DSA-C03 Exam Dumps | You are using Snowpark Feature Store to manage features for your machine learning models. You've created

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Question 127/143

You are using Snowpark Feature Store to manage features for your machine learning models. You've created several Feature Groups and now want to consume these features for training a model. To optimize retrieval, you want to use point-in-time correctness. Which of the following actions/configurations are essential to ensure point-in-time correctness when retrieving features using Snowpark Feature Store?

A. Ensure that all source tables used by the Feature Groups have Change Data Capture (CDC) enabled.

B. When creating Feature Groups, specify a 'timestamp_key' that represents the event timestamp of the data in the source tables.

C. Use the method on the Feature Store client, providing a dataframe containing the 'primary_keyS and the desired for each record.

D. Create an associated Stream on the source tables used for Feature Groups

E. Explicitly specify a in the call.

Correct Answer: B,C

Options B and C are correct. B: Specifying a 'timestamp_key' during Feature Group creation is crucial for enabling point-in-time correctness. This tells the Feature Store which column represents the event timestamp. C: The method is specifically designed for point-in-time lookups. It requires a dataframe containing primary keys and the desired timestamp for each lookup. This enables the Feature Store to retrieve the feature values as they were at that specific point in time. Option A is incorrect, while enabling CDC is valuable for incremental updates, it does not guarantee point-in-time correctness without specifying the timestamp key and retrieving historical features using that key. Option D is not necessary, streams enable incremental loads but are separate from point in time. Option E, is not needed, its implicit via using .

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