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

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

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Databricks Certified Machine Learning Professional Sample Questions (Q186-Q191):

NEW QUESTION # 186
Which of the following describes the concept of MLflow Model flavors?

A. A convention that MLflow Model Registry can use to version models
B. A convention that deployment tools can use to wrap preprocessing logic into a Model
C. A convention that MLflow Experiments can use to organize their Runs by project
D. A convention that deployment tools can use to understand the model
E. A convention that MLflow Model Registry can use to organize its Models by project

Answer: C

NEW QUESTION # 187
Which of the following describes concept drift?

A. None of these describe Concept drift
B. Concept drift is when there is a change in the distribution of an input variable
C. Concept drift is when there is a change in the distribution of a target variable
D. Concept drift is when there is a change in the distribution of the predicted target given by the model
E. Concept drift is when there is a change in the relationship between input variables and target variables

Answer: D

NEW QUESTION # 188
Which of the following deployment paradigms can centrally compute predictions for a single record with exceedingly fast results?

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

Answer: E

NEW QUESTION # 189
Which of the following lists all of the model stages are available in the MLflow Model Registry?

A. None. Staging. Production
B. None. Staging. Production. Archived
C. Development. Staging. Production
D. Staging. Production. Archived
E. Development. Staging. Production. Archived

Answer: B

NEW QUESTION # 190
A data scientist set up a machine learning pipeline to automatically log a data visualization with each run. They now want to view the visualizations in Databricks. Which location in Databricks will show these data visualizations?

A. The MLflow Model Registry Model paqe
B. Logged data visualizations cannot be viewed in Databricks
C. The Artifacts section of the MLflow Experiment page
D. The Artifacts section of the MLflow Run page
E. The Figures section of the MLflow Run page

Answer: E

NEW QUESTION # 191
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