Free Practice Questions for Amazon-DEA-C01 Exam

A data engineer needs to onboard a new data producer into AWS. The data producer needs to migrate data products to AWS.The data producer maintains many data pipelines that support a business application. Each pipeline must have service accounts and their corresponding credentials. The data engineer must establish a secure connection from the data producer's on-premises data center to AWS. The data engineer must not use the public internet to transfer data from an on-premises data center to AWS.Which solution will meet these requirements?

• Instruct the new data producer to create Amazon Machine Images (AMIs) on Amazon Elastic Container Service (Amazon ECS) to store the code base of the application. Create security groups in a public subnet that allow connections only to the on-premises data center.
• Create an AWS Direct Connect connection to the on-premises data center. Store the service account credentials in AWS Secrets manager.
• Create a security group in a public subnet. Configure the security group to allow only connections from the CIDR blocks that correspond to the data producer. Create Amazon S3 buckets than contain presigned URLS that have one-day expiration dates.
• Create an AWS Direct Connect connection to the on-premises data center. Store the application keys in AWS Secrets Manager. Create Amazon S3 buckets that contain resigned URLS that have one- day expiration dates.

An airline company is collecting metrics about flight activities for analytics. The company is conducting a proof of concept (POC) test to show how analytics can provide insights that the company can use to increase on-time departures.The POC test uses objects in Amazon S3 that contain the metrics in .csv format. The POC test uses Amazon Athena to query the data. The data is partitioned in the S3 bucket by date.As the amount of data increases, the company wants to optimize the storage solution to improve query performance.Which combination of solutions will meet these requirements? (Choose two.)

• Add a randomized string to the beginning of the keys in Amazon S3 to get more throughput across partitions.
• Use an S3 bucket that is in the same account that uses Athena to query the data.
• Use an S3 bucket that is in the same AWS Region where the company runs Athena queries.
• Preprocess the .csv data to JSON format by fetching only the document keys that the query requires.
• Preprocess the .csv data to Apache Parquet format by fetching only the data blocks that are needed for predicates.

A company ingests data from multiple data sources and stores the data in an Amazon S3 bucket. An AWS Glue extract, transform, and load (ETL) job transforms the data and writes the transformed data to an Amazon S3 based data lake. The company uses Amazon Athena to query the data that is in the data lake.The company needs to identify matching records even when the records do not have a common unique identifier.Which solution will meet this requirement?

• Use Amazon Made pattern matching as part of the ETL job.
• Train and use the AWS Glue PySpark Filter class in the ETL job.
• Partition tables and use the ETL job to partition the data on a unique identifier.
• Train and use the AWS Lake Formation FindMatches transform in the ETL job.

A company is planning to migrate on-premises Apache Hadoop clusters to Amazon EMR. The company also needs to migrate a data catalog into a persistent storage solution.The company currently stores the data catalog in an on-premises Apache Hive metastore on the Hadoop clusters. The company requires a serverless solution to migrate the data catalog.Which solution will meet these requirements MOST cost-effectively?

• Use AWS Database Migration Service (AWS DMS) to migrate the Hive metastore into Amazon S3. Configure AWS Glue Data Catalog to scan Amazon S3 to produce the data catalog.
• Configure a Hive metastore in Amazon EMR. Migrate the existing on-premises Hive metastore into Amazon EMR. Use AWS Glue Data Catalog to store the company's data catalog as an external data catalog.
• Configure an external Hive metastore in Amazon EMR. Migrate the existing on-premises Hive metastore into Amazon EMR. Use Amazon Aurora MySQL to store the company's data catalog.
• Configure a new Hive metastore in Amazon EMR. Migrate the existing on-premises Hive metastore into Amazon EMR. Use the new metastore as the company's data catalog.

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.)

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

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?

• 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.
• 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.
• Create a PvSpark proqram in AWS Lambda to extract, transform, and load the data into the S3 bucket.
• 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.

A retail company has a customer data hub in an Amazon S3 bucket. Employees from many countries use the data hub to support company-wide analytics. A governance team must ensure that thecompany's data analysts can access data only for customers who are within the same country as the analysts.Which solution will meet these requirements with the LEAST operational effort?

• Create a separate table for each country's customer data. Provide access to each analyst based on the country that the analyst serves.
• Register the S3 bucket as a data lake location in AWS Lake Formation. Use the Lake Formation row- level security features to enforce the company's access policies.
• Move the data to AWS Regions that are close to the countries where the customers are. Provide access to each analyst based on the country that the analyst serves.
• Load the data into Amazon Redshift. Create a view for each country. Create separate 1AM roles for each country to provide access to data from each country. Assign the appropriate roles to the analysts.

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?

• AWS Lambda
• AWS Database Migration Service (AWS DMS)
• AWS Direct Connect
• AWS DataSync

A company maintains multiple extract, transform, and load (ETL) workflows that ingest data from the company's operational databases into an Amazon S3 based data lake. The ETL workflows use AWS Glue and Amazon EMR to process data.The company wants to improve the existing architecture to provide automated orchestration and to require minimal manual effort.Which solution will meet these requirements with the LEAST operational overhead?

• AWS Glue workflows
• AWS Step Functions tasks
• AWS Lambda functions
• Amazon Managed Workflows for Apache Airflow (Amazon MWAA) workflows

A data engineer must orchestrate a data pipeline that consists of one AWS Lambda function and one AWS Glue job. The solution must integrate with AWS services.Which solution will meet these requirements with the LEAST management overhead?

• Use an AWS Step Functions workflow that includes a state machine. Configure the state machine to run the Lambda function and then the AWS Glue job.
• Use an Apache Airflow workflow that is deployed on an Amazon EC2 instance. Define a directed acyclic graph (DAG) in which the first task is to call the Lambda function and the second task is to call the AWS Glue job.
• Use an AWS Glue workflow to run the Lambda function and then the AWS Glue job.
• Use an Apache Airflow workflow that is deployed on Amazon Elastic Kubernetes Service (Amazon EKS). Define a directed acyclic graph (DAG) in which the first task is to call the Lambda function and the second task is to call the AWS Glue job.