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DEA-C01
DEA-C01 Amazon Web Services

Data Engineer Associate

The AWS Certified Data Engineer – Associate (DEA‑C01) training teaches candidates how to design, build, and operate scalable data ingestion, storage, transformation, and governance pipelines on AWS, ensuring reliable, secure, and cost‑effective data solutions.

130
Minutes
65
Questions
720/1000
Passing Score
$150
Exam Cost
6
Languages

Who Should Take This

It is intended for data engineers, analytics engineers, or cloud developers who have two to three years of hands‑on experience building data pipelines on AWS. These professionals seek to validate their expertise, expand their knowledge of AWS data services, and earn the DEA‑C01 certification to advance their careers.

What's Covered

1 Choose appropriate data sources, configure ingestion pipelines, and transform data to meet analytical and business requirements.
2 Choose appropriate data store solutions, manage data catalogs, configure data lifecycle management, and design schemas for analytics workloads.
3 Automate data processing with workflow orchestration services, monitor and troubleshoot data pipelines, and optimize data operations for performance and cost.
4 Implement authentication, authorization, and encryption for data at rest and in transit, and apply data governance policies using AWS Lake Formation and related services.

Exam Structure

Question Types

  • Multiple Choice
  • Multiple Response

Scoring Method

Scaled scoring from 100 to 1000, minimum passing score of 720

Delivery Method

Pearson VUE testing center or online proctored

Recertification

Recertify every 3 years by passing the current exam or earning a higher-level AWS certification.

What's Included in AccelaStudy® AI

Adaptive Knowledge Graph
Practice Questions
Lesson Modules
Console Simulator Labs
Exam Tips & Strategy
20 Activity Formats

Course Outline

74 learning goals
1 Content Domain 1: Data Ingestion and Transformation
4 topics

Perform data ingestion

  • Identify AWS data ingestion services and explain the roles of Kinesis Data Streams, Kinesis Data Firehose, DMS, AppFlow, Glue, MSK, and S3 Transfer Acceleration in batch and streaming ingestion pipelines.
  • Implement streaming ingestion pipelines using Kinesis Data Streams with shard provisioning, partition key design, enhanced fan-out consumers, and Kinesis Data Firehose with buffering, compression, and delivery configuration to S3, Redshift, or OpenSearch.
  • Implement batch ingestion workflows using AWS DMS for database migration with full-load and CDC modes, AppFlow for SaaS source connectivity, and S3 as a staging layer with event notifications for downstream triggers.
  • Implement managed streaming ingestion with Amazon MSK including topic configuration, consumer group management, and MSK Connect for connector-based data integration patterns.
  • Analyze ingestion pipeline designs to determine replayability, ordering guarantees, throttling resilience, and fan-in or fan-out behaviors across Kinesis, MSK, DMS, and AppFlow for production workload requirements.

Transform and process data

  • Identify AWS data transformation services and explain the roles of AWS Glue (ETL jobs, crawlers, Data Catalog), EMR, Athena, Lambda, and Redshift Spectrum in processing batch and streaming data at varying scales.
  • Implement AWS Glue ETL jobs using PySpark and Glue DynamicFrames to perform schema transformations, format conversions (CSV to Parquet/ORC), column mappings, data deduplication, and partitioned output writes to S3.
  • Implement EMR cluster-based data processing with Spark, Hive, or Presto including cluster sizing, instance fleet configuration, step execution, and EMRFS for S3-backed storage.
  • Implement lightweight transformation using Lambda functions for event-driven record-level processing and Kinesis Data Firehose data transformation with Lambda-based preprocessing before delivery.
  • Implement SQL-based transformation using Athena CTAS and INSERT INTO operations for materialized views, Redshift stored procedures for warehouse-side transformations, and Glue DataBrew for visual data preparation.
  • Implement data format optimization by converting between CSV, JSON, Parquet, ORC, and Avro using Glue ETL or EMR, applying compression codecs (Snappy, GZIP, LZO, ZSTD), and selecting formats for read-heavy vs write-heavy workloads.
  • Analyze transformation service tradeoffs to determine when to use Glue vs EMR vs Athena vs Lambda based on data volume, velocity, cost, latency, and operational complexity constraints.

