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

1. A company runs a data pipeline that uses AWS Step Functions to orchestrate AWS Lambda functions and AWS Glue jobs. The Lambda functions and AWS Glue jobs require access to multiple Amazon RDS databases. The Lambda functions and AWS Glue jobs already have access to the VPC that hosts the RDS databases.
Which solution will meet these requirements in the MOST secure way?

A) Use the root user of the company's AWS account to create long-term access keys for the RDS databases. Include the access keys programmatically in the Lambda functions and AWS Glue jobs.
Generate new keys every 90 days.
B) Create an IAM role that has permissions to access the RDS databases. Create a second IAM role for the Lambda functions and AWS Glue jobs that has permissions to assume the IAM role that has access permissions for the RDS databases.
C) Create Java Database Connectivity (JDBC) connections between the Lambda functions and AWS Glue jobs and the RDS databases. In the connection string, include the necessary credentials.
D) Create an IAM user that can assume IAM roles that have permissions and credentials to access the RDS databases. Assign the IAM user to each of the Lambda functions and AWS Glue jobs.

2. A company runs concurrent analytical queries on Amazon Redshift tables multiple times each day. The queries require consistent data views three times each day. The company runs extract, transform, and load (ETL) operations that update dimension tables while the queries run. The company has noticed that the queries cause table-level locks during the ETL operations. The company ' s current solution experiences query timeouts and deadlocks during peak processing hours, which affects analytical reporting and on-demand analysis.
Which solution will fix this issue?

A) Deploy separate Amazon Redshift clusters for ETL and analytics workloads. Use cross-database queries and data sharing to maintain data consistency.
B) Use Amazon Redshift materialized views for analytical queries. Schedule ETL operations during off- peak hours to minimize lock contention.
C) Configure Amazon Redshift federated queries to access source data directly. Use read replicas to isolate analytical workloads from ETL operations.
D) Use Amazon Redshift Spectrum to query data in Amazon S3 for analytical workloads. Maintain ETL operations on Amazon Redshift tables with transaction isolation.

3. A company needs to generate a one-time performance report by joining data that is stored in Amazon DynamoDB, Amazon RDS, Amazon Redshift, and Amazon S3. The company wants to avoid unnecessary data movement and to minimize query execution time.
Which solution will meet these requirements?

A) Use Amazon Athena Federated Query to perform one-time joins and analysis across DynamoDB, Amazon RDS, Amazon Redshift, and Amazon S3.
B) Capture data from DynamoDB by using DynamoDB Streams. Migrate data from Amazon RDS by using AWS DMS. Export Amazon Redshift data. Store all data in Amazon S3. Use Redshift Spectrum to run queries.
C) Deploy an Amazon EMR cluster powered by Apache Spark to ingest, process, and merge datasets from multiple sources. Run analytical workloads on the merged data.
D) Set up an AWS Glue ETL pipeline to extract, transform, and centralize data in Amazon S3. Use Amazon Athena to run analytical queries.

4. A data engineer needs to deploy a complex pipeline. The stages of the pipeline must run scripts, but only fully managed and serverless services can be used.

A) Deploy AWS Glue jobs and workflows. Use AWS Glue to run the jobs and workflows on a schedule.
B) Use Amazon MWAA to build and schedule the pipeline.
C) Deploy the script to EC2. Use EventBridge to schedule it.
D) Use AWS Glue DataBrew and EventBridge to run on a schedule.

5. 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 invoke an AWS Lambda function that transforms the .csv files to JSON.Use Kinesis Data Firehose 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 convert the csv files to JSON and to store the files in Parquet format.

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