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AWS Certified Data Engineer – Associate (DEA-C01) Exam Guide
Problem
Amazon is currently the most popular cloud service. That is why people want to get a certificate in Amazon Web Services (AWS). However, there are many AWS certifications. The exam you should start with is the AWS Certified Data Engineer – DEA-C01 exam.
Solution
The AWS Data Engineering Associate (DEA-C01) exam is used to learn how to secure, configure, and monitor solutions in AWS. This tip includes numerous resources to study for this AWS certification exam.
The DEA-C01 Exam
The Data Engineering Associate Certificate (DEA-C01) is used to design, build, and operate solutions in AWS. This exam includes Data Ingestion and Transformation, Data Storage, Data Operation, and support. Finally, it includes a section related to security and governance.
Exam Difficulty
While not overly difficult, this exam has challenging aspects. For those with hands-on experience with ETLs and pipelines in AWS, as well as Glue, Redshift, S3, and IAM knowledge, this exam can be manageable. Otherwise, it is advised to arrive on test day well-prepared.
Passing Score
The minimum score to pass is 700/1000.
Book Recommendations
The following books may be useful:
- AWS Certified Data Engineer Study Guide: Associate (DEA-C01) Exam (Sybex Study Guide)
- AWS Certified Data Engineer Associate Glossary Booklet Exam Code: DEA-C01: 1st Edition – 2025
- AWS Certified Data Engineer Associate Exam Prep 600 Practice Questions Exam Code: DEA-C01: 1st Edition – 2025
- AWS Certified Data Engineer – Associate DEA-C01: Exam Preparation Guide
Study Links
The following links can help prepare you for the exam:
Data Ingestion and Transformation
Perform Data Ingestion
- Accessing data from real-time streaming platforms, such as Amazon Kinesis, Amazon MSK (Managed Streaming for Apache Kafka), DynamoDB Streams, AWS Database Migration Service (DMS), AWS Glue, and Amazon Redshift.
- Extracting data from batch-based systems, including Amazon S3, AWS Glue, Amazon EMR, AWS DMS, Amazon Redshift, AWS Lambda, and Amazon AppFlow.
- Configuring batch ingestion settings to ensure optimal performance and reliability.
- Interacting with data through API endpoints for integration and data consumption.
- Establishing job scheduling using tools like Amazon EventBridge, Apache Airflow, or by configuring time-based schedules for jobs and crawlers.
- Configuring event-driven triggers, such as S3 Event Notifications or EventBridge rules, to automate workflows.
- Invoking AWS Lambda functions from Kinesis for processing incoming data streams.
- Defining IP allowlists to securely enable access to various data sources.
- Handling request throttling and managing service rate limits, especially for services like DynamoDB, Amazon RDS, and Kinesis.
- Orchestrating fan-in and fan-out mechanisms for distributing and aggregating streaming data efficiently.
Data Transformation and Processing
- Enhance container performance by optimizing their use (e.g., with Amazon EKS or Amazon ECS).
- Establish connectivity with various data sources using technologies such as JDBC or ODBC.
- Combine and unify data from diverse origins.
- Reduce expenses associated with data processing through cost-efficient techniques.
- Deploy data transformation tools tailored to specific project needs (e.g., Amazon EMR, AWS Glue, Lambda, or Amazon Redshift).
- Convert data formats as needed (e.g., from .csv to Apache Parquet).
- Identify and fix common errors or bottlenecks in data transformation processes.
- Develop APIs that allow external systems to access data via AWS services.
Pipeline Orchestration
- Use orchestration tools to design and manage data ETL workflows (e.g., Lambda, EventBridge, Amazon MWAA, Step Functions, or AWS Glue workflows).
- Create pipelines designed for high performance, availability, scalability, reliability, and error resilience.
- Implement and support workflows using a serverless architecture.
- Send alerts or trigger processes through messaging services like Amazon SNS or SQS.
Programming Principles in Data Handling
- Improve code efficiency to reduce processing time for data ingestion and transformations.
- Configure Lambda functions to match concurrency and performance demands.
- Utilize SQL for data transformations, including stored procedures in Amazon Redshift.
- Write structured SQL queries that align with pipeline requirements.
- Use Git to manage repositories, including tasks like cloning, branching, and updating.
- Package and deploy serverless applications using AWS SAM (e.g., for Lambda, Step Functions, DynamoDB).
- Access and use storage volumes within Lambda environments.
Managing Data Storage
Selecting Appropriate Data Stores
- Choose storage services that meet performance and budget goals (e.g., Redshift, EMR, Lake Formation, RDS, DynamoDB, Kinesis, MSK).
- Configure storage based on data access needs and usage patterns.
- Match storage solutions like Amazon S3 to the correct scenarios.
- Use data migration tools (e.g., AWS Transfer Family) as part of processing systems.