Orchestrate data pipelines

  • Identify AWS orchestration services and explain the roles of Step Functions, MWAA (Managed Workflows for Apache Airflow), Glue Workflows, and EventBridge in scheduling, coordinating, and managing data pipeline dependencies.
  • Implement Step Functions state machines with task, choice, parallel, map, wait, and error-handling states to orchestrate multi-service data pipelines with retry logic, catch blocks, and callback patterns.
  • Implement Glue Workflows with triggers, crawlers, and ETL job chaining to build scheduled and event-driven data pipeline graphs with dependency management and notification on completion or failure.
  • Implement event-driven pipeline triggers using EventBridge rules, S3 event notifications, and SNS/SQS integration to initiate processing workflows in response to data arrival events.
  • Implement MWAA (Managed Workflows for Apache Airflow) environments with DAG deployment via S3, environment sizing, plugin and requirements management, and Airflow operator integration with AWS services.
  • Analyze orchestration designs for scalability, fault tolerance, idempotency, and operational maintainability and select the appropriate orchestration service based on workflow complexity and team expertise.

Apply programming concepts for data engineering

  • Identify programming and infrastructure-as-code concepts relevant to data engineering including SQL, PySpark, CloudFormation, CDK, and CI/CD practices for pipeline deployment.
  • Implement data pipeline infrastructure using CloudFormation or CDK templates to define Glue jobs, Step Functions, S3 buckets, IAM roles, and event rules as repeatable, version-controlled resources.
  • Implement SQL-based data manipulation for joins, aggregations, window functions, and CTEs used in Athena, Redshift, and Glue SQL contexts for analytical query development.
  • Implement PySpark data processing patterns including DataFrame operations, RDD transformations, partitioning strategies, broadcast joins, and Spark UI interpretation for Glue and EMR job development.
  • Identify distributed computing concepts and explain how data shuffling, partitioning, parallelism, and executor memory management affect Spark job performance on Glue and EMR clusters.
  • Analyze code quality, testing strategies, and CI/CD pipeline designs for data engineering workflows to improve deployment reliability, change management, and rollback safety.
2 Content Domain 2: Data Store Management
4 topics

Choose a data store

  • Identify AWS storage and database services and explain when to use S3, DynamoDB, RDS, Aurora, Redshift, OpenSearch, and ElastiCache based on data access patterns, latency, and consistency requirements.
  • Implement S3-based data lake storage with bucket design, prefix strategies, storage class selection (Standard, IA, Glacier), versioning, and object lifecycle policies for cost-efficient data tiering.
  • Implement Amazon Redshift cluster and serverless configurations with distribution styles, sort keys, compression encodings, materialized views, and workload management (WLM) queues for analytical workloads.
  • Implement DynamoDB table design with partition keys, sort keys, global and local secondary indexes, read/write capacity modes (on-demand vs provisioned), and DynamoDB Streams for change data capture.
  • Implement RDS and Aurora database configurations including instance sizing, read replicas, Multi-AZ deployments, automated backups, and performance tuning for transactional data engineering workloads.
  • Analyze data store tradeoffs among S3, Redshift, DynamoDB, RDS, and OpenSearch to select the optimal storage engine based on query patterns, data volume, cost, and migration complexity.

Understand data cataloging systems

  • Identify AWS data cataloging capabilities and explain how the Glue Data Catalog, Glue crawlers, Glue Schema Registry, and Lake Formation data catalog integration support metadata management and schema discovery.
  • Implement Glue crawlers to discover and catalog S3, JDBC, and DynamoDB data sources with classification, schema inference, partition detection, and crawler scheduling for automated catalog maintenance.
  • Implement Glue Schema Registry for schema versioning, compatibility enforcement (backward, forward, full), and serialization/deserialization of Avro, JSON Schema, and Protobuf records in streaming pipelines.
  • Analyze catalog strategies for discoverability, governance alignment, and downstream analytics usability to determine appropriate crawl schedules, partition schemes, and metadata enrichment approaches.

Manage the lifecycle of data

  • Identify data lifecycle management concepts and explain how S3 lifecycle policies, Glacier vault lock, DynamoDB TTL, Redshift snapshot scheduling, and RDS automated backups control data retention and archival.
  • Implement S3 lifecycle rules to transition objects across storage classes (Standard to IA to Glacier to Deep Archive), configure expiration policies, and manage versioned object cleanup with noncurrent version transitions.
  • Implement data retention and expiration controls using DynamoDB TTL for automatic item deletion, Redshift snapshot management for point-in-time recovery, and RDS retention policies for backup windows.
  • Analyze lifecycle policy impacts on durability, legal compliance, retrieval latency, and cost across storage tiers to design data retention strategies that satisfy regulatory and operational requirements.

