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Amazon Web Services DAS-C01 Dumps

Exam Code:
DAS-C01
Exam Name:
AWS Certified Data Analytics - Specialty
Last Update: Feb 4, 2023
157 Questions
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AWS Certified Data Analytics (DAS-C01) is designed for individuals performing data analytics roles, as its name exam shows. It examines the candidate's ability to understand designing, building, securing, and maintaining data analytics solutions on AWS and the data lifecycle of collection, storage, processing, and visualization. Certification from AWS is an enterprise accreditation that verifies candidates' knowledge of AWS data lakes and analytics software.

Candidates for the AWS Certified Data Analytics – Specialty test must have the following qualifications: at least five years of expertise with data analytics techniques and two years of the practical learning experience with AWS. Second, expertise with AWS services to design, create, secure, and maintain analytics systems.

The AWS Certified Data Analytics exam is a 65-question Specialized level exam. The time limit is of 180 minutes to complete the exam. The exam is also available in two formats: a testing center and an online written exam. The exam fee for AWS Certified Data Analytics - Specialty is $300.

DAS-C01 Exam Details

Format: Multiple choice, Multiple Answer
Exam Type: Specialty
Exam Method: Testing center or online proctored exam
Time: 180 Min
Exam Price: $300 USD
Language: Available in English, Japanese, Korean, and Simplified Chinese

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AWS Certified Data Analytics - Specialty Questions and Answers

Questions 1

A marketing company is using Amazon EMR clusters for its workloads. The company manually installs third- party libraries on the clusters by logging in to the master nodes. A data analyst needs to create an automated solution to replace the manual process.

Which options can fulfill these requirements? (Choose two.)

Options:

A.

Place the required installation scripts in Amazon S3 and execute them using custom bootstrap actions.

B.

Place the required installation scripts in Amazon S3 and execute them through Apache Spark in Amazon EMR.

C.

Install the required third-party libraries in the existing EMR master node. Create an AMI out of that master node and use that custom AMI to re-create the EMR cluster.

D.

Use an Amazon DynamoDB table to store the list of required applications. Trigger an AWS Lambda function with DynamoDB Streams to install the software.

E.

Launch an Amazon EC2 instance with Amazon Linux and install the required third-party libraries on the instance. Create an AMI and use that AMI to create the EMR cluster.

Questions 2

A company has 1 million scanned documents stored as image files in Amazon S3. The documents contain typewritten application forms with information including the applicant first name, applicant last name, application date, application type, and application text. The company has developed a machine learning algorithm to extract the metadata values from the scanned documents. The company wants to allow internal data analysts to analyze and find applications using the applicant name, application date, or application text. The original images should also be downloadable. Cost control is secondary to query performance.

Which solution organizes the images and metadata to drive insights while meeting the requirements?

Options:

A.

For each image, use object tags to add the metadata. Use Amazon S3 Select to retrieve the files based on the applicant name and application date.

B.

Index the metadata and the Amazon S3 location of the image file in Amazon Elasticsearch Service. Allow the data analysts to use Kibana to submit queries to the Elasticsearch cluster.

C.

Store the metadata and the Amazon S3 location of the image file in an Amazon Redshift table. Allow the data analysts to run ad-hoc queries on the table.

D.

Store the metadata and the Amazon S3 location of the image files in an Apache Parquet file in Amazon S3, and define a table in the AWS Glue Data Catalog. Allow data analysts to use Amazon Athena to submit custom queries.

Questions 3

A software company hosts an application on AWS, and new features are released weekly. As part of the application testing process, a solution must be developed that analyzes logs from each Amazon EC2 instance to ensure that the application is working as expected after each deployment. The collection and analysis solution should be highly available with the ability to display new information with minimal delays.

Which method should the company use to collect and analyze the logs?

Options:

A.

Enable detailed monitoring on Amazon EC2, use Amazon CloudWatch agent to store logs in Amazon S3, and use Amazon Athena for fast, interactive log analytics.

B.

Use the Amazon Kinesis Producer Library (KPL) agent on Amazon EC2 to collect and send data to Kinesis Data Streams to further push the data to Amazon Elasticsearch Service and visualize using Amazon QuickSight.

C.

Use the Amazon Kinesis Producer Library (KPL) agent on Amazon EC2 to collect and send data to Kinesis Data Firehose to further push the data to Amazon Elasticsearch Service and Kibana.

D.

Use Amazon CloudWatch subscriptions to get access to a real-time feed of logs and have the logs delivered to Amazon Kinesis Data Streams to further push the data to Amazon Elasticsearch Service and Kibana.

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