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Exam Certified Machine Learning Professional All Questions

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Exam Certified Machine Learning Professional topic 1 question 43 discussion

Actual exam question from Databricks's Certified Machine Learning Professional
Question #: 43
Topic #: 1
[All Certified Machine Learning Professional Questions]

A machine learning engineer has developed a model and registered it using the FeatureStoreClient fs. The model has model URI model_uri. The engineer now needs to perform batch inference on customer-level Spark DataFrame spark_df, but it is missing a few of the static features that were used when training the model. The customer_id column is the primary key of spark_df and the training set used when training and logging the model.
Which of the following code blocks can be used to compute predictions for spark_df when the missing feature values can be found in the Feature Store by searching for features by customer_id?

  • A. df = fs.get_missing_features(spark_df, model_uri)
    fs.score_model(model_uri, df)
  • B. fs.score_model(model_uri, spark_df)
  • C. df = fs.get_missing_features(spark_df, model_uri)
    fs.score_batch(model_uri, df)
  • D. df = fs.get_missing_features(spark_df)
    fs.score_batch(model_uri, df)
  • E. fs.score_batch(model_uri, spark_df)
Show Suggested Answer Hide Answer
Suggested Answer: E 🗳️

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victorcolome
10 months, 1 week ago
Selected Answer: E
The answer is E. See score_batch in https://api-docs.databricks.com/python/feature-store/latest/feature_store.client.html. "Additional features required for model evaluation will be automatically retrieved from Feature Store." Besides, methods "get_missing_features" and "score_model" do not appear in the documentation.
upvoted 4 times
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IT3008
10 months, 2 weeks ago
Right answer is E - there is no API info called 'get_missing_features' in the doc.
upvoted 1 times
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BokNinja
11 months, 1 week ago
The correct answer is C. df = fs.get_missing_features(spark_df, model_uri) fs.score_batch(model_uri, df). In this code snippet, fs.get_missing_features(spark_df, model_uri) is used to retrieve the missing features from the Feature Store using the customer_id as the key. The resulting DataFrame df contains the original data along with the retrieved features. Then, fs.score_batch(model_uri, df) is used to perform batch inference on the DataFrame df using the model specified by model_uri.
upvoted 1 times
inet777
6 months ago
Except - I could not find a method called get_missing_features in FeatureStoreClient APIs. E is right answer.
upvoted 1 times
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