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

Section	Weight	Objectives
Topic 1: Model Development	44%	- Feature Store and automated feature pipelines - Advanced MLflow usage - Scalable ML pipelines with SparkML - Distributed training and hyperparameter tuning
Topic 2: Model Deployment	12%	- Custom model serving - Model rollout and version management - Deployment strategies
Topic 3: ML Ops	44%	- Testing and validation strategies - Automated retraining workflows - Model monitoring and drift detection with Lakehouse Monitoring - Environment management with Databricks Asset Bundles

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Databricks Certified Machine Learning Professional Sample Questions (Q136-Q141):

NEW QUESTION # 136
A machine learning engineer wants to programmatically create a new Databricks Job whose schedule depends on the result of some automated tests in a machine learning pipeline.
Which of the following Databricks tools can be used to programmatically create the Job?

A. Databricks REST APIs
B. AutoML APIs
C. MLflow Client
D. Jobs cannot be created programmatically
E. MLflow APIs

Answer: A

NEW QUESTION # 137
A Machine Learning Engineer has created a custom PyFunc model wrapper for a fraud detection system that needs to be registered in Unity Catalog under the schema risk_models in the production catalog. The model requires specific dependencies and must be accessible for governance and version control. The engineer is working on a dedicated cluster with Unity Catalog enabled, but the model registration is failing. Why is the model failing to be registered in this case?

A. The model artifacts must be stored in DBFS rather than Unity Catalog volumes.
B. The model signature must be provided, as Unity Catalog requires all models to have signatures.
C. The PyFunc model must inherit from mlflow.sklearn.Model instead of mlflow.pyfunc.PythonModel.
D. The cluster must be configured with shared access mode instead of dedicated mode.

Answer: D

Explanation:
Unity Catalog model registration requires the cluster to run in shared access mode. Dedicated (single-user) clusters do not support registering models into Unity Catalog because governance, lineage, and access control enforcement rely on shared-mode execution. As a result, even if Unity Catalog is enabled, model registration will fail when attempted from a dedicated cluster.

NEW QUESTION # 138
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. This can only be viewed in the MLflow Experiments UI
B. mlflow.sklearn.load_model(model_uri)
C. mlflow.load_model(model_uri)
D. client.list_artifacts(run_id)["feature-importances.csv"]
E. client.pyfunc.load_model(model_uri)

Answer: B

NEW QUESTION # 139
How can you save a trained Spark ML PipelineModel?

A. pipeline.persist()
B. pipeline.save()
C. pipeline.store()
D. pipelineModel.write().save()

Answer: D

Explanation:
Example:
pipelineModel.write().overwrite().save("/model")

NEW QUESTION # 140
Which of the following deployment paradigms can centrally compute predictions for a single record with exceedingly fast results?

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

Answer: D

NEW QUESTION # 141
......

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