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

1. A telecommunications company collects network usage data throughout each day at a rate of several thousand data points each second. The company runs an application to process the usage data in real time. The company aggregates and stores the data in an Amazon Aurora DB instance.
Sudden drops in network usage usually indicate a network outage. The company must be able to identify sudden drops in network usage so the company can take immediate remedial actions.
Which solution will meet this requirement with the LEAST latency?

A) Create an AWS Lambda function to query Aurora for drops in network usage. Use Amazon EventBridge to automatically invoke the Lambda function every minute.
B) Modify the processing application to publish the data to an Amazon Kinesis data stream. Create an Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) application to detect drops in network usage.
C) Replace the Aurora database with an Amazon DynamoDB table. Create an AWS Lambda function to query the DynamoDB table for drops in network usage every minute. Use DynamoDB Accelerator (DAX) between the processing application and DynamoDB table.
D) Create an AWS Lambda function within the Database Activity Streams feature of Aurora to detect drops in network usage.

2. A retail company is expanding its operations globally. The company needs to use Amazon QuickSight to accurately calculate currency exchange rates for financial reports. The company has an existing dashboard that includes a visual that is based on an analysis of a dataset that contains global currency values and exchange rates.
A data engineer needs to ensure that exchange rates are calculated with a precision of four decimal places.
The calculations must be precomputed. The data engineer must materialize results in QuickSight super-fast, parallel, in-memory calculation engine (SPICE).
Which solution will meet these requirements?

A) Define and create the calculated field in the dataset.
B) Define and create the calculated field in the dashboard.
C) Define and create the calculated field in the analysis.
D) Define and create the calculated field in the visual.

3. A company uses Amazon Redshift for its data warehouse. A data engineer must query a table named orders.
complete_orders_history, which contains 100 columns. The query must return all columns except columns named company_id and unique_system_id.
Which Amazon Redshift SQL statement will meet this requirement?

A) SELECT * EXCLUDE company_id, unique_system_idFROM orders.complete_orders_history;
B) SELECT * NOT IN company_id, unique_system_idFROM orders.complete_orders_history;
C) SELECT * EXCEPT company_id, unique_system_idFROM orders.complete_orders_history;
D) SELECT * TRUNCATE company_id, unique_system_idFROM orders.complete_orders_history;

4. A company needs to store semi-structured transactional data in a serverless database.
The application writes data infrequently but reads it frequently, with millisecond retrieval required.

A) Store the data in an Amazon RDS for MySQL cluster. Configure RDS Optimized Reads.
B) Store the data in an Amazon S3 Apache Iceberg table. Enable S3 Transfer Acceleration.
C) Store the data in an Amazon DynamoDB table. Configure a DynamoDB Accelerator (DAX) cache.
D) Store the data in an Amazon S3 Standard bucket. Enable S3 Transfer Acceleration.

5. A company stores historical customer data in an Amazon Redshift table. A column named Email contains null entries and values that are not email addresses. The quality of the Email column is critical for multiple downstream processes. A data engineer must create an AWS Glue Data Quality rule that fails when the percentage of valid email addresses in the Email column is less than 90%.
Which component of an AWS Glue Data Quality rule will meet these requirements?

A) UniqueValueRatio " Email " matches with a threshold set to > 0.1
B) Uniqueness " Email " matches with a threshold set to > 0.9
C) ColumnValues " Email " matches with a threshold set to > 0.9
D) ColumnValues " Email " matches with a threshold set to > 0.1

Solutions: