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Amazon-DEA-C01 Exam Questions
- Topic 1: Data Ingestion and Transformation: This section assesses data engineers on their ability to design scalable data ingestion pipelines. It focuses on collecting and transforming data from various sources for analysis. Candidates should be skilled in using AWS data services to create secure, optimized ingestion processes that support data analysis.
- Topic 2: Data Store Management: This domain evaluates database administrators and data engineers who manage AWS data storage. It covers creating and optimizing relational databases, NoSQL databases, and data lakes. The focus is on performance, scalability, and data integrity, ensuring efficient and reliable storage solutions.
- Topic 3: Data Operations and Support: Targeted at database administrators and engineers, this section covers maintaining and monitoring AWS data workflows. It emphasizes automation, monitoring, troubleshooting, and pipeline optimization, ensuring smooth operations and resolving system issues effectively.
- Topic 4: Data Security and Governance: This section database cloud security engineers on securing AWS data and ensuring policy compliance. It focuses on access control, encryption, privacy, and auditing, requiring candidates to design governance frameworks that meet regulatory standards.
Free Amazon Amazon-DEA-C01 Exam Actual Questions
Note: Premium Questions for Amazon-DEA-C01 were last updated On Apr. 16, 2026 (see below)
A company has an on-premises PostgreSQL database that contains customer data. The company wants to migrate the customer data to an Amazon Redshift data warehouse. The company has established a VPN connection between the on-premises database and AWS.
The on-premises database is continuously updated. The company must ensure that the data in Amazon Redshift is updated as quickly as possible.
Which solution will meet these requirements?
Option B is the only solution that supports near real-time updates from a continuously changing source to Amazon Redshift. The requirement says the on-premises PostgreSQL database is ''continuously updated'' and the target must be updated ''as quickly as possible.'' Nightly full backups or nightly full loads (Options A and D) inherently introduce at least a daily lag, which violates the freshness requirement. Similarly, exporting with pg_dump and reloading with COPY (Option C) is a batch approach and does not provide continuous change propagation.
The study material explicitly positions AWS Database Migration Service (DMS) for database migrations and highlights that it supports both full-load and change data capture (CDC), and that CDC enables continuous replication so ongoing changes can be applied after the initial load.
A company is building a new application that ingests CSV files into Amazon Redshift. The company has developed the frontend for the application.
The files are stored in an Amazon S3 bucket. Files are no larger than 5 MB.
A data engineer is developing the extract, transform, and load (ETL) pipeline for the CSV files. The data engineer configured a Redshift cluster and an AWS Lambda function that copies the data out of the files into the Redshift cluster.
Which additional steps should the data engineer perform to meet these requirements?
Option A is the most direct and operationally efficient way to trigger the existing Lambda-based load into Amazon Redshift whenever a new CSV file is uploaded to Amazon S3. The key requirement is event-driven automation on ''new object created'' events, and the study material explicitly describes using Amazon EventBridge to react to S3 uploads by creating a rule on the default event bus and routing matching events to a target application.
Compared with queue-based designs (Options B and D), Option A reduces components: there is no need to manage an SQS queue, batching/visibility timeout behavior, or retry semantics at the consumer layer. This is especially appropriate because the files are small ( 5 MB) and the Lambda function is already implemented to perform the copy/load step. Using DMS (Option C) is not designed for ''S3 object arrival Lambda Redshift'' event triggering; it introduces unnecessary services and operational work for a simple file-ingestion trigger.
A data engineer is troubleshooting an AWS Glue workflow that occasionally fails. The engineer determines that the failures are a result of data quality issues. A business reporting team needs to receive an email notification any time the workflow fails in the future.
Which solution will meet this requirement?
An ecommerce company wants to use AWS to migrate data pipelines from an on-premises environment into the AWS Cloud. The company currently uses a third-party too in the on-premises environment to orchestrate data ingestion processes.
The company wants a migration solution that does not require the company to manage servers. The solution must be able to orchestrate Python and Bash scripts. The solution must not require the company to refactor any code.
Which solution will meet these requirements with the LEAST operational overhead?
The ecommerce company wants to migrate its data pipelines into the AWS Cloud without managing servers, and the solution must orchestrate Python and Bash scripts without refactoring code. Amazon Managed Workflows for Apache Airflow (Amazon MWAA) is the most suitable solution for this scenario.
Option B: Amazon Managed Workflows for Apache Airflow (Amazon MWAA) MWAA is a managed orchestration service that supports Python and Bash scripts via Directed Acyclic Graphs (DAGs) for workflows. It is a serverless, managed version of Apache Airflow, which is commonly used for orchestrating complex data workflows, making it an ideal choice for migrating existing pipelines without refactoring. It supports Python, Bash, and other scripting languages, and the company would not need to manage the underlying infrastructure.
Other options:
AWS Lambda (Option A) is more suited for event-driven workflows but would require breaking down the pipeline into individual Lambda functions, which may require refactoring.
AWS Step Functions (Option C) is good for orchestration but lacks native support for Python and Bash without using Lambda functions, and it may require code changes.
AWS Glue (Option D) is an ETL service primarily for data transformation and not suitable for orchestrating general scripts without modification.
Amazon Managed Workflows for Apache Airflow (MWAA) Documentation
A data engineer needs to create a new empty table in Amazon Athena that has the same schema as an existing table named old-table.
Which SQL statement should the data engineer use to meet this requirement?
A.
B.
C.
D.
Problem Analysis:
The goal is to create a new empty table in Athena with the same schema as an existing table (old_table).
The solution must avoid copying any data.
Key Considerations:
CREATE TABLE AS (CTAS) is commonly used in Athena for creating new tables based on an existing table.
Adding the WITH NO DATA clause ensures only the schema is copied, without transferring any data.
Solution Analysis:
Option A: Copies both schema and data. Does not meet the requirement for an empty table.
Option B: Inserts data into an existing table, which does not create a new table.
Option C: Creates an empty table but does not copy the schema.
Option D: Creates a new table with the same schema and ensures it is empty by using WITH NO DATA.
Final Recommendation:
Use D. CREATE TABLE new_table AS (SELECT * FROM old_table) WITH NO DATA to create an empty table with the same schema.
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