A company currently has large data sets defined in S3 and are using AWS Athena to query the data sets. Since the query time is taking longer than expected , steps need to be taken to improve query performance. Which of the following can be taken ensuring that cost is not increased in the implementation process? Choose 2 answers from the options given below

Answer options:

A.Consider splitting the data set
B.Consider using the CREATE TABLE AS SELECT statement
C.Consider using AWS Quicksight instead
D.Consider using EMR clusters

Answer – A and B
An example of this is given in the AWS Documentation
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Using CTAS statements with Amazon Athena to reduce cost and improve performance
Amazon Athena is an interactive query service that makes it more efficient to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run. Athena recently released support for creating tables using the results of a SELECT query or CREATE TABLE AS SELECT (CTAS) statement. Analysts can use CTAS statements to create new tables from existing tables on a subset of data, or a subset of columns. They also have options to convert the data into columnar formats, such as Apache Parquet and Apache ORC, and partition it. Athena automatically adds the resultant table and partitions to the AWS Glue Data Catalog, making them immediately available for subsequent queries.
CTAS statements help reduce cost and improve performance by allowing users to run queries on smaller tables constructed from larger tables. This post covers three use cases that demonstrate the benefit of using CTAS to create a new dataset, smaller than the original one, allowing subsequent queries to run faster. Assuming our use case requires repeatedly querying the data, we can now query a smaller and more optimal dataset to get the results faster.
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Option C is incorrect since this is more of a visualization tool
Option D is incorrect since this would increase the costs of the overall solution
For more information on this use case, please refer to the below URL
https://aws.amazon.com/blogs/big-data/using-ctas-statements-with-amazon-athena-to-reduce-cost-and-improve-performance/