Design data models and schema evolution

  • Identify data modeling concepts and explain the differences among star schema, snowflake schema, denormalized models, wide tables, and key-value patterns as applied in Redshift, DynamoDB, and S3-based data lakes.
  • Implement dimensional data models in Redshift with fact and dimension tables, distribution and sort key alignment, and late-binding views for schema-on-read flexibility across data warehouse layers.
  • Implement schema evolution strategies using Glue Schema Registry compatibility modes, Athena schema-on-read with SerDe configuration, and Parquet/ORC column addition for backward-compatible data lake evolution.
  • Implement data partitioning strategies for S3-based data lakes using Hive-style partitioning, partition projection in Athena, and bucketing in Glue to optimize query performance and minimize scan costs.
  • Analyze schema migration and data model evolution decisions to evaluate compatibility, lineage traceability, query performance impact, and downstream consumer readiness across analytical systems.
3 Content Domain 3: Data Operations and Support
4 topics

Automate data processing by using AWS services

  • Identify AWS automation capabilities and explain how Lambda, Step Functions, EventBridge Scheduler, Glue triggers, and MWAA DAGs support automated and scheduled data processing.
  • Implement Lambda-based automation for data processing tasks including S3 event-driven triggers, SQS-based batch processing, scheduled invocations via EventBridge rules, and error handling with dead-letter queues.
  • Implement MWAA DAG-based orchestration for complex multi-step data pipelines with task dependencies, branching logic, sensor-based waiting, and failure notification integration.
  • Analyze automation workflow failures and determine root causes across Lambda timeouts, Step Functions state transitions, Glue job errors, and MWAA task failures to improve production reliability.

Analyze data by using AWS services

  • Identify AWS analytics services and explain when to use Athena, Redshift, QuickSight, OpenSearch, and EMR for ad hoc querying, dashboarding, full-text search, and large-scale analytical processing.
  • Implement Athena queries over S3 data lakes using the Glue Data Catalog, partition pruning, columnar format optimization, workgroups for cost control, and federated queries for cross-source analytics.
  • Implement Redshift analytical queries with distribution-aware join strategies, Redshift Spectrum for querying external S3 data, data sharing across clusters, and result caching for performance optimization.
  • Analyze query performance bottlenecks and optimize analytical accuracy, runtime efficiency, data scan costs, and service utilization across Athena, Redshift, and OpenSearch workloads.

Maintain and monitor data pipelines

  • Identify AWS monitoring and observability services and explain the roles of CloudWatch Metrics, CloudWatch Logs, CloudWatch Alarms, EventBridge, and SNS in pipeline health monitoring and alerting.
  • Implement CloudWatch dashboards, custom metrics, and log groups for Glue job metrics, Lambda invocation tracking, Kinesis iterator age monitoring, and Redshift query performance visibility.
  • Implement alerting and notification workflows using CloudWatch Alarms with threshold and anomaly detection, SNS topic notifications, and EventBridge rules to trigger automated remediation actions.
  • Analyze operational telemetry patterns to detect pipeline anomalies, classify incident severity, correlate failures across ingestion-transformation-delivery stages, and prioritize corrective remediation steps.

Ensure data quality

  • Identify data quality concepts and explain how Glue Data Quality rules, Athena query-based validation, DynamoDB conditional writes, and Lambda-based checks enforce consistency, completeness, and correctness.
  • Implement Glue Data Quality rules with DQDL expressions for null checks, uniqueness validation, referential integrity, and custom rule evaluation integrated into Glue ETL job workflows.
  • Implement data validation gates within pipelines using Lambda-based row-level checks, Athena query assertions for aggregate constraints, and Step Functions choice states for quality-based routing decisions.
  • Analyze data quality failures to isolate root causes across source systems, transformation logic, and delivery stages and design durable prevention mechanisms including schema enforcement and dead-letter routing.
4 Content Domain 4: Data Security and Governance
4 topics

Apply authentication mechanisms

  • Identify AWS identity and authentication services and explain the roles of IAM users, roles, policies, STS, identity federation, and service-linked roles in securing data service access.
  • Implement IAM roles and policies for data services including Glue job execution roles, Lambda execution roles, Redshift IAM-based authentication, and EMR service roles with least-privilege scoping.
  • Implement cross-account access patterns using IAM role assumption, STS AssumeRole, and resource-based policies for S3 cross-account bucket access and Redshift data sharing across accounts.
  • Analyze authentication boundary designs across managed and unmanaged data services to identify misconfigured trust relationships, overly permissive roles, and unauthorized access exposure.

