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AWS Certified Data Engineer – Associate (DEA-C01)

AWS Certified Data Engineer – Associate (DEA-C01)

The AWS Certified Data Engineer – Associate (DEA-C01) certification is designed to validate a candidate’s expertise in designing, building, and maintaining data processing solutions on AWS. It emphasizes core competencies such as data ingestion, transformation, orchestration, pipeline monitoring, cost optimization, and data governance.

– Key Skills Validated

Candidates who pass the DEA-C01 exam demonstrate proficiency in the following areas:

  • Data Ingestion & Transformation: Design and implement data workflows that effectively ingest and transform data using programming best practices.
  • Pipeline Orchestration & Automation: Build scalable and automated data pipelines, ensuring performance optimization and operational efficiency.
  • Storage & Data Modeling: Select the most appropriate data stores, define efficient data models, and manage schema catalogs and lifecycle policies.
  • Monitoring & Troubleshooting: Maintain, monitor, and troubleshoot data pipelines to resolve issues proactively.
  • Data Security & Governance: Implement robust data protection mechanisms, including authentication, encryption, logging, and compliance controls.
  • Data Quality & Analysis: Analyze data quality metrics and ensure consistency and reliability across the data infrastructure.

– Ideal Candidate Profile

The exam is intended for individuals with:

  • 2–3 years of industry experience in data engineering, with a strong grasp of the complexities introduced by data volume, variety, and velocity.
  • 1–2 years of hands-on experience with AWS services, specifically those used for data storage, processing, governance, and analytics.
  • A thorough understanding of how to design data architectures that meet operational, security, and analytical requirements.

– Recommended General IT Knowledge

To be well-prepared for this exam, candidates should be familiar with:

  • Designing and maintaining ETL (Extract, Transform, Load) pipelines from source to destination.
  • Applying language-agnostic programming principles within data workflows.
  • Version control using Git for collaborative development and maintenance.
  • Utilizing data lakes for scalable and cost-effective storage.
  • Foundational knowledge in networking, compute, and storage concepts.

– Recommended AWS Knowledge

A successful candidate should have hands-on expertise with AWS services and be able to:

  • Apply AWS tools and services to perform key tasks such as ingestion, transformation, storage selection, lifecycle management, and data security.
  • Use AWS services for encryption, compliance, and access control in data engineering workflows.
  • Compare and contrast AWS offerings based on performance, cost-efficiency, and capabilities to choose the right service for the job.
  • Construct and execute SQL queries within AWS data services.
  • Analyze datasets using AWS analytics services and validate data quality for consistency and accuracy.

Exam Details

AWS Certified Data Engineer – Associate (DEA-C01)

The AWS Certified Data Engineer (DEA-C01) is an associate-level certification designed to validate expertise in building and managing data pipelines and related workflows on AWS. The exam has a total duration of 130 minutes and consists of 65 questions, presented in either multiple choice or multiple response format.

Candidates can take the exam through a Pearson VUE testing center or opt for the online proctored format, depending on their convenience. The exam is available in English, Japanese, Korean, and Simplified Chinese. The DEA-C01 exam is scored on a scaled range of 100 to 1,000, with a minimum passing score of 720. The result is provided as a pass or fail designation, based on the scaled score achieved.

Course Outline

The exam covers the following topics:

1. Understand Data Ingestion and Transformation

Task Statement 1.1: Performing data ingestion.

Knowledge of:

  • Learn about throughput and latency characteristics for AWS services that ingest data
  • Data ingestion patterns (for example, frequency and data history) (AWS Documentation: Data ingestion patterns)
  • Streaming data ingestion (AWS Documentation: Streaming ingestion)
  • Batch data ingestion (for example, scheduled ingestion, event-driven ingestion) (AWS Documentation: Data ingestion methods)
  • Replayability of data ingestion pipelines
  • Stateful and stateless data transactions

Skills in:

Task Statement 1.2: Transforming and processing data.

Knowledge of:

Skills in:

  • Optimizing container usage for performance needs (for example, Amazon Elastic Kubernetes Service [Amazon EKS], Amazon Elastic Container Service [Amazon ECS])
  • Connecting to different data sources (for example, Java Database Connectivity [JDBC], Open Database Connectivity [ODBC]) (AWS Documentation: Connecting to Amazon Athena with ODBC and JDBC drivers)
  • Integrating data from multiple sources (AWS Documentation: What is Data Integration?)
  • Optimizing costs while processing data (AWS Documentation: Cost optimization)
  • Implementing data transformation services based on requirements (for example, Amazon EMR, AWS Glue, Lambda, Amazon Redshift)
  • Transforming data between formats (for example, from .csv to Apache Parquet) (AWS Documentation: Three AWS Glue ETL job types for converting data to Apache Parquet)
  • Troubleshooting and debugging common transformation failures and performance issues (AWS Documentation: Troubleshooting resources)
  • Creating data APIs to make data available to other systems by using AWS services (AWS Documentation: Using RDS Data API)

Task Statement 1.3: Orchestrating data pipelines.

