribution keys and sort keys. Understand when to use Redshift vs. Athena vs. EMR. Learn Redshift Spectrum for querying S3 data directly. This is critical as Redshift appears throughout the exam.
  3. Practice Streaming Data Ingestion (2 weeks): Build hands-on projects with Kinesis Data Streams and Firehose. Understand producer-consumer patterns, shard management, and data transformation with Lambda. Practice scenarios where streaming is required (real-time analytics, clickstream data) vs. batch processing (daily reports).
  4. Practice Exams and Data Governance (1-2 weeks): Take timed practice exams to identify weak areas. Focus on data security (KMS, IAM, Lake Formation) and data governance (Data Catalog, lineage tracking, data quality). Review AWS whitepapers on data lakes and modern data architectures. Domain 4 (Security and Governance) accounts for 18% of the exam.

Download the official AWS DEA-C01 exam guide and review the AWS Glue Developer Guide before starting your preparation. Hands-on experience with Glue, Redshift, and Kinesis is essential for passing.

Frequently Asked Questions

No. All Nex Arc practice questions are original content created by certified professionals based on official exam guides and publicly available documentation. We do not offer brain dumps, leaked questions, or actual exam content. Using or distributing real exam questions violates certification provider agreements and can result in certification revocation. Our questions are designed to test the same knowledge and skills as the real exam, using different scenarios and wording.
The AWS DEA-C01 exam consists of 65 questions that you need to complete in 130 minutes. Questions are either multiple choice (one correct answer) or multiple response (two or more correct answers). Our premium course includes 1,040 practice questions across 16 full practice exams with detailed explanations.
The passing score is 720 out of 1000. AWS uses a scaled scoring model, and not all questions carry the same weight. Focus on understanding concepts rather than memorizing answers.
Click on the "Buy Now" button in the sidebar to purchase the complete course. After payment, you'll have instant access to 16 practice exams with 1,040 questions with detailed explanations and lifetime access.
While there are no formal prerequisites, AWS recommends 2-3 years of hands-on experience building data pipelines on AWS. You should have strong SQL and Python/Scala skills, understand data modeling and ETL concepts, and be familiar with AWS services like Glue, Redshift, Kinesis, and S3. Experience with Spark is highly beneficial for the Glue ETL portions.
The AWS DEA-C01 certification is valid for three years from the date you pass the exam. To maintain your certification status, you'll need to recertify by passing the current version of the exam or earning a higher-level AWS certification before your certification expires. AWS typically sends reminders as your expiration date approaches.
The exam costs $150 USD. If you don't pass on your first attempt, you must wait 14 days before retaking the exam. There is no limit to the number of times you can attempt the exam, but you'll need to pay the full exam fee for each attempt. AWS does not offer partial refunds for failed exams.
AWS Glue dominates the exam - you must know Glue ETL jobs, crawlers, Data Catalog, and DataBrew thoroughly. Redshift is critical for data warehousing questions (loading data, optimization, Spectrum). Kinesis Data Streams and Firehose appear frequently for streaming scenarios. Lake Formation for data lake permissions and S3 for storage optimization are also heavily tested. Domain 1 (Data Ingestion and Transformation) accounts for 34% of questions.
DEA-C01 focuses on modern AWS-native data services like Glue, Lake Formation, and Redshift Serverless, while BDS-C00 heavily emphasized Hadoop ecosystem tools (EMR, Hive, Pig). DEA-C01 is more practical for building data pipelines today - it covers serverless data processing, managed ETL, and data lake governance. The Big Data Specialty retired in April 2023, making DEA-C01 the current standard for AWS data engineering certification.
If you're new to AWS, start with the Cloud Practitioner (CLF-C02) to learn cloud fundamentals. Then consider the Solutions Architect Associate (SAA-C03) to understand AWS architecture patterns. The DEA-C01 is best taken after you have solid AWS foundations and 2-3 years of data engineering experience. If you're already experienced with AWS but new to data services, you can go directly to DEA-C01 after learning Glue and Redshift basics.
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