Apply authorization mechanisms

  • Identify AWS authorization mechanisms and explain how IAM identity-based policies, resource-based policies, Lake Formation permissions, and S3 access points provide layered data access control.
  • Implement Lake Formation fine-grained access controls with database, table, column, row, and cell-level permissions, data filters, and tag-based access control (LF-TBAC) for centralized data lake authorization.
  • Implement S3 bucket policies, access points, and S3 Object Lambda to enforce authorization boundaries for multi-tenant and cross-account data access patterns in data lake architectures.
  • Analyze authorization strategy gaps across IAM, Lake Formation, and S3 policies and refine permission constructs to enforce least-privilege access and satisfy governance constraints.

Ensure data encryption and masking

  • Identify AWS encryption and data protection services and explain KMS key types, key policies, grants, SSE-S3, SSE-KMS, SSE-C, client-side encryption, Macie, and Redshift dynamic data masking capabilities.
  • Implement encryption at rest and data masking using KMS customer managed keys for S3, Redshift, DynamoDB, RDS, Glue Data Catalog, and Kinesis, combined with Glue PII detection, Macie for sensitive data discovery, and Redshift dynamic data masking policies.
  • Analyze encryption, masking, and tokenization strategies against compliance obligations, data utility requirements, key management overhead, and cross-service encryption consistency to select appropriate data protection approaches.

Prepare logs for audit and ensure data privacy and governance

  • Identify AWS audit and governance services and explain how CloudTrail, S3 server access logs, Redshift audit logging, Lake Formation, AWS RAM, Macie, and S3 Object Lock support audit logging, data sharing, PII handling, and compliance governance.
  • Implement centralized audit logging using CloudTrail with multi-region trail configuration, S3 log delivery, CloudTrail Lake for SQL-based event analysis, and log integrity validation with digest files.
  • Implement Lake Formation governed tables, cross-account data sharing with AWS RAM, and data residency controls using S3 region restrictions, VPC endpoints, and service control policies for governance enforcement.
  • Analyze audit log patterns, governance framework completeness, and data sharing controls to determine investigative queries, verify compliance, and maintain secure collaboration across accounts and teams.

Hands-On Labs

25 labs ~575 min total Console Simulator

Practice in a simulated cloud console or Python code sandbox — no account needed. Each lab runs entirely in your browser.

Certification Benefits

Salary Impact

$145,000
Average Salary

Related Job Roles

Data Engineer Data Architect ETL Developer Data Pipeline Engineer Analytics Engineer

Industry Recognition

The AWS Data Engineer Associate certification validates in-demand data engineering skills on the world's largest cloud platform. With the explosion of data-driven decision-making, certified AWS data engineers are sought after for building scalable analytics infrastructure across enterprises.

Scope

Included Topics

  • All domains and task statements in the AWS Certified Data Engineer - Associate (DEA-C01) exam guide: Content Domain 1 Data Ingestion and Transformation (34%), Content Domain 2 Data Store Management (26%), Content Domain 3 Data Operations and Support (22%), and Content Domain 4 Data Security and Governance (18%).
  • Associate-level data engineering workflows for ingestion, transformation, orchestration, storage design, operational support, monitoring, and governance in AWS environments.
  • Scenario-based service selection and implementation decisions for building and operating secure, reliable, and cost-efficient data pipelines on AWS.
  • Key AWS services for data engineers: S3, Glue, Athena, Redshift, Kinesis Data Streams, Kinesis Data Firehose, DynamoDB, RDS, Aurora, Lake Formation, EMR, Step Functions, Lambda, EventBridge, MWAA (Managed Workflows for Apache Airflow), DMS, AppFlow, MSK (Managed Streaming for Apache Kafka), CloudWatch, CloudTrail, IAM, KMS, Secrets Manager, Macie, SNS, SQS, QuickSight, OpenSearch Service.

Not Covered

  • Machine learning model development, model training workflows, and data science algorithm design that are outside DEA-C01 job scope.
  • Business intelligence dashboard authoring and data visualization implementation workflows not directly tested in DEA-C01.
  • Programming language-specific syntax mastery beyond high-level programming concepts applied to data pipelines.
  • Transient exact service pricing values and short-lived commercial offers that are not stable for long-term domain specifications.
  • AWS CLI command-level syntax memorization and SDK version-specific API signatures.
  • Professional-level data engineering architecture governance and enterprise operating model design that exceed associate-level objectives.

Official Exam Page

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