Topic 1 Question 240
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?
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.
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.
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.
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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コメント(5)
- 正解だと思う選択肢: 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.
👍 2kaike_reis2023/08/17 - 正解だと思う選択肢: 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
👍 2loict2023/09/12 - 正解だと思う選択肢: C
KDA provides facility for rolling averages and meet with realtime requirement
👍 1SandeepGun2023/06/17
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