Topic 1 Question 257
A music streaming company is building a pipeline to extract features. The company wants to store the features for offline model training and online inference. The company wants to track feature history and to give the company’s data science teams access to the features.
Which solution will meet these requirements with the MOST operational efficiency?
Use Amazon SageMaker Feature Store to store features for model training and inference. Create an online store for online inference. Create an offline store for model training. Create an IAM role for data scientists to access and search through feature groups.
Use Amazon SageMaker Feature Store to store features for model training and inference. Create an online store for both online inference and model training. Create an IAM role for data scientists to access and search through feature groups.
Create one Amazon S3 bucket to store online inference features. Create a second S3 bucket to store offline model training features. Turn on versioning for the S3 buckets and use tags to specify which tags are for online inference features and which are for offline model training features. Use Amazon Athena to query the S3 bucket for online inference. Connect the S3 bucket for offline model training to a SageMaker training job. Create an IAM policy that allows data scientists to access both buckets.
Create two separate Amazon DynamoDB tables to store online inference features and offline model training features. Use time-based versioning on both tables. Query the DynamoDB table for online inference. Move the data from DynamoDB to Amazon S3 when a new SageMaker training job is launched. Create an IAM policy that allows data scientists to access both tables.
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コメント(4)
- 正解だと思う選択肢: A
Answer is A - "SageMaker Feature Store consists of an online and an offline mode for managing features. The online store is used for low-latency real-time inference use cases. The offline store is primarily used for batch predictions and model training." https://aws.amazon.com/blogs/machine-learning/speed-ml-development-using-sagemaker-feature-store-and-apache-iceberg-offline-store-compaction/
👍 7asdfzxc2023/06/19 - 正解だと思う選択肢: A
Amazon SageMaker Feature Store is a managed service that makes it easy to store and manage features for machine learning models. It provides a scalable and reliable way to store features, and it supports both online inference and offline model training. Creating separate online and offline stores in SageMaker Feature Store will allow the music streaming company to optimize the storage and performance of their features for each use case. The online store can be configured to be highly available and performant, while the offline store can be configured to be cost-effective and scalable.
👍 1Mickey3212023/08/23 - 正解だと思う選択肢: A
A. YES - online store will be faster for inference, offline store cheaper for batch B. NO - online store for offline will be too expensive C. NO - want to use Feature store D. NO - want to use Feature store
👍 1loict2023/09/12
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