Knowledge of:

  • How to integrate various AWS services to create ETL pipelines
  • Event-driven architecture (AWS Documentation: Event-driven architectures)
  • How to configure AWS services for data pipelines based on schedules or dependencies (AWS Documentation: What is AWS Data Pipeline?)
  • Serverless workflows

Skills in:

Task Statement 1.4: Applying programming concepts.

Knowledge of:

  • Continuous integration and continuous delivery (CI/CD) (implementation, testing, and deployment of data pipelines) (AWS Documentation: Continuous delivery and continuous integration)
  • SQL queries (for data source queries and data transformations) (AWS Documentation: Using a SQL query to transform data)
  • Infrastructure as code (IaC) for repeatable deployments (for example, AWS Cloud Development Kit [AWS CDK], AWS CloudFormation) (AWS Documentation: Infrastructure as code)
  • Distributed computing (AWS Documentation: What is Distributed Computing?)
  • Data structures and algorithms (for example, graph data structures and tree data structures)
  • SQL query optimization

Skills in:

2. Learn About Data Store Management

Task Statement 2.1: Choosing a data store.

Knowledge of:

Skills in:

  • Implementing the appropriate storage services for specific cost and performance requirements (for example, Amazon Redshift, Amazon EMR, AWS Lake Formation, Amazon RDS, DynamoDB, Amazon Kinesis Data Streams, Amazon MSK) (AWS Documentation: Streaming ingestion)
  • Configuring the appropriate storage services for specific access patterns and requirements (for example, Amazon Redshift, Amazon EMR, Lake Formation, Amazon RDS, DynamoDB) (AWS Documentation: What is AWS Lake Formation?, Querying external data using Amazon Redshift Spectrum)
  • Applying storage services to appropriate use cases (for example, Amazon S3) (AWS Documentation: What is Amazon S3?)
  • Integrating migration tools into data processing systems (for example, AWS Transfer Family)
  • Implementing data migration or remote access methods (for example, Amazon Redshift federated queries, Amazon Redshift materialized views, Amazon Redshift Spectrum) (AWS Documentation: Querying data with federated queries in Amazon Redshift)

Task Statement 2.2: Understanding data cataloging systems.

Knowledge of:

Skills in:

Task Statement 2.3: Managing the lifecycle of data.

Knowledge of:

Skills in:

  • Performing load and unload operations to move data between Amazon S3 and Amazon Redshift (AWS Documentation: Unloading data to Amazon S3)
  • Managing S3 Lifecycle policies to change the storage tier of S3 data (AWS Documentation: Managing your storage lifecycle)
  • Expiring data when it reaches a specific age by using S3 Lifecycle policies (AWS Documentation: Expiring objects)
  • Managing S3 versioning and DynamoDB TTL (AWS Documentation: Time to Live (TTL))

Task Statement 2.4: Designing data models and schema evolution.

Knowledge of:

Skills in:

AWS Certified Data Engineer – Associate

3. Understand Data Operations and Support

Task Statement 3.1: Automating data processing by using AWS services.

Knowledge of:

Skills in:

Task Statement 3.2: Analyzing data by using AWS services.

Knowledge of:

Skills in:

  • Visualizing data by using AWS services and tools (for example, AWS Glue DataBrew, Amazon QuickSight)
  • Verifying and cleaning data (for example, Lambda, Athena, QuickSight, Jupyter Notebooks, Amazon SageMaker Data Wrangler)
  • Using Athena to query data or to create views (AWS Documentation: Working with views)
  • Using Athena notebooks that use Apache Spark to explore data (AWS Documentation: Using Apache Spark in Amazon Athena)

Task Statement 3.3: Maintaining and monitoring data pipelines.

Knowledge of:

Skills in:

Task Statement 3.4: Ensuring data quality.

Knowledge of:

  • Data sampling techniques (AWS Documentation: Using Spigot to sample your dataset)
  • How to implement data skew mechanisms (AWS Documentation: Data skew)
  • Data validation (data completeness, consistency, accuracy, and integrity)
  • Data profiling

Skills in:

4. Learn about Data Security and Governance

Task Statement 4.1: Applying authentication mechanisms.

Knowledge of:

  • VPC security networking concepts (AWS Documentation: What is Amazon VPC?)
  • Differences between managed services and unmanaged services
  • Authentication methods (password-based, certificate-based, and role-based) (AWS Documentation: Authentication methods)
  • Differences between AWS managed policies and customer managed policies (AWS Documentation: Managed policies and inline policies)

Skills in:

Task Statement 4.2: Implementing authorization mechanisms.

