Data-Engineer-Associate Testfagen - Data-Engineer-Associate Prüfungsaufgaben

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Data-Engineer-Associate Prüfungsaufgaben, Data-Engineer-Associate Tests

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Amazon AWS Certified Data Engineer - Associate (DEA-C01) Data-Engineer-Associate Prüfungsfragen mit Lösungen (Q92-Q97):

92. Frage
A company implements a data mesh that has a central governance account. The company needs to catalog all data in the governance account. The governance account uses AWS Lake Formation to centrally share data and grant access permissions.
The company has created a new data product that includes a group of Amazon Redshift Serverless tables. A data engineer needs to share the data product with a marketing team. The marketing team must have access to only a subset of columns. The data engineer needs to share the same data product with a compliance team. The compliance team must have access to a different subset of columns than the marketing team needs access to.
Which combination of steps should the data engineer take to meet these requirements? (Select TWO.)

A. Create views of the tables that need to be shared. Include only the required columns.
B. Create an Amazon Redshift managed VPC endpoint in the marketing team's account. Grant the marketing team access to the views.
C. Share the Amazon Redshift data share to the Amazon Redshift Serverless workgroup in the marketing team's account.
D. Share the Amazon Redshift data share to the Lake Formation catalog in the governance account.
E. Create an Amazon Redshift data than that includes the tables that need to be shared.

Antwort: A,C

Begründung:
The company is using a data mesh architecture with AWS Lake Formation for governance and needs to share specific subsets of data with different teams (marketing and compliance) using Amazon Redshift Serverless.
Option A: Create views of the tables that need to be shared. Include only the required columns.
Creating views in Amazon Redshift that include only the necessary columns allows for fine-grained access control. This method ensures that each team has access to only the data they are authorized to view.
Option E: Share the Amazon Redshift data share to the Amazon Redshift Serverless workgroup in the marketing team's account.
Amazon Redshift data sharing enables live access to data across Redshift clusters or Serverless workgroups. By sharing data with specific workgroups, you can ensure that the marketing team and compliance team each access the relevant subset of data based on the views created.
Option B (creating a Redshift data share) is close but does not address the fine-grained column-level access.
Option C (creating a managed VPC endpoint) is unnecessary for sharing data with specific teams.
Option D (sharing with the Lake Formation catalog) is incorrect because Redshift data shares do not integrate directly with Lake Formation catalogs; they are specific to Redshift workgroups.
Reference:
Amazon Redshift Data Sharing
AWS Lake Formation Documentation

93. Frage
A company needs to set up a data catalog and metadata management for data sources that run in the AWS Cloud. The company will use the data catalog to maintain the metadata of all the objects that are in a set of data stores. The data stores include structured sources such as Amazon RDS and Amazon Redshift. The data stores also include semistructured sources such as JSON files and .xml files that are stored in Amazon S3.
The company needs a solution that will update the data catalog on a regular basis. The solution also must detect changes to the source metadata.
Which solution will meet these requirements with the LEAST operational overhead?

A. Use the AWS Glue Data Catalog as the central metadata repository. Use AWS Glue crawlers to connect to multiple data stores and to update the Data Catalog with metadata changes. Schedule the crawlers to run periodically to update the metadata catalog.
B. Use Amazon Aurora as the data catalog. Create AWS Lambda functions that will connect to the data catalog. Configure the Lambda functions to gather the metadata information from multiple sources and to update the Aurora data catalog. Schedule the Lambda functions to run periodically.
C. Use Amazon DynamoDB as the data catalog. Create AWS Lambda functions that will connect to the data catalog. Configure the Lambda functions to gather the metadata information from multiple sources and to update the DynamoDB data catalog. Schedule the Lambda functions to run periodically.
D. Use the AWS Glue Data Catalog as the central metadata repository. Extract the schema for Amazon RDS and Amazon Redshift sources, and build the Data Catalog. Use AWS Glue crawlers for data that is in Amazon S3 to infer the schema and to automatically update the Data Catalog.

