A company has collected more than 100 TB of log files in the last 24 months. The files are stored as raw text in a dedicated Amazon S3 bucket. Each object has a key of the form year-month-day_log_HHmmss.txt where HHmmss represents the time the log file was initially created. A table was created in Amazon Athena that points to the S3 bucket. One-time queries are run against a subset of columns in the table several times an hour.
A data analyst must make changes to reduce the cost of running these queries. Management wants a solution with minimal maintenance overhead.
Which combination of steps should the data analyst take to meet these requirements? (Choose three.)

A. Convert the log files to Apace Avro format.

B. Add a key prefix of the form date=year-month-day/ to the S3 objects to partition the data.

C. Convert the log files to Apache Parquet format.

D. Add a key prefix of the form year-month-day/ to the S3 objects to partition the data.

E. Drop and recreate the table with the PARTITIONED BY clause. Run the ALTER TABLE ADD PARTITION statement.

F. Drop and recreate the table with the PARTITIONED BY clause. Run the MSCK REPAIR TABLE statement.




 

Suggested Answer: BCF

Community Answer: BCF

Reference:
https://docs.aws.amazon.com/athena/latest/ug/msck-repair-table.html


This question is in DAS-C01 AWS Certified Data Analytics – Specialty Exam
For getting AWS Certified Data Analytics – Specialty Certificate



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