Quiz 2024 Professional Data-Engineer-Associate: AWS Certified Data Engineer

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Amazon AWS Certified Data Engineer - Associate (DEA-C01) Sample Questions (Q95-Q100):

NEW QUESTION # 95
A data engineer needs to use an Amazon QuickSight dashboard that is based on Amazon Athena queries on data that is stored in an Amazon S3 bucket. When the data engineer connects to the QuickSight dashboard, the data engineer receives an error message that indicates insufficient permissions.
Which factors could cause to the permissions-related errors? (Choose two.)

A. The Athena tables are not cataloged.
B. QuickSiqht does not have access to the S3 bucket.
C. There is no connection between QuickSgqht and Athena.
D. QuickSight does not have access to decrypt S3 data.
E. There is no 1AM role assigned to QuickSiqht.

Answer: B,D

Explanation:
QuickSight does not have access to the S3 bucket and QuickSight does not have access to decrypt S3 data are two possible factors that could cause the permissions-related errors. Amazon QuickSight is a business intelligence service that allows you to create and share interactive dashboards based on various data sources, including Amazon Athena. Amazon Athena is a serverless query service that allows you to analyze data stored in Amazon S3 using standard SQL. To use an Amazon QuickSight dashboard that is based on Amazon Athena queries on data that is stored in an Amazon S3 bucket, you need to grant QuickSight access to both Athena and S3, as well as any encryption keys that are used to encrypt the S3 data. If QuickSight does not have access to the S3 bucket or the encryption keys, it will not be able to read the data from Athena and display it on the dashboard, resulting in an error message that indicates insufficient permissions.
The other options are not factors that could cause the permissions-related errors. Option A, there is no connection between QuickSight and Athena, is not a factor, as QuickSight supports Athena as a native data source, and you can easily create a connection between them using the QuickSight console or the API. Option B, the Athena tables are not cataloged, is not a factor, as QuickSight can automatically discover the Athena tables that are cataloged in the AWS Glue Data Catalog, and you can also manually specify the Athena tables that are not cataloged. Option E, there is no IAM role assigned to QuickSight, is not a factor, as QuickSight requires an IAM role to access any AWS data sources, including Athena and S3, and you can create and assign an IAM role to QuickSight using the QuickSight console or the API. References:
Using Amazon Athena as a Data Source
Granting Amazon QuickSight Access to AWS Resources
Encrypting Data at Rest in Amazon S3

NEW QUESTION # 96
A company's data engineer needs to optimize the performance of table SQL queries. The company stores data in an Amazon Redshift cluster. The data engineer cannot increase the size of the cluster because of budget constraints.
The company stores the data in multiple tables and loads the data by using the EVEN distribution style. Some tables are hundreds of gigabytes in size. Other tables are less than 10 MB in size.
Which solution will meet these requirements?

A. Specify a combination of distribution, sort, and partition keys for all tables.
B. Use the ALL distribution style for rarely updated small tables. Specify primary and foreign keys for all tables.
C. Keep using the EVEN distribution style for all tables. Specify primary and foreign keys for all tables.
D. Use the ALL distribution style for large tables. Specify primary and foreign keys for all tables.

Answer: A

Explanation:
This solution meets the requirements of optimizing the performance of table SQL queries without increasing the size of the cluster. By using the ALL distribution style for rarely updated small tables, you can ensure that the entire table is copied to every node in the cluster, which eliminates the need for data redistribution during joins. This can improve query performance significantly, especially for frequently joined dimension tables.
However, using the ALL distribution style also increases the storage space and the load time, so it is only suitable for small tables that are not updated frequently or extensively. By specifying primary and foreign keys for all tables, you can help the query optimizer to generate better query plans and avoid unnecessary scans or joins. You can also use the AUTO distribution style to let Amazon Redshift choose the optimal distribution style based on the table size and the query patterns. References:
* Choose the best distribution style
* Distribution styles
* Working with data distribution styles

NEW QUESTION # 97
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. Share the Amazon Redshift data share to the Lake Formation catalog in the governance account.
B. Create an Amazon Redshift managed VPC endpoint in the marketing team's account. Grant the marketing team access to the views.
C. Create an Amazon Redshift data than that includes the tables that need to be shared.
D. Share the Amazon Redshift data share to the Amazon Redshift Serverless workgroup in the marketing team's account.
E. Create views of the tables that need to be shared. Include only the required columns.

