Amazon DATA-ENGINEER-ASSOCIATE Online Practice Questions and Exam Preparation

Amazon DATA-ENGINEER-ASSOCIATE Online Questions & Answers

• Question 71:

  A company plans to use Amazon Kinesis Data Firehose to store data in Amazon S3. The source data consists of 2 MB csv files. The company must convert the .csv files to JSON format. The company must store the files in Apache Parquet format.

  Which solution will meet these requirements with the LEAST development effort?

  A. Use Kinesis Data Firehose to convert the csv files to JSON. Use an AWS Lambda function to store the files in Parquet format.
  B. Use Kinesis Data Firehose to convert the csv files to JSON and to store the files in Parquet format.
  C. Use Kinesis Data Firehose to invoke an AWS Lambda function that transforms the .csv files to JSON and stores the files in Parquet format.
  D. Use Kinesis Data Firehose to invoke an AWS Lambda function that transforms the .csv files to JSON. Use Kinesis Data Firehose to store the files in Parquet format.
  D. Use Kinesis Data Firehose to invoke an AWS Lambda function that transforms the .csv files to JSON. Use Kinesis Data Firehose to store the files in Parquet format.

• Question 72:

  A company needs to use an Aws Glue PySpark job to read specific data from an Amazon DynamoDB table. The company knows the partition key values for the required records. The existing processing logic of the AWS Glue PySpark job requires the data to be in DynamicFrame format. The company needs a solution to ensure that the job reads only the specified data.

  Which solution will meet this requirement with the MINIMUM number of read capacity units (RCUs)?

  A. Use the AWS Glue DynamoDB ETL connector to read the DynamoDB table. Use the filter option to read the required partition key.
  B. Perform a query on the DynamoDB table in the AWS Glue job by using only the sort key in the key condition expression. Load the data into a DynamicFrame.
  C. Perform a scan on the DynamoDB table in the Aws Glue job. Put the data into a DynamicFrame. Filter the DynamicFrame on the partition key.
  D. Perform a query on the DynamoDB table in the AWS Glue job. Use the partition key in the key condition expression. Put the data into a DynamicFrame.
  D. Perform a query on the DynamoDB table in the AWS Glue job. Use the partition key in the key condition expression. Put the data into a DynamicFrame.

• Question 73:

  A company uses Amazon Redshift for its data warehouse. The company must automate refresh schedules for Amazon Redshift materialized views.

  Which solution will meet this requirement with the LEAST effort?

  A. Use Apache Airflow to refresh the materialized views.
  B. Use an AWS Lambda user-defined function (UDF) within Amazon Redshift to refresh the materialized views.
  C. Use the query editor v2 in Amazon Redshift to refresh the materialized views.
  D. Use an AWS Glue workflow to refresh the materialized views.
  C. Use the query editor v2 in Amazon Redshift to refresh the materialized views.

• Question 74:

  Two developers are working on separate application releases. The developers have created feature branches named Branch A and Branch B by using a GitHub repository's master branch as the source.

  The developer for Branch A deployed code to the production system. The code for Branch B will merge into a master branch in the following week's scheduled application release.

  Which command should the developer for Branch B run before the developer raises a pull request to the master branch?

  A. git diff branchB master git commit -m
  B. git pull master
  C. git rebase master
  D. git fetch -b master
  C. git rebase master

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  Explanation

  To ensure that Branch B is up to date with the latest changes in the master branch before submitting a pull request, the correct approach is to perform a git rebase. This command rewrites the commit history so that Branch B will be based on the latest changes in the master branch. git rebase master: This command moves the commits of Branch B to be based on top of the latest state of the master branch.

  It allows the developer to resolve any conflicts and create a clean history.

• Question 75:

  A company needs to transform IoT sensor data in near real time before the company stores the data in an Amazon S3 bucket. The data is available from a data stream in Amazon Kinesis Data Streams.

  The company needs to apply complex and stateful transformations to the data before the company stores the data.

  Which solution will meet these requirements with the LEAST operational overhead?

  A. Schedule AWS Glue ETL jobs to process the data stream.
  B. Configure an application in Amazon Managed Service for Apache Flink to process the data stream.
  C. Configure an AWS Lambda function to process the data stream.
  D. Schedule Apache Spark jobs on an Amazon EMR cluster to process the data stream.
  B. Configure an application in Amazon Managed Service for Apache Flink to process the data stream.

• Question 76:

  A data engineer notices slow query performance on a highly partitioned table that is in Amazon Athena.

  The table contains daily data for the previous 5 years, partitioned by date. The data engineer wants to improve query performance and to automate partition management.

  Which solution will meet these requirements?

  A. Use an AWS Lambda function that runs daily. Configure the function to manually create new partitions in AWS Glue for each day's data.
  B. Use partition projection in Athena. Configure the table properties by using a date range from 5 years ago to the present.
  C. Reduce the number of partitions by changing the partitioning schema from daily to monthly granularity.
  D. Increase the proceing capacity of Athena queries by allocating more compute resources.
  B. Use partition projection in Athena. Configure the table properties by using a date range from 5 years ago to the present.

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  Explanation

  Partition projection eliminates the need for frequent metadata lookups or explicit partition registration in the AWS Glue Data Catalog. By defining a date range in your table's properties (for example, projection.enabled=true, projection.order_date.type=DATE, projection.order_date.range=2020/01/01,NOW, and the appropriate format), Athena will calculate partitions on-the-fly. This both speeds up queries (avoiding catalog calls for thousands of partitions) and removes the need for any external partition-management jobs.

• Question 77:

  A company uses an on-premises Microsoft SQL Server database to store financial transaction data. The company migrates the transaction data from the on-premises database to AWS at the end of each month.

  The company has noticed that the cost to migrate data from the on-premises database to an Amazon RDS for SQL Server database has increased recently.