- Enable data migration or remote access methods such as Redshift federated queries, materialized views, or Redshift Spectrum.
Data Catalog Systems
- Use data catalogs to retrieve data directly from its origin.
- Create and manage catalogs using tools like AWS Glue Data Catalog or Hive metastore.
- Automatically identify data structures and populate catalogs using AWS Glue crawlers.
- Keep partition data in sync with the catalog.
- Set up new connections (sources or targets) to support data cataloging, especially in AWS Glue.
Handle Data Lifecycle Management
- Execute data load and unload processes between Amazon S3 and Amazon Redshift.
- Apply Amazon S3 Lifecycle policies to transition data across different storage classes.
- Automatically remove outdated data using S3 Lifecycle rules based on age.
- Manage versioning in S3 and set time-to-live (TTL) policies in DynamoDB to control data retention.
Design Data Models and Manage Schema Changes
- Create database schemas suited to services like Amazon Redshift, DynamoDB, and Lake Formation.
- Plan for and adapt to evolving data properties.
- Convert database schemas using tools such as AWS Schema Conversion Tool (SCT) or AWS DMS Schema Conversion.
- Use AWS services to establish and trace the history and flow of data (e.g., SageMaker ML Lineage Tracking).
Data Operations and Support
Automate data processing
- Coordinate ETL pipelines with orchestration services like Amazon MWAA or Step Functions.
- Diagnose and resolve issues in Amazon’s managed workflow systems.
- Use software development kits (SDKs) to interact with AWS features programmatically.
- Leverage services such as EMR, Redshift, and Glue for processing datasets.
- Work with APIs for data consumption and management.
- Prepare data for transformation with tools like AWS Glue DataBrew.
- Perform data queries using services like Amazon Athena.
- Automate data handling using AWS Lambda.
- Schedule and manage events through services like EventBridge.
Analyze Data Using AWS Tools
- Build data visualizations with AWS tools like QuickSight or Glue DataBrew.
- Clean and validate data using services such as Lambda, Athena, Jupyter Notebooks, QuickSight, or SageMaker Data Wrangler.
- Use Amazon Athena for querying datasets or creating data views.
- Utilize Athena notebooks powered by Apache Spark to explore and analyze datasets.
Maintain and Monitor Pipelines
- Retrieve and store logs for audit purposes.
- Set up comprehensive logging and monitoring solutions for accountability and tracking.
- Configure alerts using AWS notification services during pipeline monitoring.
- Investigate and resolve performance-related pipeline issues.
- Monitor API activity using AWS CloudTrail.
- Support and troubleshoot data pipelines (e.g., Glue and EMR workflows).
- Use Amazon CloudWatch Logs for capturing application behavior and automating responses.
- Perform in-depth log analysis using services like Athena, OpenSearch, EMR, or CloudWatch Logs Insights.
Assure Data Quality
- Execute quality assurance checks on data during processing (e.g., identifying missing values).
- Define and apply data quality rules using tools like AWS Glue DataBrew.
- Analyze and ensure consistency in data entries using Glue DataBrew.
Data Security and Governance
Use Authentication Mechanisms
- Update security group settings in your VPC for access control.
- Create and maintain IAM roles, groups, services, and endpoints.
- Secure and rotate passwords using services like AWS Secrets Manager.
- Assign IAM roles to services such as Lambda, API Gateway, CLI, or CloudFormation.
- Attach IAM policies to control access via roles, endpoints, or features like S3 Access Points or PrivateLink.
Implement Authorization Mechanisms
- Write custom IAM policies when predefined ones are insufficient.
- Store credentials securely using Secrets Manager or Parameter Store.
- Manage user roles and permissions at the database level, such as in Amazon Redshift.
- Control access to data services (Redshift, EMR, Athena, S3) through Lake Formation.
Enforce Encryption and Masking
- Apply data masking or anonymization practices to meet regulatory or internal standards.
- Use AWS KMS to encrypt and decrypt sensitive data.
- Configure encryption to work across different AWS accounts.
- Ensure secure data transfer by enabling encryption in transit.
Prepare Logging for Audit
- Monitor API activity with AWS CloudTrail.
- Store and manage logs using CloudWatch Logs.
- Use CloudTrail Lake for centralized analysis of audit logs.
- Analyze logs through Athena, CloudWatch Logs Insights, or OpenSearch.
- Integrate multiple AWS services for log management, especially in high-volume use cases like Amazon EMR.
Understand Privacy and Governance Requirements
- Set access permissions to allow secure data sharing (e.g., for Amazon Redshift).
- Identify personally identifiable information (PII) using tools like Macie integrated with Lake Formation.
- Prevent data backup or replication to restricted AWS regions through privacy controls.
- Track changes in configuration settings with AWS Config to enforce governance.
Next Steps
