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Amazon Data-Engineer-Associate Exam Syllabus Topics:

Section	Weight	Objectives
Data Store Management	26%	- Design data models 1. Normalization and denormalization 2. Schema design 3. Partitioning and indexing strategies - Manage data lifecycle 1. Data archiving 2. Amazon S3 storage classes 3. Data retention policies - Understand data cataloging 1. AWS Glue Data Catalog 2. Schema evolution 3. Data discovery and classification - Choose a data store 1. Data lakes vs. data warehouses 2. Access and storage patterns 3. Amazon S3, Amazon RDS, Amazon DynamoDB, Amazon Redshift 4. Data characteristics (structured, semi-structured, unstructured)
Data Security and Governance	18%	- Ensure data encryption 1. Encryption at rest and in transit 2. AWS KMS - Manage data privacy and compliance 1. Data masking and tokenization 2. PII data handling 3. AWS Lake Formation permissions - Apply authentication and authorization 1. Amazon S3 bucket policies 2. Service control policies (SCPs) 3. AWS IAM policies and roles - Implement data quality checks 1. AWS Glue DataBrew 2. Data validation
Data Operations and Support	22%	- Automate data pipelines 1. Scheduling jobs 2. Event-driven triggers 3. AWS Lambda triggers - Manage and troubleshoot data processes 1. Cost optimization 2. Performance tuning 3. Debugging failed jobs - Monitor data pipelines 1. Logging and metrics 2. Amazon CloudWatch 3. AWS CloudTrail
Data Ingestion and Transformation	34%	- Transform and process data 1. Data transformation services (AWS Glue, Amazon EMR, AWS Lambda) 2. Batch and stream processing 3. ETL/ELT patterns 4. Data partitioning and compression - Apply programming concepts 1. Infrastructure as Code (IaC) 2. SQL, Python, Scala 3. Version control - Perform data ingestion 1. Streaming data ingestion 2. Data ingestion patterns (frequency and data history) 3. Replayability of data 4. Throughput and latency characteristics for AWS services 5. Batch data ingestion (scheduled ingestion, event-driven ingestion) - Orchestrate data pipelines 1. AWS Step Functions 2. Amazon Managed Workflows for Apache Airflow (MWAA) 3. AWS Glue Workflows 4. Event-driven architectures

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Amazon AWS Certified Data Engineer - Associate (DEA-C01) Sample Questions (Q260-Q265):

NEW QUESTION # 260
A data engineer is configuring an AWS Glue Apache Spark extract, transform, and load (ETL) Job. The job contains a sort-merge join of two large and equally sized DataFrames.
The job is failing with the following error: No space left on device.
Which solution will resolve the error?

A. Use the AWS Glue Spark shuffle manager.
B. Convert the sort-merge join in the job to be a broadcast join.
C. Deploy an Amazon Elastic Block Store (Amazon EBS) volume for the job to use.
D. Convert the DataFrames to DynamicFrames, and perform a DynamicFrame join in the job.

Answer: D

NEW QUESTION # 261
A company uses an Amazon Redshift Single-AZ cluster for enterprise analytics. The company wants to set up a highly resilient disaster recovery (DR) solution for the cluster. The solution must meet a recovery time objective (RTO) of less than 1 hour.
Which solution will meet this requirement MOST cost-effectively?

A. Configure a Redshift cluster from a cross-Region snapshot copy in a second AWS Region when necessary.
B. Use a Redshift dense storage (DS2) node. Enable Multi-AZ deployment.
C. Use a Redshift RA3 node. Enable Multi-AZ deployment.
D. Use a Redshift RA3 node. Enable cluster relocation.

Answer: C

Explanation:
Option B best meets a highly resilient DR requirement with an RTO under 1 hour because a Multi-AZ deployment is designed to provide rapid recovery through redundancy across Availability Zones, rather than requiring a restore-and-rebuild process after a failure. A snapshot-based strategy (Option C) can be valuable for backup and regional recovery, but restoring a new cluster from snapshots is a heavier operation and is not the most reliable path to consistently achieving sub-hour RTO during real incidents. The study material highlights snapshot/restore as an operational mechanism for creating a new environment from a Redshift cluster's saved state, which inherently implies a restore process and associated time to bring resources online.
Option D (cluster relocation) is primarily an operational move mechanism and is not the same as maintaining continuously resilient capacity for unplanned AZ-impacting events. Option A is not the best choice because Multi-AZ resiliency is aligned with modern Redshift architectures, and RA3 is the recommended node family for contemporary Redshift deployments where performance and managed storage characteristics are key for enterprise analytics. The study material reinforces Amazon Redshift as the platform for high-performance enterprise analytics, so a resilience-first configuration is appropriate.

