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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 30, 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

An online gaming company is using an Amazon Kinesis Data Analytics SQL application with a Kinesis data stream as its source. The source sends three non-null fields to the application: player_id, score, and us_5_digit_zip_code.

A data analyst has a .csv mapping file that maps a small number of us_5_digit_zip_code values to a territory code. The data analyst needs to include the territory code, if one exists, as an additional output of the Kinesis Data Analytics application.

How should the data analyst meet this requirement while minimizing costs?

Options:

A.

Store the contents of the mapping file in an Amazon DynamoDB table. Preprocess the records as they arrive in the Kinesis Data Analytics application with an AWS Lambda function that fetches the mapping and supplements each record to include the territory code, if one exists. Change the SQL query in the application to include the new field in the SELECT statement.

B.

Store the mapping file in an Amazon S3 bucket and configure the reference data column headers for the

.csv file in the Kinesis Data Analytics application. Change the SQL query in the application to include a join to the file’s S3 Amazon Resource Name (ARN), and add the territory code field to the SELECT columns.

C.

Store the mapping file in an Amazon S3 bucket and configure it as a reference data source for the Kinesis Data Analytics application. Change the SQL query in the application to include a join to the reference table and add the territory code field to the SELECT columns.

D.

Store the contents of the mapping file in an Amazon DynamoDB table. Change the Kinesis Data Analytics application to send its output to an AWS Lambda function that fetches the mapping and supplements each record to include the territory code, if one exists. Forward the record from the Lambda function to the original application destination.

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Question 2

A company analyzes its data in an Amazon Redshift data warehouse, which currently has a cluster of three dense storage nodes. Due to a recent business acquisition, the company needs to load an additional 4 TB of user data into Amazon Redshift. The engineering team will combine all the user data and apply complex calculations that require I/O intensive resources. The company needs to adjust the cluster's capacity to support the change in analytical and storage requirements.

Which solution meets these requirements?

Options:

A.

Resize the cluster using elastic resize with dense compute nodes.

B.

Resize the cluster using classic resize with dense compute nodes.

C.

Resize the cluster using elastic resize with dense storage nodes.

D.

Resize the cluster using classic resize with dense storage nodes.

Question 3

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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