Data-Engineer-Associate Exam Dumps - AWS Certified Data Engineer - Associate (DEA-C01)

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Question # 17

A companyâ€™s data processing pipeline uses AWS Glue jobs and AWS Glue Data Catalog. All AWS Glue jobs must run in a custom VPC inside a private subnet. The company uses a NAT gateway to support outbound connections.

A data engineer needs to use AWS Glue to migrate data from an on-premises PostgreSQL database to Amazon S3. There is no current network connection between AWS and the on-premises environment. However, the data engineer has updated the on-premises database to allow traffic from the custom VPC.

Which solution will meet these requirements?

Create a JDBC connection in AWS Glue with the database JDBC URL, username, and password.

Create a Simple Authentication and Security Layer (SASL) connection in AWS Glue to the on-premises database.

Create a JDBC connection in AWS Glue with a security group that allows TCP traffic to and from itself.

Create a JDBC connection in AWS Glue that uses a JDBC driver stored in Amazon S3. Retrieve the database URL, username, and password from AWS Secrets Manager.

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Question # 18

A data engineer needs to use Amazon Neptune to develop graph applications.

Which programming languages should the engineer use to develop the graph applications? (Select TWO.)

Gremlin

SQL

ANSI SQL

SPARQL

Spark SQL

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Question # 19

A company has a data lake in Amazon S3. The company collects AWS CloudTrail logs for multiple applications. The company stores the logs in the data lake, catalogs the logs in AWS Glue, and partitions the logs based on the year. The company uses Amazon Athena to analyze the logs.

Recently, customers reported that a query on one of the Athena tables did not return any data. A data engineer must resolve the issue.

Which combination of troubleshooting steps should the data engineer take? (Select TWO.)

Confirm that Athena is pointing to the correct Amazon S3 location.

Increase the query timeout duration.

Use the MSCK REPAIR TABLE command.

Restart Athena.

Delete and recreate the problematic Athena table.

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Question # 20

A company has a data processing pipeline that includes several dozen steps. The data processing pipeline needs to send alerts in real time when a step fails or succeeds. The data processing pipeline uses a combination of Amazon S3 buckets, AWS Lambda functions, and AWS Step Functions state machines.

A data engineer needs to create a solution to monitor the entire pipeline.

Which solution will meet these requirements?

Configure the Step Functions state machines to store notifications in an Amazon S3 bucket when the state machines finish running. Enable S3 event notifications on the S3 bucket.

Configure the AWS Lambda functions to store notifications in an Amazon S3 bucket when the state machines finish running. Enable S3 event notifications on the S3 bucket.

Use AWS CloudTrail to send a message to an Amazon Simple Notification Service (Amazon SNS) topic that sends notifications when a state machine fails to run or succeeds to run.

Configure an Amazon EventBridge rule to react when the execution status of a state machine changes. Configure the rule to send a message to an Amazon Simple Notification Service (Amazon SNS) topic that sends notifications.

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Question # 21

A data engineer is using AWS Glue to build an extract, transform, and load (ETL) pipeline that processes streaming data from sensors. The pipeline sends the data to an Amazon S3 bucket in near real-time. The data engineer also needs to perform transformations and join the incoming data with metadata that is stored in an Amazon RDS for PostgreSQL database. The data engineer must write the results back to a second S3 bucket in Apache Parquet format.

Which solution will meet these requirements?

Use an AWS Glue streaming job and AWS Glue Studio to perform the transformations and to write the data in Parquet format.

Use AWS Glue jobs and AWS Glue Data Catalog to catalog the data from Amazon S3 and Amazon RDS. Configure the jobs to perform the transformations and joins and to write the output in Parquet format.

Use an AWS Glue interactive session to process the streaming data and to join the data with the RDS database.

Use an AWS Glue Python shell job to run a Python script that processes the data in batches. Keep track of processed files by using AWS Glue bookmarks.

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Question # 22

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

Use AWS Database Migration Service (AWS DMS) Schema Conversion to migrate the schema. Use AWS DMS to migrate the data.

Use the AWS Schema Conversion Tool (AWS SCT) to migrate the schema. Use AWS Database Migration Service (AWS DMS) to migrate the data.

Use AWS Database Migration Service (AWS DMS) to migrate the data. Use automatic schema conversion.

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.

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Question # 23

A company is developing machine learning (ML) models. A data engineer needs to apply data quality rules to training data. The company stores the training data in an Amazon S3 bucket.

Create an AWS Lambda function to check data quality and to raise exceptions in the code.

Create an AWS Glue DataBrew project for the data in the S3 bucket. Create a ruleset for the data quality rules. Create a profile job to run the data quality rules. Use Amazon EventBridge to run the profile job when data is added to the S3 bucket.

Create an Amazon EMR provisioned cluster. Add a Python data quality package.

Create AWS Lambda functions to evaluate data quality rules and orchestrate with AWS Step Functions.

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Question # 24

A data engineer is designing a log table for an application that requires continuous ingestion. The application must provide dependable API-based access to specific records from other applications. The application must handle more than 4,000 concurrent write operations and 6,500 read operations every second.

Create an Amazon Redshift table with the KEY distribution style. Use the Amazon Redshift Data API to perform all read and write operations.

Store the log files in an Amazon S3 Standard bucket. Register the schema in AWS Glue Data Catalog. Create an external Redshift table that points to the AWS Glue schema. Use the table to perform Amazon Redshift Spectrum read operations.

Create an Amazon Redshift table with the EVEN distribution style. Use the Amazon Redshift JDBC connector to establish a database connection. Use the database connection to perform all read and write operations.

Create an Amazon DynamoDB table that has provisioned capacity to meet the application's capacity needs. Use the DynamoDB table to perform all read and write operations by using DynamoDB APIs.

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