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

Topic	Details
Topic 1	Identify JIT feature values as a need for real-time deployment Describe how to list all webhooks and how to delete a webhook
Topic 2	Describe the advantages of using the pyfunc MLflow flavor Manually log parameters, models, and evaluation metrics using MLflow
Topic 3	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 4	Describe model serving deploys and endpoint for every stage Identify scenarios in which feature drift and or label drift are likely to occur
Topic 5	Describe concept drift and its impact on model efficacy Describe summary statistic monitoring as a simple solution for numeric feature drift
Topic 6	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 7	Identify the requirements for tracking nested runs Describe an MLflow flavor and the benefits of using MLflow flavors
Topic 8	Create, overwrite, merge, and read Feature Store tables in machine learning workflows View Delta table history and load a previous version of a Delta table
Topic 9	Identify less performant data storage as a solution for other use cases Describe why complex business logic must be handled in streaming deployments
Topic 10	Identify which code block will trigger a shown webhook Describe the basic purpose and user interactions with Model Registry

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Databricks Certified Machine Learning Professional Sample Questions (Q126-Q131):

NEW QUESTION # 126
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. None of these code blocks tan accomplish the task.
D. mlflow.log_data(importance_path, "feature-importance.csv")
E.

Answer: E

NEW QUESTION # 127
Which deployment paradigm can centrally compute predictions for a single record with exceedingly fast results?

A. None of these strategies will accomplish the task.
B. Batch
C. Streaming
D. Edge/on-device
E. Real-time

Answer: E

NEW QUESTION # 128
A machine learning engineer is attempting to create a webhook that will trigger a Databricks Job job_id when a model version for model model transitions into any MLflow Model Registry stage.
They have the following incomplete code block:

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

A. "MODEL_VERSION_TRANSITIONED_TO_PRODUCTION"
B. "MODEL_VERSION_CREATED"
C. "MODEL_VERSION_TRANSITIONED_TO_STAGING", "MODEL_VERSION_TRANSITIONED_TO_PRODUCTION"
D. "MODEL_VERSION_TRANSITIONED_TO_STAGING"
E. "MODEL_VERSION_TRANSITIONED_STAGE"

Answer: D

NEW QUESTION # 129
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. mlflow.sklearn.load_model(model_uri)
B. client.list_artifacts(run_id)["feature-importances.csv"]
C. client.pyfunc.load_model(model_uri)
D. mlflow.load_model(model_uri)
E. This can only be viewed in the MLflow Experiments UI

Answer: A

NEW QUESTION # 130
A machine learning engineer is migrating a machine learning pipeline to use Databricks Machine Learning. They have programmatically identified the best run from an MLflow Experiment and stored its URI in the model_uri variable and its Run ID in the run_id variable. They have also determined that the model was logged with the name "model". Now, the machine learning engineer wants to register that model in the MLflow Model Registry with the name "best_model".
Which line of code can they use to register the model to the MLflow Model Registry?

A. mlflow.register_model(model_uri, "best_model")
B. mlflow.register_model(f"runs:/{run_id}/model")
C. mlflow.register_model(f"runs:/{run_id}/best_model", "model")
D. mlflow.register_model(model_uri, "model")
E. mlflow.register_model(run_id, "best_model")

Answer: A

NEW QUESTION # 131
......

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