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Exam AWS Certified Machine Learning - Specialty topic 1 question 240 discussion

Exam question from Amazon's AWS Certified Machine Learning - Specialty
Question #: 240
Topic #: 1
[All AWS Certified Machine Learning - Specialty Questions]

A machine learning (ML) engineer at a bank is building a data ingestion solution to provide transaction features to financial ML models. Raw transactional data is available in an Amazon Kinesis data stream.

The solution must compute rolling averages of the ingested data from the data stream and must store the results in Amazon SageMaker Feature Store. The solution also must serve the results to the models in near real time.

Which solution will meet these requirements?

  • A. Load the data into an Amazon S3 bucket by using Amazon Kinesis Data Firehose. Use a SageMaker Processing job to aggregate the data and to load the results into SageMaker Feature Store as an online feature group.
  • B. Write the data directly from the data stream into SageMaker Feature Store as an online feature group. Calculate the rolling averages in place within SageMaker Feature Store by using the SageMaker GetRecord API operation.
  • C. Consume the data stream by using an Amazon Kinesis Data Analytics SQL application that calculates the rolling averages. Generate a result stream. Consume the result stream by using a custom AWS Lambda function that publishes the results to SageMaker Feature Store as an online feature group.
  • D. Load the data into an Amazon S3 bucket by using Amazon Kinesis Data Firehose. Use a SageMaker Processing job to load the data into SageMaker Feature Store as an offline feature group. Compute the rolling averages at query time.
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Suggested Answer: C 🗳️
by SandeepGun at June 17, 2023, 3:41 p.m.
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endeesa
11 months, 3 weeks ago
Selected Answer: C
From what I see, C is the only option that will meet the time constratins
upvoted 1 times
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loict
1 year, 2 months ago
Selected Answer: C
A. NO - no need for intermediary S3 storage B. NO - Feature store does not have built-in transformations C. YES - https://aws.amazon.com/blogs/machine-learning/using-streaming-ingestion-with-amazon-sagemaker-feature-store-to-make-ml-backed-decisions-in-near-real-time/ D. NO - Computing a query time is expensive, you want it done once and cached
upvoted 2 times
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Mickey321
1 year, 2 months ago
Selected Answer: C
Amazon Kinesis Data Analytics is a fully managed service that makes it easy to process streaming data. Amazon Kinesis Data Analytics SQL is a feature of Amazon Kinesis Data Analytics that allows you to process streaming data using SQL. AWS Lambda is a serverless compute service that allows you to run code without provisioning or managing servers. SageMaker Feature Store is a managed service that makes it easy to store and manage features for machine learning models.
upvoted 1 times
vkbajoria
8 months, 1 week ago
agree with all but the only question is how can we consume data directly using Kinesis Data Analytics? Don't we need Kinesis Data Stream or Firehose to consume the stream data?
upvoted 1 times
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kaike_reis
1 year, 2 months ago
Selected Answer: C
The letter B is wrong as KDS does not have the ability to load (another service is needed for this, such as KDF). The letter D is wrong as it saves a variable that needs to be accessed quickly in an offline group in the Feature Store. Since the solution starts with KDS and we need the moving average results to be displayed in near real time, the letter C guarantees this: KDS → KDA → Lambda (triggered quickly) → SM FS. Letter A is wrong, as it does not guarantee near real-time feedback.
upvoted 2 times
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SandeepGun
1 year, 4 months ago
Selected Answer: C
KDA provides facility for rolling averages and meet with realtime requirement
upvoted 1 times
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