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

1. A retail company stores data from a product lifecycle management (PLM) application in an on-premises MySQL database. The PLM application frequently updates the database when transactions occur.
The company wants to gather insights from the PLM application in near real time. The company wants to integrate the insights with other business datasets and to analyze the combined dataset by using an Amazon Redshift data warehouse.
The company has already established an AWS Direct Connect connection between the on-premises infrastructure and AWS.
Which solution will meet these requirements with the LEAST development effort?

A) Use the Amazon AppFlow SDK to build a custom connector for the MySQL database to continuously replicate the database changes. Set Amazon Redshift as the destination for the connector.
B) Run scheduled AWS DataSync tasks to synchronize data from the MySQL database. Set Amazon Redshift as the destination for the tasks.
C) Run a full load plus CDC task in AWS Database Migration Service (AWS DMS) to continuously replicate the MySQL database changes. Set Amazon Redshift as the destination for the task.
D) Run a scheduled AWS Glue extract, transform, and load (ETL) job to get the MySQL database updates by using a Java Database Connectivity (JDBC) connection. Set Amazon Redshift as the destination for the ETL job.

2. A banking company uses an application to collect large volumes of transactional data. The company uses Amazon Kinesis Data Streams for real-time analytics. The company's application uses the PutRecord action to send data to Kinesis Data Streams.
A data engineer has observed network outages during certain times of day. The data engineer wants to configure exactly-once delivery for the entire processing pipeline.
Which solution will meet this requirement?

A) Stop using Kinesis Data Streams. Use Amazon EMR instead. Use Apache Flink and Apache Spark Streaming in Amazon EMR.
B) Update the checkpoint configuration of the Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) data collection application to avoid duplicate processing of events.
C) Design the application so it can remove duplicates during processing by embedding a unique ID in each record at the source.
D) Design the data source so events are not ingested into Kinesis Data Streams multiple times.

3. A company receives call logs as Amazon S3 objects that contain sensitive customer information. The company must protect the S3 objects by using encryption. The company must also use encryption keys that only specific employees can access.
Which solution will meet these requirements with the LEAST effort?

A) Use server-side encryption with AWS KMS keys (SSE-KMS) to encrypt the objects that contain customer information. Configure an IAM policy that restricts access to the KMS keys that encrypt the objects.
B) Use server-side encryption with Amazon S3 managed keys (SSE-S3) to encrypt the objects that contain customer information. Configure an IAM policy that restricts access to the Amazon S3 managed keys that encrypt the objects.
C) Use server-side encryption with customer-provided keys (SSE-C) to encrypt the objects that contain customer information. Restrict access to the keys that encrypt the objects.
D) Use an AWS CloudHSM cluster to store the encryption keys. Configure the process that writes to Amazon S3 to make calls to CloudHSM to encrypt and decrypt the objects. Deploy an IAM policy that restricts access to the CloudHSM cluster.

4. A company has a data warehouse that contains a table that is named Sales. The company stores the table in Amazon Redshift The table includes a column that is named city_name. The company wants to query the table to find all rows that have a city_name that starts with "San" or "El." Which SQL query will meet this requirement?

A) Select * from Sales where city_name -, ^(San|EI) *';
B) Select * from Sales where city_name -, ^(San&EI)";
C) Select * from Sales where city_name - '$(San&EI)";
D) Select * from Sales where city_name - '$(San|EI)";

5. A company uses Amazon Redshift for its data warehouse. The company must automate refresh schedules for Amazon Redshift materialized views.
Which solution will meet this requirement with the LEAST effort?

A) Use an AWS Lambda user-defined function (UDF) within Amazon Redshift to refresh the materialized views.
B) Use the query editor v2 in Amazon Redshift to refresh the materialized views.
C) Use Apache Airflow to refresh the materialized views.
D) Use an AWS Glue workflow to refresh the materialized views.

Solutions: