Databricks-Machine-Learning-Professional Exam Dumps : Databricks Certified Machine Learning Professional

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, you can use the following code block:

Python

# Get the missing features from the Feature Store using the model URI and the customer_id column

df = fs.get_missing_features(spark_df, model_uri, lookup_key="customer_id")

# Score the DataFrame using the model URI and the Feature Store Client

fs.score_batch(model_uri, df)

AI-generated code. Review and use carefully. More info on FAQ.

The fs.get_missing_features method takes a Spark DataFrame, a model URI, and a lookup key as arguments. It returns a new Spark DataFrame that contains the originalcolumns plus the missing features that are required by the model. The missing features are retrieved from the Feature Store by joining the DataFrame with the feature tables using the lookup key. The lookup key must match the primary key of the feature tables. The model URI must point to a registered model that was trained using features from the Feature Store1.

The fs.score_batch method takes a model URI and a Spark DataFrame as arguments. It applies the model to the DataFrame and returns a new Spark DataFrame that contains the original columns plus a prediction column. The model URI must point to a registered model that was trained using features from the Feature Store2.

The other options are incorrect because:

• Option A: fs.score_model is not a valid method name, as it is missing an underscore. The correct method name is fs.score_batch2.
• Option B: fs.score_model without getting the missing features will not work, as the model expects the DataFrame to have all the features that were used for training. The correct way is to use fs.get_missing_features before fs.score_batch12.
• Option D: fs.score_batch without getting the missing features will not work, as the model expects the DataFrame to have all the features that were used for training. The correct way is to use fs.get_missing_features before fs.score_batch12.
• Option E: fs.score_batch without specifying the lookup key will not work, as the fs.get_missing_features method requires a lookup key to join the DataFrame with the feature tables. The correct way is to use fs.get_missing_features with the lookup key “customer_id” before fs.score_batch12. References: Get missing features, Score batch

To log feature importance data from a CSV file as an artifact of an MLflow run, the correct method is mlflow.log_artifact, which takes the local file path and the artifact path as arguments.The mlflow.log_model method is used to log a model with a specific flavor, such as pyfunc or spark. The mlflow.log_data method does not exist. The mlflow.log_model_and_data method also does not exist, and it is not necessary to log both the model and the data in this case. References:

• MLflow Tracking API
• MLflow Models

MLflow provides a module called mlflow.shap that can be used to automatically calculate and log a Shapley feature importance plot using the SHAP library. The mlflow.shap.log_explanation function takes a predict function and a sample of input data, and computes the SHAP values and base values for each feature. It then logs the following artifacts to the current run:

• Base values
• SHAP values (computed using shap.KernelExplainer)
• Summary bar plot (shows the average impact of each feature on model output) The mlflow.shap.log_explanation function can be used with any model that has a predict function, such as scikit-learn, xgboost, etc1234 References:
• mlflow.shap — MLflow 2.9.1 documentation
• Extract feature importance from a mlflow 1.9 PyFuncModel model
• 9.6 SHAP (SHapley Additive exPlanations) | Interpretable Machine Learning
• load MLFlow model and plot feature importance with feature names