CERTIFIED-MACHINE-LEARNING-PROFESSIONAL · Question #24
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-le
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Question
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?
Options
- Adf = fs.get_missing_features(spark_df, model_uri)
- Bfs.score_model(model_uri, spark_df)
- Cdf = fs.get_missing_features(spark_df, model_uri)
- Dfs.score_batch(model_uri, df)
- Efs.score_batch(model_uri, spark_df)
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