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AWS Certified Data Engineer Associate

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AWS Certified Data Engineer - Associate

The AWS Certified Data Engineer Associate (DEA-C01) exam confirms a candidate’s skill in setting up data pipelines and addressing issues related to cost and performance using best practices. The exam also verifies a candidate’s ability to:

  • Ingest and transform data, and manage data pipelines with programming concepts.
  • Opt for the best data store, devise data models, organize data schemas, and handle data lifecycles.
  • Operate, sustain, and supervise data pipelines.
  • Evaluate data and guarantee data quality.
  • Implement suitable authentication, authorization, data encryption, privacy, and governance.
  • Activate logging.

Target Audience

The ideal candidate should possess around 2–3 years of experience in data engineering. They should grasp how the volume, variety, and velocity of data impact aspects like ingestion, transformation, modeling, security, governance, privacy, schema design, and optimal data store design. Additionally, the candidate should have hands-on experience with AWS services for at least 1–2 years.

Recommended general IT knowledge includes:

  • Setting up and maintaining extract, transform, and load (ETL) pipelines from ingestion to destination
  • Application of high-level programming concepts, regardless of language, as required by the pipeline
  • Utilization of Git commands for source control
  • Knowledge of data lakes for storing data
  • General understanding of networking, storage, and compute concepts

Recommended AWS knowledge for the candidate includes:

  • Knowing how to utilize AWS services to complete the tasks outlined in the Introduction section of this exam guide
  • Grasping the AWS services related to encryption, governance, protection, and logging for all data within data pipelines
  • Being able to compare AWS services to comprehend the differences in cost, performance, and functionality
  • Having the skill to structure and execute SQL queries on AWS services
  • Understanding how to analyze data, check data quality, and maintain data consistency using AWS services

Exam Details

aws exam detail

AWS Data Engineer Associate is an associate-level exam that will have 85 questions. The time duration for the exam is 170 minutes. The exam consists of two types of questions:

  • Multiple choice: You choose one correct response from four options, including three incorrect ones (distractors).
  • Multiple response: You pick two or more correct responses from five or more options.

The passing score for the exam is 720. The exam cost is 75$ USD and is available in English language.

Course Outline

This exam course ourline contains information about the weightings, content domains, and tasks for the exam. It provide extra details for each task statement to aid in your preparation. The exam is divided into different content domains, each with its own weighting.

aws data engineer course outline

Domain 1: Data Ingestion and Transformation

Task Statement 1.1: Perform data ingestion.

Knowledge of:

  • 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: Transform and process 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: Orchestrate 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: Apply 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:

Domain 2: Data Store Management

Task Statement 2.1: Choose 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: Understand data cataloging systems.

Knowledge of:

Skills in:

Task Statement 2.3: Manage the lifecycle of data.

Knowledge of:

Skills in:

Task Statement 2.4: Design data models and schema evolution.

Knowledge of:

Skills in:

Domain 3: Data Operations and Support

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

Knowledge of:

Skills in:

Task Statement 3.2: Analyze 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)
exam course

Task Statement 3.3: Maintain and monitor data pipelines.

Knowledge of:

Skills in:

Task Statement 3.4: Ensure 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:

Domain 4: Data Security and Governance

Task Statement 4.1: Apply authentication mechanisms.

Knowledge of:

Skills in:

Task Statement 4.2: Apply authorization mechanisms.

Knowledge of:

Skills in:

Task Statement 4.3: Ensure data encryption and masking.

Knowledge of:

Skills in:

Task Statement 4.4: Prepare logs for audit.

Knowledge of:

Skills in:

Task Statement 4.5: Understand 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 Data Engineer Associate Exam FAQs

AWS Exam Policy

Amazon Web Services (AWS) lays out specific rules and procedures for their certification exams. These guidelines cover various aspects of exam training and certification. Some of the key policies include:

Exam Retake Policy:

If a candidate doesn’t pass the exam, they must wait for 14 days before being eligible for a retake. There’s no limit on the number of attempts until the exam is passed, but the full registration fee is required for each attempt.

Exam Rescheduling:

To reschedule or cancel an exam, follow these steps:

  1. Sign in to aws.training/Certification.
  2. Click on the “Go to your Account” button.
  3. Choose “Manage PSI” or “Pearson VUE Exams.”
  4. You’ll be directed to the PSI or Pearson VUE dashboard.
  5. If the exam is with PSI, click “View Details” for the scheduled exam. If it’s with Pearson VUE, select the exam in the “Upcoming Appointments” menu.
  6. Keep in mind that you can reschedule the exam up to 24 hours before the scheduled time, and each appointment can only be rescheduled twice. If you need to take the exam a third time, you must cancel it and then schedule it for a suitable date.

AWS Data Engineer Associate Exam Study Guide

aws study guide

AWS Exam Page

AWS furnishes an exam page that includes the certification’s course outline, an overview, and crucial details. These information are crafted by AWS experts to showcase skills and guide candidates through hands-on exercises reflective of exam scenarios. Further, use the certification page validates proficiency in core data-related AWS services, the ability to implement data pipelines, troubleshoot issues, and optimize cost and performance following best practices. If you’re keen on leveraging AWS technology to transform data for analysis and actionable insights, taking this exam provides an early chance to earn the new certification.

AWS Learning Resources

AWS offers a diverse range of learning resources to cater to individuals at various stages of their cloud computing journey. From beginners seeking foundational knowledge to experienced professionals aiming to refine their skills, AWS provides comprehensive documentation, tutorials, and hands-on labs. The AWS Training and Certification platform offers structured courses led by expert instructors, covering a wide array of topics from cloud fundamentals to specialized domains like machine learning and security. Some of them for AWS Data Engineer Associate exams are:

Join Study Groups

Study groups offer a dynamic and collaborative approach to AWS exam preparation. By joining these groups, you gain access to a community of like-minded individuals who are also navigating the complexities of AWS certifications. Engaging in discussions, sharing experiences, and collectively tackling challenges can provide valuable insights and enhance your understanding of key concepts. Study groups create a supportive environment where members can clarify doubts, exchange tips, and stay motivated throughout their certification journey. This collaborative learning experience not only strengthens your grasp of AWS technologies but also fosters a sense of camaraderie among peers pursuing similar goals.

Use Practice Tests

Incorporating AWS practice tests into your preparation strategy is essential for achieving exam success. These practice tests simulate the actual exam environment, allowing you to assess your knowledge, identify areas for improvement, and familiarize yourself with the types of questions you may encounter. Regularly taking practice tests helps build confidence, refines your time-management skills, and ensures you are well-prepared for the specific challenges posed by AWS certification exams. The combination of study groups and practice tests creates a well-rounded and effective approach to mastering AWS technologies and earning your certification.

aws data engineer practice tests
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