200+ Free AWS Data Engineer Practice Questions

The AWS Data Engineer Associate (DEA-C01) exam has an estimated pass rate of around 65%. AWS does not officially publish pass rates. You need an estimated scaled score of 720 out of 1000 to pass, with 65 questions (50 scored + 15 unscored) in 170 minutes. Most candidates with 1-2 years of hands-on AWS data engineering experience pass on their first attempt with thorough preparation.

The DEA-C01 exam has 65 total questions: 50 scored questions and 15 unscored pretest questions. You have 170 minutes (2 hours 50 minutes) to complete the exam. Questions are either multiple choice (one correct answer) or multiple response (two or more correct answers). Approximately 60% of questions are scenario-based, presenting real-world data engineering challenges.

The four exam domains are: Domain 1 – Data Ingestion and Transformation (34%): Kinesis, MSK, DMS, Glue, EMR, Lambda, Step Functions, batch and streaming ingestion, ETL transformation; Domain 2 – Data Store Management (26%): S3, Redshift, Athena, DynamoDB, RDS, Lake Formation, data modeling, partitioning; Domain 3 – Data Operations and Support (22%): CloudWatch, CloudTrail, monitoring, troubleshooting, cost optimization, backup, disaster recovery; Domain 4 – Data Security and Governance (18%): IAM, KMS, Macie, encryption, compliance, data privacy.

Most candidates study for 6-10 weeks, investing 80-120 hours total. AWS recommends 2-3 years of data engineering experience with 1-2 years of hands-on AWS experience. Key study areas: 1) Data ingestion services (Kinesis, DMS, Glue). 2) Data storage services (S3, Redshift, DynamoDB). 3) ETL orchestration (Step Functions, MWAA). 4) Monitoring and operations (CloudWatch, CloudTrail). 5) Security and governance best practices. 6) Complete 200+ practice questions and score 80%+ on practice exams.

Core services tested heavily: Data Ingestion (Kinesis Data Streams, Kinesis Firehose, DMS, Glue, EMR, Lambda); Data Storage (S3, Redshift, Athena, DynamoDB, RDS, Lake Formation); Orchestration (Step Functions, MWAA, EventBridge); Analytics (QuickSight, OpenSearch); Security (IAM, KMS, Macie, Secrets Manager); Monitoring (CloudWatch, CloudTrail). Understanding data pipeline architecture and when to use each service is critical.

Kinesis Data Streams is for real-time data streaming with custom consumers requiring custom code for data processing. It supports replay, allows multiple consumers, and provides per-shard ordering. Kinesis Data Firehose is a fully managed service for loading streaming data into destinations (S3, Redshift, Elasticsearch, Splunk) without custom code. Firehose handles automatic scaling, batching, compression, and format conversion. Use Streams for custom processing; use Firehose for simple delivery to supported destinations.

Use AWS Glue for serverless ETL with minimal infrastructure management, especially for data cataloging, schema discovery, and Spark/Python-based transformations. Glue is ideal for simpler ETL workflows, data preparation, and integration with the Glue Data Catalog. Use Amazon EMR when you need full control over the cluster, support for specific Hadoop ecosystem tools, complex big data processing, machine learning with Spark MLlib, or when you need long-running clusters. EMR provides more flexibility but requires more management.

AWS Lake Formation builds on S3 to provide centralized data lake management. It simplifies data ingestion, cataloging, cleaning, and transformation. Lake Formation provides fine-grained access control at database, table, column, and row levels across multiple analytics services (Athena, Redshift, EMR, QuickSight). It automates data cataloging with Glue crawlers, manages data permissions through a single interface, and enforces consistent security policies across your data lake without needing to configure S3 bucket policies for each service.