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Amazon Web Services DAS-C01 Dumps

Exam Code:
DAS-C01
Exam Name:
AWS Certified Data Analytics - Specialty
Last Update: Oct 3, 2022
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 data analyst is designing a solution to interactively query datasets with SQL using a JDBC connection. Users will join data stored in Amazon S3 in Apache ORC format with data stored in Amazon Elasticsearch Service (Amazon ES) and Amazon Aurora MySQL.

Which solution will provide the MOST up-to-date results?

Options:

A.

Use AWS Glue jobs to ETL data from Amazon ES and Aurora MySQL to Amazon S3. Query the data with Amazon Athena.

B.

Use Amazon DMS to stream data from Amazon ES and Aurora MySQL to Amazon Redshift. Query the data with Amazon Redshift.

C.

Query all the datasets in place with Apache Spark SQL running on an AWS Glue developer endpoint.

D.

Query all the datasets in place with Apache Presto running on Amazon EMR.

Questions 2

A company’s marketing team has asked for help in identifying a high performing long-term storage service for their data based on the following requirements:

  • The data size is approximately 32 TB uncompressed.
  • There is a low volume of single-row inserts each day.
  • There is a high volume of aggregation queries each day.
  • Multiple complex joins are performed.
  • The queries typically involve a small subset of the columns in a table.

Which storage service will provide the MOST performant solution?

Options:

A.

Amazon Aurora MySQL

B.

Amazon Redshift

C.

Amazon Neptune

D.

Amazon Elasticsearch

Questions 3

A company uses Amazon Redshift as its data warehouse. A new table has columns that contain sensitive data. The data in the table will eventually be referenced by several existing queries that run many times a day.

A data analyst needs to load 100 billion rows of data into the new table. Before doing so, the data analyst must ensure that only members of the auditing group can read the columns containing sensitive data.

How can the data analyst meet these requirements with the lowest maintenance overhead?

Options:

A.

Load all the data into the new table and grant the auditing group permission to read from the table. Load all the data except for the columns containing sensitive data into a second table. Grant the appropriate users read-only permissions to the second table.

B.

Load all the data into the new table and grant the auditing group permission to read from the table. Use the GRANT SQL command to allow read-only access to a subset of columns to the appropriate users.

C.

Load all the data into the new table and grant all users read-only permissions to non-sensitive columns. Attach an IAM policy to the auditing group with explicit ALLOW access to the sensitive data columns.

D.

Load all the data into the new table and grant the auditing group permission to read from the table. Create a view of the new table that contains all the columns, except for those considered sensitive, and grant the appropriate users read-only permissions to the table.

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