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A data engineer is optimizing query performance in Amazon Athena notebooks that use Apache Spark to analyze large datasets that are stored in Amazon S3. The data is partitioned. An AWS Glue crawler updates the partitions.
The data engineer wants to minimize the amount of data that is scanned to improve efficiency of Athena queries.
Which solution will meet these requirements?
A data engineer is launching an Amazon EMR cluster. The data that the data engineer needs to load into the new cluster is currently in an Amazon S3 bucket. The data engineer needs to ensure that data is encrypted both at rest and in transit.
The data that is in the S3 bucket is encrypted by an AWS Key Management Service (AWS KMS) key. The data engineer has an Amazon S3 path that has a Privacy Enhanced Mail (PEM) file.
Which solution will meet these requirements?
To meet both encryption at rest and in transit, a single Amazon EMR security configuration can be created specifying the AWS KMS key for encryption at rest and the PEM file for in-transit encryption. The study guide clearly states:
''AWS Key Management Service (KMS) provides encryption for data at rest, and SSL/TLS ensures encryption for data in transit, providing end-to-end encryption within an AWS environment.''
-- Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf
A single security configuration is sufficient and the cleanest way to apply these security features during EMR cluster setup.
A data engineer needs to create an empty copy of an existing table in Amazon Athena to perform data processing tasks. The existing table in Athena contains 1,000 rows.
Which query will meet this requirement?
In Amazon Athena, you can use CREATE TABLE AS SELECT with WITH NO DATA to create an empty copy of an existing table's schema:
''The query CREATE TABLE new_table AS SELECT * FROM old_table WITH NO DATA; creates a new table with the same schema but without copying over the data.''
-- Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf
This is the most efficient way to create an empty version of the existing table.
A company needs to store and analyze a large amount of IoT sensor data. The company needs to retain the data indefinitely. The company analyzes the data in an Amazon Redshift cluster.
Which solution will meet these requirements MOST cost-effectively?
Option B is correct because the requirement is to retain a very large dataset indefinitely and analyze it from Amazon Redshift in the most cost-effective way. Amazon Redshift Spectrum allows Redshift to query data directly in Amazon S3 without loading all of the data into Redshift-managed storage. That reduces warehouse storage cost for long-term retained data. AWS documentation also recommends using Apache Parquet for Spectrum because Parquet is a columnar format, which allows Redshift Spectrum to read only the columns needed instead of scanning entire text files. This improves performance and lowers query cost.
Option A and D are less cost-effective because auto-copy loads data into the Redshift cluster, which means the company pays to store all historical data in Redshift even though the requirement is indefinite retention of a large volume of IoT data. Option C is worse than B because JSON is a row-oriented text format, and AWS guidance says columnar formats such as Parquet or ORC are preferred for Redshift Spectrum for better scan efficiency and lower cost.
A data engineer needs to optimize the performance of a data pipeline that handles retail orders. Data about the orders is ingested daily into an Amazon S3 bucket.
The data engineer runs queries once each week to extract metrics from the orders data based on the order date for multiple date ranges. The data engineer needs an optimization solution that ensures the query performance will not degrade when the volume of data increases.
For query workloads on S3 data that depend on date-based filters, partitioning by order date optimizes performance and cost because Athena reads only the relevant partitions.
Athena scales automatically and doesn't degrade with increasing data size when partitions are managed efficiently.
''Partitioning data in Amazon S3 based on query predicates such as order date improves Athena query performance and reduces scanned data volume.''
-- Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf
This is the most cost-effective and scalable option for date-based queries.