  The company requires a cost-effective solution to migrate the data to AWS. The solution must cause minimal downtown for the applications that access the database.

  Which AWS service should the company use to meet these requirements?

  A. AWS Lambda
  B. AWS Database Migration Service (AWS DMS)
  C. AWS Direct Connect
  D. AWS DataSync
  B. AWS Database Migration Service (AWS DMS)

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  Explanation

  AWS Database Migration Service (AWS DMS) is a cloud service that makes it possible to migrate relational databases, data warehouses, NoSQL databases, and other types of data stores to AWS quickly, securely, and with minimal downtime and zero data loss. AWS DMS supports migration between 20-plus database and analytics engines, such as Microsoft SQL Server to Amazon RDS for SQL Server. AWS DMS takes over many of the difficult or tedious tasks involved in a migration project, such as capacity analysis, hardware and software procurement, installation and administration, testing and debugging, and ongoing replication and monitoring. AWS DMS is a cost-effective solution, as you only pay for the compute resources and additional log storage used during the migration process. AWS DMS is the best solution for the company to migrate the financial transaction data from the on-premises Microsoft SQL Server database to AWS, as it meets the requirements of minimal downtime, zero data loss, and low cost.

  Option A is not the best solution, as AWS Lambda is a serverless compute service that lets you run code without provisioning or managing servers, but it does not provide any built-in features for database migration. You would have to write your own code toextract, transform, and load the data from the source to the target, which would increase the operational overhead and complexity.

  Option C is not the best solution, as AWS Direct Connect is a service that establishes a dedicated network connection from your premises to AWS, but it does not provide any built-in features for database migration. You would still need to use another service or tool to perform the actual data transfer, which would increase the cost and complexity.

  Option D is not the best solution, as AWS DataSync is a service that makes it easy to transfer data between on-premises storage systems and AWS storage services, such as Amazon S3, Amazon EFS, and Amazon FSx for Windows File Server, but it does not support Amazon RDS for SQL Server as a target. You would have to use another service or tool to migrate the data from Amazon S3 to Amazon RDS for SQL Server, which would increase the latency and complexity.

  References:

  Database Migration - AWS Database Migration Service - AWS

  What is AWS Database Migration Service?

  AWS Database Migration Service Documentation

  AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide

• Question 78:

  A retail company stores transactions, store locations, and customer information tables in four reserved ra3.4xlarge Amazon Redshift cluster nodes. All three tables use even table distribution.

  The company updates the store location table only once or twice every few years.

  A data engineer notices that Redshift queues are slowing down because the whole store location table is constantly being broadcast to all four compute nodes for most queries. The data engineer wants to speed up the query performance by minimizing the broadcasting of the store location table.

  Which solution will meet these requirements in the MOST cost-effective way?

  A. Change the distribution style of the store location table from EVEN distribution to ALL distribution.
  B. Change the distribution style of the store location table to KEY distribution based on the column that has the highest dimension.
  C. Add a join column named store_id into the sort key for all the tables.
  D. Upgrade the Redshift reserved node to a larger instance size in the same instance family.
  A. Change the distribution style of the store location table from EVEN distribution to ALL distribution.

• Question 79:

  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?

  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.
  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.

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  Explanation

  This solution meets the requirement of gaining access to SageMaker Studio to use AWS Glue interactive sessions. AWS Glue interactive sessions are a way to use AWS Glue DataBrew and AWS Glue Data Catalog from within SageMaker Studio. To use AWS Glue interactive sessions, the data engineer's IAM user needs to have permissions to assume the AWS Glue service role and the SageMaker execution role.

  By adding 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, the data engineer can grant these permissions and avoid the access denied error. The other options are not sufficient or necessary to resolve the error.

  References:

  Get started with data integration from Amazon S3 to Amazon Redshift using AWS Glue interactive sessions Troubleshoot Errors - Amazon SageMaker AccessDeniedException on sagemaker:CreateDomain in AWS SageMaker Studio, despite having SageMakerFullAccess

• Question 80:

  A company manages an Amazon Redshift data warehouse. The data warehouse is in a public subnet inside a custom VPC. A security group allows only traffic from within itself. An ACL is open to all traffic.

  The company wants to generate several visualizations in Amazon QuickSight for an upcoming sales event.

  The company will run QuickSight Enterprise edition in a second AWS account inside a public subnet within a second custom VPC.

  The new public subnet has a security group that allows outbound traffic to the existing Redshift cluster.

  A data engineer needs to establish connections between Amazon Redshift and QuickSight. QuickSight must refresh dashboards by querying the Redshift cluster.

  Which solution will meet these requirements?

  A. Configure the Redshift security group to allow inbound traffic on the Redshift port from the QuickSight security group.
  B. Aign Elastic IP addrees to the QuickSight visualizations. Configure the QuickSight security group to allow inbound traffic on the Redshift port from the Elastic IP addrees.
  C. Confirm that the CIDR ranges of the Redshift VPC and the QuickSight VPC are the same. If CIDR ranges are different, reconfigure one CIDR range to match the other. Establish network peering between the VPCs.
  D. Create a QuickSight gateway endpoint in the Redshift VPC. Attach an endpoint policy to the gateway endpoint to ensure only specific QuickSight accounts can use the endpoint.
  A. Configure the Redshift security group to allow inbound traffic on the Redshift port from the QuickSight security group.

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  Explanation

  Because your Redshift cluster is publicly acceible in its VPC and QuickSight runs in a separate VPC and account, you simply need to allow QuickSight's outbound connections through the Redshift security group.

  By adding an inbound rule on the Redshift SG that permits traffic on port 5439 (or your cluster's port) from the QuickSight security group, QuickSight can refresh dashboards without any additional infrastructure changes.