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

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

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Databricks-Machine-Learning-Professional Actual Lab Questions: Databricks Certified Machine Learning Professional & Databricks-Machine-Learning-Professional Study Guide

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Databricks Certified Machine Learning Professional Sample Questions (Q25-Q30):

NEW QUESTION # 25
A machine learning engineer has developed a random forest model using scikit-learn, logged the model using MLflow as random_forest_model, and stored its run ID in the run_id Python variable. They now want to deploy that model by performing batch inference on a Spark DataFrame spark_df.
Which of the following code blocks can they use to create a function called predict that they can use to complete the task?

A.
B. It is not possible to deploy a scikit-learn model on a Spark DataFrame.
C.
D.
E.

Answer: C

NEW QUESTION # 26
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 Databricks tool can be used to programmatically create the Job?

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

Answer: C

NEW QUESTION # 27
Which MLflow command logs a trained model?

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

Answer: D

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

NEW QUESTION # 28
A machine learning engineer wants to move their model version model_version for the MLflow Model Registry model model from the Staging stage to the Production stage using MLflow Client client. Which of the following code blocks can they use to accomplish the task?

Answer: E

NEW QUESTION # 29
A Machine Learning Engineer wants to implement MLOps. They currently have three environments: dev, stage, and prod. They have many private PyPI packages that they use to train their models, and they are concerned that their environments will not be consistent between dev, stage, and prod. The engineer needs to follow enterprise scaling and CI/CD best practices to ensure that their environments are consistent. Which approach will do this?

A. Use the same DBR for ML runtime across environments and %pip install any additional packages at the top of the notebook.
B. Define an all purpose cluster with the private PyPI packages installed, and use the same cluster config across environments.
C. Define dependencies for each task in their job(s) running the ML pipeline using Databricks Asset Bundles or Terraform.
D. Use the same DBR across environments.

Answer: C

Explanation:
Defining dependencies at the job or task level using Databricks Asset Bundles or Terraform aligns with enterprise MLOps and CI/CD best practices. This approach makes environment configuration declarative, version-controlled, and reproducible across dev, stage, and prod, ensuring consistent use of private PyPI packages without relying on mutable clusters or manual installation steps.

NEW QUESTION # 30
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