Answer: D,E

Explanation:
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.
References:
* Amazon Redshift Data Sharing
* AWS Lake Formation Documentation

NEW QUESTION # 98
A data engineer needs to join data from multiple sources to perform a one-time analysis job. The data is stored in Amazon DynamoDB, Amazon RDS, Amazon Redshift, and Amazon S3.
Which solution will meet this requirement MOST cost-effectively?

A. Use Redshift Spectrum to query data from DynamoDB, Amazon RDS, and Amazon S3 directly from Redshift.
B. Use Amazon Athena Federated Query to join the data from all data sources.
C. Copy the data from DynamoDB, Amazon RDS, and Amazon Redshift into Amazon S3. Run Amazon Athena queries directly on the S3 files.
D. Use an Amazon EMR provisioned cluster to read from all sources. Use Apache Spark to join the data and perform the analysis.

Answer: B

Explanation:
Amazon Athena Federated Query is a feature that allows you to query data from multiple sources using standard SQL. You can use Athena Federated Query to join data from Amazon DynamoDB, Amazon RDS, Amazon Redshift, and Amazon S3, as well as other data sources such as MongoDB, Apache HBase, and Apache Kafka1. Athena Federated Query is a serverless and interactive service, meaning you do not need to provision or manage any infrastructure, and you only pay for the amount of data scanned by your queries.
Athena Federated Query is the most cost-effective solution for performing a one-time analysis job on data from multiple sources, as it eliminates the need to copy or move data, and allows you to query data directly from the source.
The other options are not as cost-effective as Athena Federated Query, as they involve additional steps or costs. Option A requires you to provision and pay for an Amazon EMR cluster, which can be expensive and time-consuming for a one-time job. Option B requires you to copy or move data from DynamoDB, RDS, and Redshift to S3, which can incur additional costs for data transfer and storage, and also introduce latency and complexity. Option D requires you to have an existing Redshift cluster, which can be costly and may not be necessary for a one-time job. Option D also does not supportquerying data from RDS directly, so you would need to use Redshift Federated Query to access RDS data, which adds another layer of complexity2.
References:
Amazon Athena Federated Query
Redshift Spectrum vs Federated Query

NEW QUESTION # 99
A data engineer needs to maintain a central metadata repository that users access through Amazon EMR and Amazon Athena queries. The repository needs to provide the schema and properties of many tables. Some of the metadata is stored in Apache Hive. The data engineer needs to import the metadata from Hive into the central metadata repository.
Which solution will meet these requirements with the LEAST development effort?

A. Use a Hive metastore on an EMR cluster.
B. Use a metastore on an Amazon RDS for MySQL DB instance.
C. Use the AWS Glue Data Catalog.
D. Use Amazon EMR and Apache Ranger.

Answer: C

Explanation:
The AWS Glue Data Catalog is an Apache Hive metastore-compatible catalog that provides a central metadata repository for various data sources and formats. You can use the AWS Glue Data Catalog as an external Hive metastore for Amazon EMR and Amazon Athena queries, and import metadata from existing Hive metastores into the Data Catalog. This solution requires the least development effort, as you can use AWS Glue crawlers to automatically discover and catalog the metadata from Hive, and use the AWS Glue console, AWS CLI, or Amazon EMR API to configure the Data Catalog as the Hive metastore. The other options are either more complex or require additional steps, such as setting up Apache Ranger for security, managing a Hive metastore on an EMR cluster or an RDS instance, or migrating the metadata manually.
References:
* Using the AWS Glue Data Catalog as the metastore for Hive (Section: Specifying AWS Glue Data Catalog as the metastore)
* Metadata Management: Hive Metastore vs AWS Glue (Section: AWS Glue Data Catalog)
* AWS Glue Data Catalog support for Spark SQL jobs (Section: Importing metadata from an existing Hive metastore)
* AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide (Chapter 5, page 131)

NEW QUESTION # 100
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