Câu 6: Databricks Certified Machine Learning Professional

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?

1. A. df = fs.get_missing_features(spark_df, model_uri)fs.score_model(model_uri, df)
2. B. fs.score_model(model_uri, spark_df)
3. C. df = fs.get_missing_features(spark_df, model_uri)fs.score_batch(model_uri, df)
4. D. df = fs.get_missing_features(spark_df)fs.score_batch(model_uri, df)
5. E. fs.score_batch(model_uri, spark_df) — đáp án hiện tại