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

1. A company is designing a serverless data processing workflow in AWS Step Functions that involves multiple steps. The processing workflow ingests data from an external API, transforms the data by using multiple AWS Lambda functions, and loads the transformed data into Amazon DynamoDB.
The company needs the workflow to perform specific steps based on the content of the incoming data.
Which Step Functions state type should the company use to meet this requirement?

A) Map
B) Parallel
C) Task
D) Choice

2. A company has an on-premises PostgreSQL database that contains customer data. The company wants to migrate the customer data to an Amazon Redshift data warehouse. The company has established a VPN connection between the on-premises database and AWS.
The on-premises database is continuously updated. The company must ensure that the data in Amazon Redshift is updated as quickly as possible.
Which solution will meet these requirements?

A) Create an AWS Database Migration Service (AWS DMS) full-load task. Set Amazon Redshift as the target. Configure the task to use the change data capture (CDC) feature.
B) Use the pg_dump utility to generate a backup of the PostgreSQL database. Use the AWS Schema Conversion Tool (AWS SCT) to upload the backup to Amazon Redshift. Set up a cron job to perform a backup. Upload the backup to Amazon Redshift every night.
C) Create an AWS Database Migration Service (AWS DMS) full-load task. Set Amazon Redshift as the target. Configure the task to perform a full load of the database to Amazon Redshift every night.
D) Use the pg_dump utility to generate a backup of the PostgreSQL database. Upload the backup to an Amazon S3 bucket. Use the COPY command to import the data into Amazon Redshift.

3. 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?

A) 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.
B) 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.
C) Create a PvSpark proqram in AWS Lambda to extract, transform, and load the data into the S3 bucket.
D) 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.

4. 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.

5. 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?

A) Use an AWS Glue interactive session to process the streaming data and to join the data with the RDS database.
B) Use an AWS Glue streaming job and AWS Glue Studio to perform the transformations and to write the data in Parquet format.
C) 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.
D) 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.

Solutions: