Exam AWS Certified Machine Learning - Specialty All Questions

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Exam AWS Certified Machine Learning - Specialty topic 1 question 273 discussion

Exam question from Amazon's AWS Certified Machine Learning - Specialty

Question #: 273
Topic #: 1

[All AWS Certified Machine Learning - Specialty Questions]

A network security vendor needs to ingest telemetry data from thousands of endpoints that run all over the world. The data is transmitted every 30 seconds in the form of records that contain 50 fields. Each record is up to 1 KB in size. The security vendor uses Amazon Kinesis Data Streams to ingest the data. The vendor requires hourly summaries of the records that Kinesis Data Streams ingests. The vendor will use Amazon Athena to query the records and to generate the summaries. The Athena queries will target 7 to 12 of the available data fields.

Which solution will meet these requirements with the LEAST amount of customization to transform and store the ingested data?

A. Use AWS Lambda to read and aggregate the data hourly. Transform the data and store it in Amazon S3 by using Amazon Kinesis Data Firehose.
B. Use Amazon Kinesis Data Firehose to read and aggregate the data hourly. Transform the data and store it in Amazon S3 by using a short-lived Amazon EMR cluster.
C. Use Amazon Kinesis Data Analytics to read and aggregate the data hourly. Transform the data and store it in Amazon S3 by using Amazon Kinesis Data Firehose.
D. Use Amazon Kinesis Data Firehose to read and aggregate the data hourly. Transform the data and store it in Amazon S3 by using AWS Lambda.

Show Suggested Answer

Suggested Answer: C 🗳️

by kukreti18 at July 7, 2023, 12:07 p.m.

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Comments

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sukye

7 months, 1 week ago

Selected Answer: C

Not D because Firehose is not best to aggregate, and lambda is not necessary.

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loict

9 months, 2 weeks ago

Selected Answer: C

A. NO - AWS Lambda is not per to aggregate, it works on a per-row basis B. NO - Kinesis Data Firehose is not best to aggregate C. YES - Amazon Kinesis Data Analytics can aggregate and stream to Firehose D. NO - Kinesis Data Firehose is not best to aggregate

upvoted 3 times

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jyrajan69

9 months, 2 weeks ago

Kinesis Data Firehose can invoke your Lambda function to transform incoming source data and deliver the transformed data to destinations. So the answer cannot be C, has to be D

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chet100

9 months, 4 weeks ago

C it is

upvoted 1 times

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Mickey321

10 months ago

Selected Answer: C

changing back to C. very confusing

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Mickey321

10 months ago

Selected Answer: D

Changing to option D Option D is the best option because it allows the network security vendor to use Amazon Kinesis Data Firehose to read and aggregate the data hourly from Amazon Kinesis Data Streams, and use AWS Lambda to transform the data and store it in Amazon S3. This way, the network security vendor can leverage the benefits of both services: Amazon Kinesis Data Firehose can provide a simple and scalable way to ingest, buffer, compress, and batch the streaming data; AWS Lambda can provide a flexible and cost-effective way to perform custom logic on the data, such as selecting only 7 to 12 fields for Athena queries. This option meets the requirements with the least amount of customization to transform and store the ingested data

upvoted 1 times

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kaike_reis

10 months, 1 week ago

Selected Answer: C

Letter B is wrong, as it brings the addition of yet another new service (EMR). Letter D is wrong, as we cannot directly use KDF to perform transformations (it's a load service only). Letter C is the most correct and fastest, as it uses the Kinesis family. Letter A is functional, as we can call Lambda via KDS, but it would involve more customization given the Lambda code to be built.

upvoted 1 times

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Mickey321

10 months, 4 weeks ago

Selected Answer: C

Changing my answer to C

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Mickey321

10 months, 4 weeks ago

Selected Answer: D

This option meets all the requirements with the least amount of customization. You can use Amazon Kinesis Data Firehose to ingest streaming data from thousands of endpoints and configure it to buffer the data by size or time interval (such as 1 hour). You can use AWS Lambda to transform the data and select only the relevant fields before delivering it to Amazon S3. You can also configure Amazon Kinesis Data Firehose to convert the data to a columnar format such as Apache Parquet or Apache ORC, which are optimized for querying with Amazon Athena3.

upvoted 1 times