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

1. A data engineer is using an AWS Glue ETL job to remove outdated customer records from a table that contains customer account information. The data engineer is using the following SQL command to remove customers that exist in a table named monthly_accounts_update from the customer accounts table:
MERGE INTO accounts t USING monthly_accounts_update s ON t.customer = s.customer WHEN MATCHED THEN DELETE What will happen when the data engineer runs the SQL command?

A) Only customer records that are present in both tables will be retained in the customer accounts table.
B) All customer records that exist in both the customer accounts table and the monthly_accounts_update table will be deleted from the accounts table.
C) The table will be deleted.
D) No records will be deleted because the command syntax is not valid in AWS Glue.

2. A company uses AWS Glue Data Catalog to index data that is uploaded to an Amazon S3 bucket every day.
The company uses a daily batch processes in an extract, transform, and load (ETL) pipeline to upload data from external sources into the S3 bucket.
The company runs a daily report on the S3 data. Some days, the company runs the report before all the daily data has been uploaded to the S3 bucket. A data engineer must be able to send a message that identifies any incomplete data to an existing Amazon Simple Notification Service (Amazon SNS) topic.
Which solution will meet this requirement with the LEAST operational overhead?

A) Create data quality checks on the source datasets that the daily reports use. Create data quality actions by using AWS Glue workflows to confirm the completeness and consistency of the datasets. Configure the data quality actions to create an event in Amazon EventBridge if a dataset is incomplete. Configure EventBridge to send the event that informs the data engineer about the incomplete datasets to the Amazon SNS topic.
B) Create AWS Lambda functions that run data quality queries on the columns data type and the presence of null values. Orchestrate the ETL pipeline by using an AWS Step Functions workflow that runs the Lambda functions. Configure the Step Functions workflow to send an email notification that informs the data engineer about the incomplete datasets to the SNS topic.
C) Create data quality checks on the source datasets that the daily reports use. Create a new Amazon EMR cluster. Use Apache Spark SQL to create Apache Spark jobs in the EMR cluster that run data quality queries on the columns data type and the presence of null values. Orchestrate the ETL pipeline by using an AWS Step Functions workflow. Configure the workflow to send an email notification that informs the data engineer about the incomplete datasets to the SNS topic.
D) Create data quality checks for the source datasets that the daily reports use. Create a new AWS managed Apache Airflow cluster. Run the data quality checks by using Airflow tasks that run data quality queries on the columns data type and the presence of null values. Configure Airflow Directed Acyclic Graphs (DAGs) to send an email notification that informs the data engineer about the incomplete datasets to the SNS topic.

3. 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 AW5 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) 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.
B) Assign Elastic IP addresses to the QuickSight visualizations. Configure the QuickSight security group to allow inbound traffic on the Redshift port from the Elastic IP addresses.
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) Configure the Redshift security group to allow inbound traffic on the Redshift port from the QuickSight security group.

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

5. A data engineer uses Amazon Redshift to run resource-intensive analytics processes once every month. Every month, the data engineer creates a new Redshift provisioned cluster. The data engineer deletes the Redshift provisioned cluster after the analytics processes are complete every month. Before the data engineer deletes the cluster each month, the data engineer unloads backup data from the cluster to an Amazon S3 bucket.
The data engineer needs a solution to run the monthly analytics processes that does not require the data engineer to manage the infrastructure manually.
Which solution will meet these requirements with the LEAST operational overhead?

A) Use AWS CloudFormation templates to automatically process the analytics workload.
B) Use Amazon Step Functions to pause the Redshift cluster when the analytics processes are complete and to resume the cluster to run new processes every month.
C) Use the AWS CLI to automatically process the analytics workload.
D) Use Amazon Redshift Serverless to automatically process the analytics workload.

Solutions: