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Amazon Web Services DAS-C01 Exam With Confidence Using Practice Dumps

Exam Code:
DAS-C01
Exam Name:
AWS Certified Data Analytics - Specialty
Certification:
AWS Certified Data Analytics
Vendor:
Amazon Web Services
Questions:
207
Last Updated:
Apr 4, 2025
Exam Status:
Stable
Amazon Web Services DAS-C01

DAS-C01: AWS Certified Data Analytics Exam 2025 Study Guide Pdf and Test Engine

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AWS Certified Data Analytics - Specialty Questions and Answers

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Question 1

A company wants to enrich application logs in near-real-time and use the enriched dataset for further analysis. The application is running on Amazon EC2 instances across multiple Availability Zones and storing its logs using Amazon CloudWatch Logs. The enrichment source is stored in an Amazon DynamoDB table.

Which solution meets the requirements for the event collection and enrichment?

Options:

A.

Use a CloudWatch Logs subscription to send the data to Amazon Kinesis Data Firehose. Use AWS Lambda to transform the data in the Kinesis Data Firehose delivery stream and enrich it with the data in the DynamoDB table. Configure Amazon S3 as the Kinesis Data Firehose delivery destination.

B.

Export the raw logs to Amazon S3 on an hourly basis using the AWS CLI. Use AWS Glue crawlers to catalog the logs. Set up an AWS Glue connection for the DynamoDB table and set up an AWS Glue ETL job to enrich the data. Store the enriched data in Amazon S3.

C.

Configure the application to write the logs locally and use Amazon Kinesis Agent to send the data to Amazon Kinesis Data Streams. Configure a Kinesis Data Analytics SQL application with the Kinesis data stream as the source. Join the SQL application input stream with DynamoDB records, and then store the enriched output stream in Amazon S3 using Amazon Kinesis Data Firehose.

D.

Export the raw logs to Amazon S3 on an hourly basis using the AWS CLI. Use Apache Spark SQL on Amazon EMR to read the logs from Amazon S3 and enrich the records with the data from DynamoDB. Store the enriched data in Amazon S3.

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Question 2

A company has developed several AWS Glue jobs to validate and transform its data from Amazon S3 and load it into Amazon RDS for MySQL in batches once every day. The ETL jobs read the S3 data using a DynamicFrame. Currently, the ETL developers are experiencing challenges in processing only the incremental data on every run, as the AWS Glue job processes all the S3 input data on each run.

Which approach would allow the developers to solve the issue with minimal coding effort?

Options:

A.

Have the ETL jobs read the data from Amazon S3 using a DataFrame.

B.

Enable job bookmarks on the AWS Glue jobs.

C.

Create custom logic on the ETL jobs to track the processed S3 objects.

D.

Have the ETL jobs delete the processed objects or data from Amazon S3 after each run.

Question 3

A manufacturing company uses Amazon Connect to manage its contact center and Salesforce to manage its customer relationship management (CRM) data. The data engineering team must build a pipeline to ingest data from the contact center and CRM system into a data lake that is built on Amazon S3.

What is the MOST efficient way to collect data in the data lake with the LEAST operational overhead?

Options:

A.

Use Amazon Kinesis Data Streams to ingest Amazon Connect data and Amazon AppFlow to ingest Salesforce data.

B.

Use Amazon Kinesis Data Firehose to ingest Amazon Connect data and Amazon Kinesis Data Streams to ingest Salesforce data.

C.

Use Amazon Kinesis Data Firehose to ingest Amazon Connect data and Amazon AppFlow to ingest Salesforce data.

D.

Use Amazon AppFlow to ingest Amazon Connect data and Amazon Kinesis Data Firehose to ingest Salesforce data.

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