Q6Data Preparation for Machine Learning (ML)
HOTSPOT - An ML engineer needs to use Amazon SageMaker Feature Store to create and manage features to train a model. Select and order the steps from the following list to create and use the features in Feature Store. Each step should be selected one time. (Select and order three.) • Access the store to build datasets for training. • Create a feature group. • Ingest the records.
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Discussion · 3
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To create and manage features with Amazon SageMaker Feature Store, use these steps:
1) Create a feature group : Organize your features by defining a feature group.
2) Ingest the records : Load the data into the feature group.
3) Access the store to build datasets for training : Retrieve the data from the feature group to prepare for model training.
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Correct Steps (In Order):
✅ 1. Create a feature group.
✅ 2. Ingest the records.
✅ 3. Access the store to build datasets for training.
Why?
1️⃣ Create a Feature Group → Defines schema & storage for feature data.
2️⃣ Ingest Records → Stores data in the feature group.
3️⃣ Access Store → Retrieves features for model training.
Keywords to Remember:
✅ Feature Group → Defines structure
✅ Ingest Records → Store features
✅ Access Store → Retrieve for training
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Explanation:
Create a feature group: First, you define the schema (features) of your dataset.
Ingest the records: Fill the feature group with data.
Access the store to build datasets for training: Retrieve the ingested data to use when training models.