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Question 202 - MLS-C01 discussion

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A company ingests machine learning (ML) data from web advertising clicks into an Amazon S3 data lake. Click data is added to an Amazon Kinesis data stream by using the Kinesis Producer Library (KPL). The data is loaded into the S3 data lake from the data stream by using an Amazon Kinesis Data Firehose delivery stream. As the data volume increases, an ML specialist notices that the rate of data ingested into Amazon S3 is relatively constant. There also is an increasing backlog of data for Kinesis Data Streams and Kinesis Data Firehose to ingest.

Which next step is MOST likely to improve the data ingestion rate into Amazon S3?

A.
Increase the number of S3 prefixes for the delivery stream to write to.
Answers
A.
Increase the number of S3 prefixes for the delivery stream to write to.
B.
Decrease the retention period for the data stream.
Answers
B.
Decrease the retention period for the data stream.
C.
Increase the number of shards for the data stream.
Answers
C.
Increase the number of shards for the data stream.
D.
Add more consumers using the Kinesis Client Library (KCL).
Answers
D.
Add more consumers using the Kinesis Client Library (KCL).
Suggested answer: C

Explanation:

The data ingestion rate into Amazon S3 can be improved by increasing the number of shards for the data stream. A shard is the base throughput unit of a Kinesis data stream. One shard provides 1 MB/second data input and 2 MB/second data output. Increasing the number of shards increases the data ingestion capacity of the stream. This can help reduce the backlog of data for Kinesis Data Streams and Kinesis Data Firehose to ingest.

References:

Shard - Amazon Kinesis Data Streams

Scaling Amazon Kinesis Data Streams with AWS CloudFormation - AWS Big Data Blog

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asked 16/09/2024
Rodwell Shibambu
51 questions
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