AWS AI Practitioner (AIF-C01) · Free practice question 10 of 12
SageMaker Feature Store shared features
Ormsby Manufacturing's data science teams keep re-creating the same input features, such as average machine temperature over the last hour, for different models, and the values used in training sometimes differ from those used at inference. They want a central repository to store, share, and reuse features for both training and low-latency real-time inference. Which SageMaker AI capability fits?
- A.Amazon Polly
- B.Amazon SageMaker Feature Store
- C.Amazon SageMaker Model Cards
- D.Amazon Rekognition
Show answer and explanation
Correct answer: B. Amazon SageMaker Feature Store
Why: SageMaker Feature Store is a managed repository for ML features, with an online store for low-latency lookups during real-time inference and an offline store for training and batch use, so teams can share consistent feature definitions across models. Model Cards document models, Polly converts text to speech, and Rekognition analyzes images and video.
More free AWS AI Practitioner (AIF-C01) questions
- Rekognition image content moderation
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- Polly text-to-speech narration
- Lex conversational chatbot intents
- Transcribe and Translate call pipeline
- Personalize real-time recommendations
- SageMaker Data Wrangler data preparation
- Deep learning learns features from raw data
- Stop sequences end generation
- SageMaker Model Registry version catalog
- Test set for final unbiased evaluation