Databricks Databricks-Machine-Learning-Professional Exam With Confidence Using Practice Dumps

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

Data type tests and checks are particularly important for streaming deployments because of the following reasons:

• Because the streaming deployment is always on, all types of data must be handled without producing an error. Streaming data may come from various sources and have different formats, schemas, or quality. If the streaming deployment cannot handle the data properly, it may result in errors, failures, or inaccurate outputs. Therefore, data type tests and checks can help validate the data and ensure the streaming deployment can process it correctly.
• Because the streaming deployment is always on, there is no practitioner to debug poor model performance. Streaming data may also change over time, affecting the model performance and accuracy. If the streaming deployment does not have a mechanism to monitor and evaluate the model performance, it may not be able to detect or correct any issues. Therefore, data type tests and checks can help identify any data drift or anomalies that may impact the model performance and trigger alerts or actions.
• Because the streaming deployment is always on, there is a need to confirm that the deployment can autoscale. Streaming data may have varying volumes and velocities, requiring the streaming deployment to scale up or down accordingly. If the streaming deployment does not have a way to adjust the resources and throughput based on the data demand, it may result in bottlenecks, delays, or inefficiencies. Therefore, data type tests and checks can help measure the data load and ensure the streaming deployment can autoscale effectively. References: Streaming Data, Data Validation, Model Monitoring, Autoscaling

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