Online DAS-C01 Questions Video

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Question 24

A company using Amazon QuickSight Enterprise edition has thousands of dashboards analyses and datasets. The company struggles to manage and assign permissions for granting users access to various items within QuickSight. The company wants to make it easier to implement sharing and permissions management.

Which solution should the company implement to simplify permissions management?

Options:

Use QuickSight folders to organize dashboards, analyses, and datasets Assign individual users permissions to these folders

Use QuickSight folders to organize dashboards analyses, and datasets Assign group permissions by using these folders.

Use AWS 1AM resource-based policies to assign group permissions to QuickSight items

Use QuickSight user management APIs to provision group permissions based on dashboard naming conventions

Question 25

A data analyst runs a large number of data manipulation language (DML) queries by using Amazon Athena with the JDBC driver. Recently, a query failed after It ran for 30 minutes. The query returned the following message

Java.sql.SGLException: Query timeout

The data analyst does not immediately need the query results However, the data analyst needs a long-term solution for this problem

Which solution will meet these requirements?

Options:

Split the query into smaller queries to search smaller subsets of data.

In the settings for Athena, adjust the DML query timeout limit

In the Service Quotas console, request an increase for the DML query timeout

Save the tables as compressed .csv files

Question 26

A transportation company uses IoT sensors attached to trucks to collect vehicle data for its global delivery fleet. The company currently sends the sensor data in small .csv files to Amazon S3. The files are then loaded into a 10-node Amazon Redshift cluster with two slices per node and queried using both Amazon Athena and Amazon Redshift. The company wants to optimize the files to reduce the cost of querying and also improve the speed of data loading into the Amazon Redshift cluster.

Which solution meets these requirements?

Options:

Use AWS Glue to convert all the files from .csv to a single large Apache Parquet file. COPY the file into Amazon Redshift and query the file with Athena from Amazon S3.

Use Amazon EMR to convert each .csv file to Apache Avro. COPY the files into Amazon Redshift and query the file with Athena from Amazon S3.

Use AWS Glue to convert the files from .csv to a single large Apache ORC file. COPY the file into Amazon Redshift and query the file with Athena from Amazon S3.

Use AWS Glue to convert the files from .csv to Apache Parquet to create 20 Parquet files. COPY the files into Amazon Redshift and query the files with Athena from Amazon S3.

Question 27

A global company has different sub-organizations, and each sub-organization sells its products and services in various countries. The company's senior leadership wants to quickly identify which sub-organization is the strongest performer in each country. All sales data is stored in Amazon S3 in Parquet format.

Which approach can provide the visuals that senior leadership requested with the least amount of effort?