Knowledge of:

  • Authorization methods (role-based, policy-based, tag-based, and attributebased) (AWS Documentation: What is ABAC for AWS?)
  • Principle of least privilege as it applies to AWS security
  • Role-based access control and expected access patterns (AWS Documentation: Types of access control)
  • Methods to protect data from unauthorized access across services (AWS Documentation: Mitigating Unauthorized Access to Data)

Skills in:

Task Statement 4.3: Ensuring data encryption and masking.

Knowledge of:

Skills in:

Task Statement 4.4: Preparing logs for audit.

Knowledge of:

Skills in:

Task Statement 4.5: Understanding data privacy and governance.Knowledge of:

Skills in:

  • Granting permissions for data sharing (for example, data sharing for Amazon Redshift) (AWS Documentation: Sharing data in Amazon Redshift)
  • Implementing PII identification (for example, Macie with Lake Formation) (AWS Documentation: Data Protection in Lake Formation)
  • Implementing data privacy strategies to prevent backups or replications of data to disallowed AWS Regions
  • Managing configuration changes that have occurred in an account (for example, AWS Config) (AWS Documentation: Managing the Configuration Recorder)

AWS Data Engineer Associate Exam FAQs

Check here for FAQs!

AWS Certified Data Engineer – FAQs

AWS Exam Policy Overview

Amazon Web Services (AWS) maintains a clear set of policies and procedures that govern its certification exams. These policies are designed to ensure a fair, consistent, and secure examination process. They cover important areas such as exam retakes, unscored content, and score reporting.

– Exam Retake Policy

Candidates who do not pass the AWS certification exam must wait a minimum of 14 days before they are eligible to retake the exam. There is no limit to the number of retakes, but each attempt requires payment of the full registration fee.

– Unscored Content

The AWS Certified Data Engineer – Associate (DEA-C01) exam may include up to 15 unscored questions. These questions are used solely for research and evaluation purposes and do not impact the final score. However, they are not identified within the exam, and candidates should answer all questions to the best of their ability.

– Exam Results and Scoring

The DEA-C01 exam results are presented as a pass or fail outcome. Scoring is based on a scaled system ranging from 100 to 1,000, with a minimum passing score of 720. This score reflects a candidate’s overall performance on the exam and is determined against a predefined standard developed by AWS experts, following industry best practices.

AWS uses a compensatory scoring model, which means that candidates do not need to pass each individual section of the exam; instead, a passing score on the overall exam is sufficient. The exam may include a performance classification table that provides section-level insights into the candidate’s strengths and weaknesses. However, because different sections carry different weights, caution should be used when interpreting this data.

AWS Data Engineer Associate Exam Study Guide

AWS DEA-C01 study guide

Step 1: Understand the Exam Objectives Thoroughly

Begin your preparation by reviewing the official AWS Certified Data Engineer – Associate (DEA-C01) exam guide. This document outlines all the key domains and topics covered in the exam. Understanding these objectives helps you identify which areas require more focus and ensures your study plan aligns with AWS’s expectations. Pay close attention to each domain’s weightage, as it indicates the proportion of questions likely to appear from that topic.

Step 2: Utilize Official AWS Training Resources

Leverage the official AWS training materials, which are curated by AWS experts and aligned with the exam objectives. These include foundational and role-based training that introduce core services and use cases relevant to data engineering. Training paths on the AWS Training and Certification portal are a reliable starting point, offering high-quality, up-to-date resources.

Step 3: Explore AWS Skill Builder for Structured Learning

Use AWS Skill Builder, a free platform that offers on-demand, interactive training modules. Skill Builder provides curated learning plans for aspiring data engineers, including hands-on tutorials, assessments, and scenario-based exercises. This platform is especially useful for reinforcing your theoretical understanding through practical examples and guided walkthroughs.

Step 4: Practice with AWS Builder Labs, Cloud Quest, and AWS Jam

Apply your knowledge in real AWS environments by completing AWS Builder Labs. These labs offer practical, guided tasks that simulate real-world data engineering scenarios. Additionally, explore AWS Cloud Quest: Data Engineer, a gamified learning experience that makes complex concepts more approachable. For a more challenge-based practice, participate in AWS Jam events, which place you in timed, scenario-based challenges that require problem-solving under pressure.

Step 5: Join Study Groups and Community Forums

Engaging with the AWS community can significantly enhance your preparation. Join AWS study groups, online forums, or local meetups where you can discuss difficult topics, ask questions, and share study resources. Platforms like Reddit, LinkedIn, and re:Post by AWS are excellent places to connect with other candidates and AWS-certified professionals.

Step 6: Take Practice Exams to Assess Your Readiness

Finally, validate your preparation by taking full-length DEA-C01 practice tests. These practice exams simulate the actual test environment and help you get accustomed to the question format, time pressure, and content depth. Review your results carefully to identify weak areas, and revisit those topics using AWS documentation or training materials. Repeated practice will build confidence and ensure you’re exam-ready.

AWS Certified Data Engineer – Associate (DEA-C01) tests
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