NEW QUESTION # 262
A company uses an Amazon Redshift cluster as a data warehouse that is shared across two departments. To comply with a security policy, each department must have unique access permissions.
Department A must have access to tables and views for Department A. Department B must have access to tables and views for Department B.
The company often runs SQL queries that use objects from both departments in one query.
Which solution will meet these requirements with the LEAST operational overhead?

A. Group tables and views for each department into dedicated schemas. Manage permissions at the schema level.
B. Update the names of the tables and views to follow a naming convention that contains the department names. Manage permissions based on the new naming convention.
C. Create an IAM user group for each department. Use identity-based IAM policies to grant table and view permissions based on the IAM user group.
D. Group tables and views for each department into dedicated databases. Manage permissions at the database level.

Answer: A

NEW QUESTION # 263
A company needs to build an extract, transform, and load (ETL) pipeline that has separate stages for batch data ingestion, transformation, and storage. The pipeline must store the transformed data in an Amazon S3 bucket. Each stage must automatically retry failures. The pipeline must provide visibility into the success or failure of individual stages.
Which solution will meet these requirements with the LEAST operational overhead?

A. Build an Amazon EventBridge-based pipeline that invokes AWS Lambda functions to perform each stage.
B. Chain AWS Glue jobs that perform each stage together by using job triggers. Set the MaxRetries field to 0.
C. Deploy AWS Step Functions workflows to orchestrate AWS Lambda functions that ingest data. Use AWS Glue jobs to transform the data and store the data in the S3 bucket.
D. Schedule Apache Airflow directed acyclic graphs (DAGs) on Amazon Managed Workflows for Apache Airflow (Amazon MWAA) to orchestrate pipeline steps. Use Amazon Simple Queue Service (Amazon SQS) to ingest data. Use AWS Glue jobs to transform data and store the data in the S3 bucket.

Answer: C

Explanation:
AWS Step Functions is a fully managed, serverless orchestration service designed to coordinate multiple AWS services into reliable workflows. Step Functions natively provide automatic retries, error handling, state-level visibility, and execution history, which directly satisfy the requirements for retry logic and monitoring individual pipeline stages.
In this solution, AWS Lambda functions are used for batch data ingestion, AWS Glue jobs handle transformation, and Amazon S3 is used for storage. Step Functions orchestrate these stages while maintaining clear separation of responsibilities. Each step can be configured with retry and catch policies, allowing the workflow to automatically recover from transient failures without manual intervention.
Chaining AWS Glue jobs alone does not provide detailed stage-level visibility or flexible retry control, and setting MaxRetries to 0 contradicts the retry requirement. EventBridge-based pipelines lack native workflow state tracking. Amazon MWAA introduces unnecessary operational overhead, including environment management and Airflow maintenance.
Therefore, using AWS Step Functions to orchestrate Lambda and Glue provides the most scalable, observable, and low-maintenance solution.

NEW QUESTION # 264
A company is uploading log files from on-premises servers to an Amazon S3 bucket. The company needs to validate that the logs from the on-premises servers are the same as the logs that are stored in the S3 bucket.
Which solution will meet this requirement?

A. Enable S3 Object Lock in compliance mode on the S3 bucket. Upload the objects to the bucket.
B. After uploading the objects to the S3 bucket, enable S3 Object Lock in governance mode on the S3 objects.
C. Create an AWS Lambda function to calculate SHA-256 checksums. Store the results in a separate metadata table. Validate the logs after the upload.
D. Use the AWS SDK to automatically compute CRC32 checksums during the upload. Store the checksums in S3 object metadata.

Answer: D

Explanation:
Amazon S3 natively supports checksum validation during uploads when objects are uploaded by using the AWS SDK. The SDK can automatically calculate a checksum, such as CRC32, on the client side and send it with the upload request. Amazon S3 validates the checksum upon receipt and stores it with the object, ensuring that the data uploaded is exactly the same as the source data.
Storing the checksum in the S3 object metadata provides a lightweight and cost-effective way to verify data integrity without requiring additional compute, storage, or post-processing steps. This approach validates the logs at upload time, which is the most efficient and reliable method.
Using AWS Lambda to calculate SHA-256 checksums would require additional compute, orchestration, and storage, increasing cost and operational complexity. Amazon S3 Object Lock enforces immutability and retention but does not validate data integrity between source and destination. Governance or compliance mode Object Lock cannot detect whether uploaded data matches the original logs.
Therefore, using the AWS SDK checksum validation during upload is the simplest, most efficient, and AWS- recommended approach for ensuring log integrity.

NEW QUESTION # 265
......

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