Antwort: A

Begründung:
This solution will meet the requirements with the least operational overhead because it uses the AWS Glue Data Catalog as the central metadata repository for data sources that run in the AWS Cloud. The AWS Glue Data Catalog is a fully managed service that provides a unified view of your data assets across AWS and on-premises data sources. It stores the metadata of your data in tables, partitions, and columns, and enables you to access and query your data using various AWS services, such as Amazon Athena, Amazon EMR, and Amazon Redshift Spectrum. You can use AWS Glue crawlers to connect to multiple data stores, such as Amazon RDS, Amazon Redshift, and Amazon S3, and to update the Data Catalog with metadata changes.
AWS Glue crawlers can automatically discover the schema and partition structure of your data, and create or update the corresponding tables in the Data Catalog. You can schedule the crawlers to run periodically to update the metadata catalog, and configure them to detect changes to the source metadata, such as new columns, tables, or partitions12.
The other options are not optimal for the following reasons:
A: Use Amazon Aurora as the data catalog. Create AWS Lambda functions that will connect to the data catalog. Configure the Lambda functions to gather the metadata information from multiple sources and to update the Aurora data catalog. Schedule the Lambda functions to run periodically. This option is not recommended, as it would require more operational overhead to create and manage an Amazon Aurora database as the data catalog, and to write and maintain AWS Lambda functions to gather and update the metadata information from multiple sources. Moreover, this option would not leverage the benefits of the AWS Glue Data Catalog, such as data cataloging, data transformation, and data governance.
C: Use Amazon DynamoDB as the data catalog. Create AWS Lambda functions that will connect to the data catalog. Configure the Lambda functions to gather the metadata information from multiple sources and to update the DynamoDB data catalog. Schedule the Lambda functions to run periodically. This option is also not recommended, as it would require more operational overhead to create and manage an Amazon DynamoDB table as the data catalog, and to write and maintain AWS Lambda functions to gather and update the metadata information from multiple sources. Moreover, this option would not leverage the benefits of the AWS Glue Data Catalog, such as data cataloging, data transformation, and data governance.
D: Use the AWS Glue Data Catalog as the central metadata repository. Extract the schema for Amazon RDS and Amazon Redshift sources, and build the Data Catalog. Use AWS Glue crawlers for data that is in Amazon S3 to infer the schema and to automatically update the Data Catalog. This option is not optimal, as it would require more manual effort to extract the schema for Amazon RDS and Amazon Redshift sources, and to build the Data Catalog. This option would not take advantage of the AWS Glue crawlers' ability to automatically discover the schema and partition structure of your data from various data sources, and to create or update the corresponding tables in the Data Catalog.
References:
1: AWS Glue Data Catalog
2: AWS Glue Crawlers
3: Amazon Aurora
4: AWS Lambda
5: Amazon DynamoDB

94. Frage
A banking company uses an application to collect large volumes of transactional data. The company uses Amazon Kinesis Data Streams for real-time analytics. The company's application uses the PutRecord action to send data to Kinesis Data Streams.
A data engineer has observed network outages during certain times of day. The data engineer wants to configure exactly-once delivery for the entire processing pipeline.
Which solution will meet this requirement?

A. Design the application so it can remove duplicates during processing by embedding a unique ID in each record at the source.
B. Design the data source so events are not ingested into Kinesis Data Streams multiple times.
C. Update the checkpoint configuration of the Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) data collection application to avoid duplicate processing of events.
D. Stop using Kinesis Data Streams. Use Amazon EMR instead. Use Apache Flink and Apache Spark Streaming in Amazon EMR.

