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Question 170 - DAS-C01 discussion

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An event ticketing website has a data lake on Amazon S3 and a data warehouse on Amazon Redshift. Two datasets exist: events data and sales data. Each dataset has millions of records.

The entire events dataset is frequently accessed and is stored in Amazon Redshift. However, only the last 6 months of sales data is frequently accessed and is stored in Amazon Redshift. The rest of the sales data is available only in Amazon S3.

A data analytics specialist must create a report that shows the total revenue that each event has generated in the last 12 months. The report will be accessed thousands of times each week.

Which solution will meet these requirements with the LEAST operational effort?

A.
Create an AWS Glue job to access sales data that is older than 6 months from Amazon S3 and to access event and sales data from Amazon Redshift. Load the results into a new table in Amazon Redshift.
Answers
A.
Create an AWS Glue job to access sales data that is older than 6 months from Amazon S3 and to access event and sales data from Amazon Redshift. Load the results into a new table in Amazon Redshift.
B.
Create a stored procedure to copy sales data that is older than 6 months and newer than 12 months from Amazon S3 to Amazon Redshift. Create a materialized view with the autorefresh option
Answers
B.
Create a stored procedure to copy sales data that is older than 6 months and newer than 12 months from Amazon S3 to Amazon Redshift. Create a materialized view with the autorefresh option
C.
Create an AWS Lambda function to copy sales data that is older than 6 months and newer than 12 months to an Amazon Kinesis Data Firehose delivery stream. Specify Amazon Redshift as the destination of the delivery stream. Create a materialized view with the autorefresh option.
Answers
C.
Create an AWS Lambda function to copy sales data that is older than 6 months and newer than 12 months to an Amazon Kinesis Data Firehose delivery stream. Specify Amazon Redshift as the destination of the delivery stream. Create a materialized view with the autorefresh option.
D.
Create a materialized view in Amazon Redshift with the autorefresh option. Use Amazon Redshift Spectrum to include sales data that is older than 6 months.
Answers
D.
Create a materialized view in Amazon Redshift with the autorefresh option. Use Amazon Redshift Spectrum to include sales data that is older than 6 months.
Suggested answer: D

Explanation:

This solution meets the requirements because:

A materialized view is a database object that contains the results of a query.It can be used to improve query performance and reduce data processing costs by caching the query results and refreshing them periodically1.

The autorefresh option enables Amazon Redshift to automatically refresh materialized views with up-to-date data from its base tables when materialized views are created with or altered to have this option.Amazon Redshift autorefreshes materialized views as soon as possible after base tables change2.

Amazon Redshift Spectrum enables you to use your existing Amazon Redshift SQL queries to analyze data that is stored in Amazon S3.You can create external tables in your Amazon Redshift cluster and join them with other tables, including materialized views3.

By creating a materialized view in Amazon Redshift with the autorefresh option, the data analytics specialist can precompute and cache the report query results and keep them updated automatically. This can improve the report performance and reduce the load on the Amazon Redshift cluster.

By using Amazon Redshift Spectrum to include sales data that is older than 6 months, the data analytics specialist can access the data that is stored in Amazon S3 without loading it into Amazon Redshift. This can reduce the storage costs and avoid data duplication.

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asked 16/09/2024
Luis Elola
28 questions
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