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Databricks Databricks-Machine-Learning-Professional Exam Syllabus Topics:

Topic	Details
Topic 1	Identify a use case for HTTP webhooks and where the Webhook URL needs to come Identify advantages of using Job clusters over all-purpose clusters
Topic 2	Test whether the updated model performs better on the more recent data Identify when retraining and deploying an updated model is a probable solution to drift
Topic 3	Describe concept drift and its impact on model efficacy Describe summary statistic monitoring as a simple solution for numeric feature drift
Topic 4	Identify live serving benefits of querying precomputed batch predictions Describe Structured Streaming as a common processing tool for ETL pipelines
Topic 5	Identify the requirements for tracking nested runs Describe an MLflow flavor and the benefits of using MLflow flavors
Topic 6	Identify less performant data storage as a solution for other use cases Describe why complex business logic must be handled in streaming deployments
Topic 7	Identify JIT feature values as a need for real-time deployment Describe how to list all webhooks and how to delete a webhook
Topic 8	Describe model serving deploys and endpoint for every stage Identify scenarios in which feature drift and or label drift are likely to occur
Topic 9	Describe the advantages of using the pyfunc MLflow flavor Manually log parameters, models, and evaluation metrics using MLflow

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Databricks Certified Machine Learning Professional Sample Questions (Q137-Q142):

NEW QUESTION # 137
A data scientist has developed and logged a Spark ML random forest model model, and then they ended their Spark session and terminated their cluster. After starting a new cluster, they want to review the featureImportances of the original model object. Which lines of code can be used to restore the model object so that featureImportances is available?

A. client.pyfunc.load_model(model_uri)
B. client.list_artifacts(run_id)["feature-importances.csv"]
C. This can only be viewed in the MLflow Experiments UI
D. mlflow.sklearn.load_model(model_uri)
E. mlflow.load_model(model_uri)

Answer: D

NEW QUESTION # 138
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. fs.score_model(model_uri, spark_df)
B. df = fs.get_missing_features(spark_df, model_uri)
fs.score_model(model_uri, df)
C. fs.score_batch(model_uri, spark_df)
D. df = fs.get_missing_features(spark_df, model_uri)
fs.score_batch(model_uri, df)
E. df = fs.get_missing_features(spark_df)
fs.score_batch(model_uri, df)

Answer: C

NEW QUESTION # 139
A data scientist has developed a model model and computed the RMSE of the model on the test set. They have assigned this value to the variable rmse. They now want to manually store the RMSE value with the MLflow run.
They write the following incomplete code block:

Which of the following lines of code can be used to fill in the blank so the code block can successfully complete the task?

A. There is no way to store values like this.
B. log_metric
C. log_model
D. log_artifact
E. log_param

Answer: D

NEW QUESTION # 140
Which MLflow command logs a trained model?

A. mlflow.run()
B. mlflow.start_run()
C. mlflow.log_model()
D. mlflow.register_model()

Answer: C

Explanation:
mlflow.log_model() saves the model artifact within the run.

NEW QUESTION # 141
A machine learning engineer wants to log feature importance data from a CSV file at path importance_path with an MLflow run for model model. Which code block will accomplish this task inside of an existing MLflow run block?

A. mlflow.log_artifact(importance_path, "feature-importance.csv")
B.
C.
D. mlflow.log_data(importance_path, "feature-importance.csv")
E. None of these code blocks tan accomplish the task.

Answer: C

NEW QUESTION # 142
......

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