A company is hosting an enterprise reporting solution with Amazon Redshift. The application provides reporting capabilities to three main groups: an executive group to access financial reports, a data analyst group to run long-running ad-hoc queries, and a data engineering group to run stored procedures and ETL processes. The executive team requires queries to run with optimal performance. The data engineering team expects queries to take minutes.
Which Amazon Redshift feature meets the requirements for this task?

• A.Concurrency scaling
• B.Short query acceleration (SQA)
• C.Workload management (WLM)
• D.Materialized views

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An airline has been collecting metrics on flight activities for analytics. A recently completed proof of concept demonstrates how the company provides insights to data analysts to improve on-time departures. The proof of concept used objects in Amazon S3, which contained the metrics in .csv format, and used Amazon Athena for querying the dat a. As the amount of data increases, the data analyst wants to optimize the storage solution to improve query performance.
Which options should the data analyst use to improve performance as the data lake grows? (Choose three.)

• A.Add a randomized string to the beginning of the keys in S3 to get more throughput across partitions.
• B.Use an S3 bucket in the same account as Athena.
• C.Compress the objects to reduce the data transfer I/O.
• D.Use an S3 bucket in the same Region as Athena.
• E.Preprocess the .csv data to JSON to reduce I/O by fetching only the document keys needed by the query.
• F.Preprocess the .csv data to Apache Parquet to reduce I/O by fetching only the data blocks needed for predicates.

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