Free Amazon AWS-DEA-C01 Exam Questions

Question 1

Your organization has deployed a series of IoT devices across its facilities to monitor environmental conditions. These devices send telemetry data every few seconds. As part of the data pipeline, you have been tasked with architecting a solution that ingests this streaming data, provides the ability to perform real-time analytics, and subsequently batches the data for storage in Amazon Redshift for further analysis. The solution should be scalable, manage large bursts of data effectively, and ensure that analytics can be performed promptly.

As a Cloud Data Engineering Consultant, which combination of AWS services would you employ to meet these requirements? (Select THREE)

A : Utilize Amazon DynamoDB to store and quickly retrieve data, which provides low latency access and flexibility to handle varying workloads.

B : Utilize Amazon Kinesis Data Firehose to batch and load the processed streaming data directly into Amazon Redshift.

C : Implement AWS Lambda functions to transform the data on the fly as it arrives on Kinesis Data Streams before sending it to Kinesis Data Firehose.

D : Deploy an Amazon Kinesis Data Streams application to collect streaming telematics data, enabling real-time analytics and quick response to data.

E : Configure Amazon QuickSight to connect to the Kinesis Data Stream for real-time analytics dashboards and visualizations.

F : Set up a scheduled Amazon Elastic MapReduce (EMR) job to process data, offering robust data transformation capabilities for complex analytics tasks.

Answer: B,C,D

Question 2

A telecommunications company collects network usage data throughout each day at a rate of several thousanddata points each second. The company runs an application to process the usage data in real time. Thecompany 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 identifysudden 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.

Answer: B

Question 3

A company wants to migrate a data warehouse from Teradata to Amazon Redshift. Which solution will meet this requirement with the LEAST operational effort?

A : Use AWS Database Migration Service (AWS DMS) Schema Conversion to migrate the schema. Use AWS DMS to migrate the data.

B : Use the AWS Schema Conversion Tool (AWS SCT) to migrate the schema. Use AWS Database Migration Service (AWS DMS) to migrate the data.

C : Use AWS Database Migration Service (AWS DMS) to migrate the data. Use automatic schema conversion.

D : Manually export the schema definition from Teradata. Apply the schema to the Amazon Redshift database. Use AWS Database Migration Service (AWS DMS) to migrate the data.

Answer: B

Question 4

A company has three subsidiaries. Each subsidiary uses a different data warehousing solution. The firstsubsidiary hosts its data warehouse in Amazon Redshift. The second subsidiary uses Teradata Vantage onAWS. The third subsidiary uses Google BigQuery.The company wants to aggregate all the data into a central Amazon S3 data lake. The company wants to useApache Iceberg as the table format.A data engineer needs to build a new pipeline to connect to all the data sources, run transformations by usingeach source engine, join the data, and write the data to Iceberg.Which solution will meet these requirements with the LEAST operational effort?

A : Use native Amazon Redshift, Teradata, and BigQuery connectors to build the pipeline in AWS Glue. Use native AWS Glue transforms to join the data. Run a Merge operation on the data lake Iceberg table.

B : Use the Amazon Athena federated query connectors for Amazon Redshift, Teradata, and BigQuery to build the pipeline in Athena. Write a SQL query to read from all the data sources, join the data, and run a Merge operation on the data lake Iceberg table

C : Use the native Amazon Redshift connector, the Java Database Connectivity (JDBC) connector for Teradata, and the open source Apache Spark BigQuery connector to build the pipeline in Amazon EMR. Write code in PySpark to join the data. Run a Merge operation on the data lake Iceberg table.

D : Use the native Amazon Redshift, Teradata, and BigQuery connectors in Amazon Appflow to write data to Amazon S3 and AWS Glue Data Catalog. Use Amazon Athena to join the data. Run a Merge operation on the data lake Iceberg table.

Answer: B

Question 5

A company wants to migrate a data warehouse from Teradata to Amazon Redshift. Which solution will meet this requirement with the LEAST operational effort?

A : Use AWS Database Migration Service (AWS DMS) Schema Conversion to migrate the schema. Use AWS DMS to migrate the data.

B : Use the AWS Schema Conversion Tool (AWS SCT) to migrate the schema. Use AWS Database Migration Service (AWS DMS) to migrate the data.

C : Use AWS Database Migration Service (AWS DMS) to migrate the data. Use automatic schema conversion.

D : Manually export the schema definition from Teradata. Apply the schema to the Amazon Redshift database. Use AWS Database Migration Service (AWS DMS) to migrate the data.

Answer: B