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Amazon Data-Engineer-Associate Exam Syllabus Topics:

Section	Weight	Objectives
Data Store Management	26%	- Optimize storage performance and cost - Select appropriate data storage solutions
Data Security and Governance	18%	- Implement data security controls - Apply governance and compliance best practices
Data Ingestion and Transformation	34%	- Ingest and transform data using AWS services - Build and manage data pipelines
Data Operations and Support	22%	- Troubleshoot data workflow issues - Monitor and maintain data pipelines

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Amazon AWS Certified Data Engineer - Associate (DEA-C01) Sample Questions (Q52-Q57):

NEW QUESTION # 52
A company uses an Amazon Redshift cluster that runs on RA3 nodes. The company wants to scale read and write capacity to meet demand. A data engineer needs to identify a solution that will turn on concurrency scaling.
Which solution will meet this requirement?

A. Turn on concurrency scaling for the daily usage quota for the Redshift cluster.
B. Turn on concurrency scaling at the workload management (WLM) queue level in the Redshift cluster.
C. Turn on concurrency scaling in the settings during the creation of and new Redshift cluster.
D. Turn on concurrency scaling in workload management (WLM) for Redshift Serverless workgroups.

Answer: D

NEW QUESTION # 53
A company is setting up a new Amazon SageMaker Unified Studio domain. Each of the company ' s business units needs isolated control over its own assets, projects, and metadata. Specific datasets must be shareable with other business units upon approval. The company also requires centralized user authentication and identity mapping.
Which solution will meet these requirements?

A. Configure each business unit as a domain unit with delegated ownership and fine-grained permissions policies. Give users the ability to share assets across domain units with explicit access control. Assign API keys to users for authentication to access the domain portal.
B. Configure each business unit as a separate domain unit to manage permissions on assets, projects, and metadata. Configure AWS IAM Identity Center for centralized authentication. Map user profiles to their respective domain units. Enable cross-business unit sharing through access requests. Instruct domain unit owners to approve or deny the requests.
C. Configure business units to be represented as separate domains. Establish isolated environments with no shared administrative policies. Configure AWS IAM Identity Center for centralized authentication.
Delegate administration at the domain level.
D. Configure business units as separate domain units with owner permissions. Restrict projects exclusively to owners to prevent data sharing between domains. Configure AWS IAM Identity Center for centralized authentication. Map user profiles to their respective domain units.

Answer: B

Explanation:
Option D is correct because domain units in Amazon SageMaker Unified Studio are specifically designed to organize assets and other domain entities by business unit or team, while also enabling delegated authority to domain unit owners for authorization and governance. AWS states that domain units let selected users within each business unit log in and share assets to the catalog, and that users elsewhere in the enterprise can discover those assets and request access. This directly matches the requirement for isolated control with controlled cross-business-unit sharing upon approval. AWS also documents that Unified Studio supports authentication through AWS IAM Identity Center, which satisfies the need for centralized authentication and identity mapping.
Option A is incorrect because API keys are not the correct authentication model for Unified Studio portal access; AWS documents IAM Identity Center and related identity-provider-based sign-in instead. Option B is wrong because it prevents the required approved sharing. Option C is less suitable because the requirement is to isolate business units within a domain while still enabling governed sharing; separate domains would add more separation than needed and complicate sharing. Therefore, one domain with separate domain units, IAM Identity Center, and access-request-based sharing is the best fit.

NEW QUESTION # 54
A company uses Amazon Redshift as its data warehouse. Data encoding is applied to the existing tables of the data warehouse. A data engineer discovers that the compression encoding applied to some of the tables is not the best fit for the data.
The data engineer needs to improve the data encoding for the tables that have sub-optimal encoding.
Which solution will meet this requirement?

A. Run the ANALYZE COMPRESSION command against the identified tables. Manually update the compression encoding of columns based on the output of the command.
B. Run the VACUUM RECLUSTER command against the identified tables.
C. Run the VACUUM REINDEX command against the identified tables.
D. Run the ANALYZE command against the identified tables. Manually update the compression encoding of columns based on the output of the command.

Answer: A

Explanation:
To improve data encoding for Amazon Redshift tables where sub-optimal encoding has been applied, the correct approach is to analyze the table to determine the optimal encoding based on the data distribution and characteristics.
Option B: Run the ANALYZE COMPRESSION command against the identified tables. Manually update the compression encoding of columns based on the output of the command.The ANALYZE COMPRESSION command in Amazon Redshift analyzes the columnar data and suggests the best compression encoding for each column. The output provides recommendations for changing the current encoding to improve storage efficiency and query performance. After analyzing, you can manually apply the recommended encoding to the columns.
Option A (ANALYZE command) is incorrect because it is primarily used to update statistics on tables, not to analyze or suggest compression encoding.
Options C and D (VACUUM commands) deal with reclaiming disk space and reorganizing data, not optimizing compression encoding.
References:
Amazon Redshift ANALYZE COMPRESSION Command

NEW QUESTION # 55
A retail company stores customer data in an Amazon S3 bucket. Some of the customer data contains personally identifiable information (PII) about customers. The company must not share PII data with business partners.
A data engineer must determine whether a dataset contains PII before making objects in the dataset available to business partners.
Which solution will meet this requirement with the LEAST manual intervention?

A. Configure AWS CloudTrail to monitor S3 PUT operations. Inspect the CloudTrail trails to identify operations that save PII.
B. Configure the S3 bucket and S3 objects to allow access to Amazon Macie. Use automated sensitive data discovery in Macie.
C. Create a table in AWS Glue Data Catalog. Write custom SQL queries to identify PII in the table. Use Amazon Athena to run the queries.
D. Create an AWS Lambda function to identify PII in S3 objects. Schedule the function to run periodically.

Answer: B

Explanation:
Amazon Macie is a fully managed data security and privacy service that uses machine learning to automatically discover, classify, and protect sensitive data in AWS, such as PII. By configuring Macie for automated sensitive data discovery, the company can minimize manual intervention while ensuring PII is identified before data is shared.

NEW QUESTION # 56
A company stores a 100 MB dataset in an Amazon S3 bucket as an Apache Parquet file. A data engineer needs to profile the data before performing data preparation steps on the data.
Which solution will meet this requirement in the MOST operationally efficient way?

A. Load the data into an Amazon QuickSight dataset. Build a topic to profile the data with questions.
B. Stream the data into Amazon Managed Service for Apache Flink for SQL queries. Use the Apache Flink dashboard to profile the data.
C. Ingest the data into Amazon Redshift Spectrum. Use SQL queries to profile the data.
D. Create a profile job on the dataset in AWS Glue DataBrew. Review the profile job results.

Answer: D

Explanation:
AWS Glue DataBrew is purpose-built for data profiling and data preparation with minimal operational overhead. A DataBrew profile job automatically analyzes datasets stored in Amazon S3 and generates detailed statistics such as column completeness, distinct values, data types, min/max values, and potential data quality issues. This is exactly what is required before data preparation steps.
The dataset size (100 MB) and Parquet format are fully supported by DataBrew, and no infrastructure provisioning, cluster management, or SQL development is required. The profiling results are produced automatically and can be reviewed directly in the AWS Management Console, making this the most efficient and lowest-effort solution.
Amazon Managed Service for Apache Flink is designed for real-time streaming analytics and introduces unnecessary complexity. Amazon Redshift Spectrum requires schema definition, SQL development, and additional setup, which is excessive for simple profiling. Amazon QuickSight is a visualization and BI service, not a data profiling tool.
Therefore, creating a DataBrew profile job is the most operationally efficient and exam-aligned solution.

NEW QUESTION # 57
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