Antwort: C

Begründung:
For exactly-once delivery and processing in Amazon Kinesis Data Streams, the best approach is to design the application so that it handlesidempotency. By embedding aunique IDin each record, the application can identify and remove duplicate records during processing.
* Exactly-Once Processing:
* Kinesis Data Streams does not natively support exactly-once processing. Therefore,idempotency should be designed into the application, ensuring that each record has a unique identifier so that the same event is processed only once, even if it is ingested multiple times.
* This pattern is widely used for achieving exactly-once semantics in distributed systems.
Reference:Building Idempotent Applications with Kinesis
Alternatives Considered:
B (Checkpoint configuration): While updating the checkpoint configuration can help with some aspects of duplicate processing, it is not a full solution for exactly-once delivery.
C (Design data source): Ensuring events are not ingested multiple times is ideal, but network outages can make this difficult, and it doesn't guarantee exactly-once delivery.
D (Using EMR): While using EMR with Flink or Spark could work, it introduces unnecessary complexity compared to handling idempotency at the application level.
References:
Amazon Kinesis Best Practices for Exactly-Once Processing
Achieving Idempotency with Amazon Kinesis

95. Frage
A company wants to migrate an application and an on-premises Apache Kafka server to AWS. The application processes incremental updates that an on-premises Oracle database sends to the Kafka server. The company wants to use the replatform migration strategy instead of the refactor strategy.
Which solution will meet these requirements with the LEAST management overhead?

A. Amazon Managed Streaming for Apache Kafka (Amazon MSK) provisioned cluster
B. Amazon Data Firehose
C. Amazon Kinesis Data Streams
D. Amazon Managed Streaming for Apache Kafka (Amazon MSK) Serverless

Antwort: D

Begründung:
Problem Analysis:
The company needs to migrate both an application and an on-premises Apache Kafka server to AWS.
Incremental updates from an on-premises Oracle database are processed by Kafka.
The solution must follow a replatform migration strategy, prioritizing minimal changes and low management overhead.
Key Considerations:
Replatform Strategy: This approach keeps the application and architecture as close to the original as possible, reducing the need for refactoring.
The solution must provide a managed Kafka service to minimize operational burden.
Low overhead solutions like serverless services are preferred.
Solution Analysis:
Option A: Kinesis Data Streams
Kinesis Data Streams is an AWS-native streaming service but is not a direct substitute for Kafka.
This option would require significant application refactoring, which does not align with the replatform strategy.
Option B: MSK Provisioned Cluster
Managed Kafka service with fully configurable clusters.
Provides the same Kafka APIs but requires cluster management (e.g., scaling, patching), increasing management overhead.
Option C: Amazon Kinesis Data Firehose
Kinesis Data Firehose is designed for data delivery rather than real-time streaming and processing.
Not suitable for Kafka-based applications.
Option D: MSK Serverless
MSK Serverless eliminates the need for cluster management while maintaining compatibility with Kafka APIs.
Automatically scales based on workload, reducing operational overhead.
Ideal for replatform migrations, as it requires minimal changes to the application.
Final Recommendation:
Amazon MSK Serverless is the best solution for migrating the Kafka server and application with minimal changes and the least management overhead.
Reference:
Amazon MSK Serverless Overview
Comparison of Amazon MSK and Kinesis

96. Frage
A company wants to migrate a data warehouse from Teradata to Amazon Redshift. Which solution will meet this requirement with the LEAST operational effort?

A. Use AWS Database Migration Service (AWS DMS) to migrate the data. Use automatic schema conversion.
B. Use the AWS Schema Conversion Tool (AWS SCT) to migrate the schema. Use AWS Database Migration Service (AWS DMS) to migrate the data.
C. Manually export the schema definition from Teradata. Apply the schema to the Amazon Redshift database. Use AWS Database Migration Service (AWS DMS) to migrate the data.
D. Use AWS Database Migration Service (AWS DMS) Schema Conversion to migrate the schema. Use AWS DMS to migrate the data.

Antwort: B

97. Frage
......

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Data-Engineer-Associate Testfagen - Data-Engineer-Associate Prüfungsaufgaben

Data-Engineer-Associate Prüfungsaufgaben, Data-Engineer-Associate Tests

Amazon AWS Certified Data Engineer - Associate (DEA-C01) Data-Engineer-Associate Prüfungsfragen mit Lösungen (Q92-Q97):

Seit Neuem aktualisierte Data-Engineer-Associate Examfragen für Amazon Data-Engineer-Associate Prüfung

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