vuthanhdatt's Second Brain

Associate-DEA-C01_37

13 min read

Questions and Answers

Question LmO77wQjLuGBHtrjdhIX

Question

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.)

Choices

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

Question rK4gny3N266EtRyByDWy

Question

A company stores datasets in JSON format and .csv format in an Amazon S3 bucket. The company has Amazon RDS for Microsoft SQL Server databases, Amazon DynamoDB tables that are in provisioned capacity mode, and an Amazon Redshift cluster. A data engineering team must develop a solution that will give data scientists the ability to query all data sources by using syntax similar to SQL. Which solution will meet these requirements with the LEAST operational overhead?

Choices

  • A: Use AWS Glue to crawl the data sources. Store metadata in the AWS Glue Data Catalog. Use Amazon Athena to query the data. Use SQL for structured data sources. Use PartiQL for data that is stored in JSON format.
  • B: Use AWS Glue to crawl the data sources. Store metadata in the AWS Glue Data Catalog. Use Redshift Spectrum to query the data. Use SQL for structured data sources. Use PartiQL for data that is stored in JSON format.
  • C: Use AWS Glue to crawl the data sources. Store metadata in the AWS Glue Data Catalog. Use AWS Glue jobs to transform data that is in JSON format to Apache Parquet or .csv format. Store the transformed data in an S3 bucket. Use Amazon Athena to query the original and transformed data from the S3 bucket.
  • D: Use AWS Lake Formation to create a data lake. Use Lake Formation jobs to transform the data from all data sources to Apache Parquet format. Store the transformed data in an S3 bucket. Use Amazon Athena or Redshift Spectrum to query the data.

Question gIzFbiSZbzKrsajvabGn

Question

A data engineer is configuring Amazon SageMaker Studio to use AWS Glue interactive sessions to prepare data for machine learning (ML) models. The data engineer receives an access denied error when the data engineer tries to prepare the data by using SageMaker Studio. Which change should the engineer make to gain access to SageMaker Studio?

Choices

  • A: Add the AWSGlueServiceRole managed policy to the data engineer’s IAM user.
  • B: Add a policy to the data engineer’s IAM user that includes the sts:AssumeRole action for the AWS Glue and SageMaker service principals in the trust policy.
  • C: Add the AmazonSageMakerFullAccess managed policy to the data engineer’s IAM user.
  • D: Add a policy to the data engineer’s IAM user that allows the sts:AddAssociation action for the AWS Glue and SageMaker service principals in the trust policy.

Question 8np86vXgZkg0o7yhevKn

Question

A company extracts approximately 1 TB of data every day from data sources such as SAP HANA, Microsoft SQL Server, MongoDB, Apache Kafka, and Amazon DynamoDB. Some of the data sources have undefined data schemas or data schemas that change. A data engineer must implement a solution that can detect the schema for these data sources. The solution must extract, transform, and load the data to an Amazon S3 bucket. The company has a service level agreement (SLA) to load the data into the S3 bucket within 15 minutes of data creation. Which solution will meet these requirements with the LEAST operational overhead?

Choices

  • A: Use Amazon EMR to detect the schema and to extract, transform, and load the data into the S3 bucket. Create a pipeline in Apache Spark.
  • B: Use AWS Glue to detect the schema and to extract, transform, and load the data into the S3 bucket. Create a pipeline in Apache Spark.
  • C: Create a PySpark program in AWS Lambda to extract, transform, and load the data into the S3 bucket.
  • D: Create a stored procedure in Amazon Redshift to detect the schema and to extract, transform, and load the data into a Redshift Spectrum table. Access the table from Amazon S3.

Question aEXuTStHXOfOPr6jLSfB

Question

A company has multiple applications that use datasets that are stored in an Amazon S3 bucket. The company has an ecommerce application that generates a dataset that contains personally identifiable information (PII). The company has an internal analytics application that does not require access to the PII. To comply with regulations, the company must not share PII unnecessarily. A data engineer needs to implement a solution that with redact PII dynamically, based on the needs of each application that accesses the dataset. Which solution will meet the requirements with the LEAST operational overhead?

Choices

  • A: Create an S3 bucket policy to limit the access each application has. Create multiple copies of the dataset. Give each dataset copy the appropriate level of redaction for the needs of the application that accesses the copy.
  • B: Create an S3 Object Lambda endpoint. Use the S3 Object Lambda endpoint to read data from the S3 bucket. Implement redaction logic within an S3 Object Lambda function to dynamically redact PII based on the needs of each application that accesses the data.
  • C: Use AWS Glue to transform the data for each application. Create multiple copies of the dataset. Give each dataset copy the appropriate level of redaction for the needs of the application that accesses the copy.
  • D: Create an API Gateway endpoint that has custom authorizers. Use the API Gateway endpoint to read data from the S3 bucket. Initiate a REST API call to dynamically redact PII based on the needs of each application that accesses the data.

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