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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: ML Ops	44%	- Model monitoring and drift detection with Lakehouse Monitoring - Environment management with Databricks Asset Bundles - Testing and validation strategies - Automated retraining workflows
Topic 3: Model Development	44%	- Scalable ML pipelines with SparkML - Advanced MLflow usage - Distributed training and hyperparameter tuning - Feature Store and automated feature pipelines

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Databricks Certified Machine Learning Professional Sample Questions (Q43-Q48):

NEW QUESTION # 43
Why is Delta Lake time travel useful in ML pipelines?

A. Reproducible training datasets
B. Smaller datasets
C. Faster model inference
D. Model tuning

Answer: A

Explanation:
Time travel allows training on exact historical datasets.

NEW QUESTION # 44
Which of the following describes the concept of MLflow Model flavors?

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

Answer: B

NEW QUESTION # 45
In a continuous integration, continuous deployment (CI/CD) process for machine learning pipelines, which of the following events commonly triggers the execution of automated testing?

A. The launch of a new cost-efficient job cluster
B. The arrival of a new feature table in the Feature Store
C. The launch of a new cost-efficient SQL endpoint
D. The arrival of a new model version in the MLflow Model Registry
E. CI/CD pipelines are not needed for machine learning pipelines

Answer: A

NEW QUESTION # 46
A data scientist wants to track the runs of their random forest model. The data scientist is changing the number of trees and the maximum depth of the trees in the forest across each run.
They write the following code block:

Which Python object type does params need to be an instance of?

A. array
B. dict
C. list
D. PySpark DataFrarne

Answer: B

Explanation:
The params variable must be a dictionary (dict) because mlflow.log_params() expects a dictionary where each key-value pair represents a parameter name and its corresponding value.
Additionally, the model instantiation RandomForestRegressor(**params) also requires params to be a dictionary to unpack the parameters correctly.

NEW QUESTION # 47
Which of the following is an advantage of using the python_function(pyfunc) model flavor over the built-in library-specific model flavors?

A. python_function provides no benefits over the built-in library-specific model flavors
B. python_function can be used to deploy models without worrying about which library was used to create the model
C. python_function can be used to store models in an MLmodel file
D. python_function can be used to deploy models without worrying about whether they are deployed in batch, streaming, or real-time environments
E. python_function can be used to deploy models in a parallelizable fashion

Answer: E

NEW QUESTION # 48
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