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Databricks Databricks-Machine-Learning-Professional Exam Overview:

Certification Vendor:	Databricks
Exam Name:	Databricks Certified Machine Learning Professional Exam
Exam Number:	Databricks-Machine-Learning-Professional
Exam Duration:	120 minutes
Real Exam Qty:	59
Certificate Validity Period:	2 years
Exam Format:	Multi-select, Multiple choice
Passing Score:	Not publicly disclosed (approximately 70%)
Exam Price:	$200 USD
Available Languages:	English
Related Certifications:	Databricks Certified Machine Learning Associate
Recommended Training:	Machine Learning at Scale Advanced Machine Learning Operations
Exam Registration:	Databricks Certification Portal
Sample Questions:	Databricks Databricks-Machine-Learning-Professional Sample Questions
Exam Way:	Online proctored or in-person test center
Pre Condition:	No mandatory prerequisites; recommended: 6+ months hands-on experience with Databricks ML, SparkML, MLflow, and Python
Official Syllabus URL:	https://www.databricks.com/learn/certification/machine-learning-professional

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

Topic	Details
Topic 1	Identify less performant data storage as a solution for other use cases Describe why complex business logic must be handled in streaming deployments
Topic 2	Identify JIT feature values as a need for real-time deployment Describe how to list all webhooks and how to delete a webhook
Topic 3	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 4	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 5	Describe the advantages of using the pyfunc MLflow flavor Manually log parameters, models, and evaluation metrics using MLflow
Topic 6	Identify the requirements for tracking nested runs Describe an MLflow flavor and the benefits of using MLflow flavors

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

NEW QUESTION # 136
Concept drift is when there is a change in which element?

A. The distribution of the predicted target given by the model
B. The distribution of a target variable
C. The relationship between input variables and target variables
D. The distribution of an input variable

Answer: C

Explanation:
Concept drift occurs when the underlying relationship between input features and the target variable changes over time. This means that even if the feature and label distributions remain stable, the way features influence the target (the predictive relationship) has shifted, leading to reduced model accuracy unless retraining occurs.

NEW QUESTION # 137
Which of the following describes the purpose of the context parameter in the predict method of Python models for MLflow?

A. The context parameter allows the user to specify which version of the registered MLflow Model should be used based on the given application's current scenario
B. The context parameter allows the user to include relevant details of the business case to allow downstream users to understand the purpose of the model
C. The context parameter allows the user to provide the model access to objects like preprocessing models or custom configuration files
D. The context parameter allows the user to document the performance of a model after it has been deployed
E. The context parameter allows the user to provide the model with completely custom if-else logic for the given application's current scenario

Answer: A

NEW QUESTION # 138
A machine learning engineer is migrating a machine learning pipeline to use Data bricks Machine Learning. The pipeline needs to automatically refresh its model each time it runs.
They are using the following code block as part of their solution:

Assuming that this is the first time that the model is being run, which statement describes the impact of the registered_model_name=model_name parameter?

A. It registers the new version of the existing model_name model in the MLflow Model Registry.
B. It registers a new model called model_name in the MLflow Model Registry.
C. It identifies the name of the logged model in the MLflow Experiment.
D. It avoids the need to specify the model name in the subsequent required call to mflow.register_model.

Answer: B

Explanation:
The parameter registered_model_name=model_name in mlflow.pyfunc.log_model() automatically creates and registers a new model in the MLflow Model Registry if it doesn't already exist. Since this is the first time the model is being run, MLflow will register a new model named model_name and log the current model as version 1 of that registered model.

NEW QUESTION # 139
A machine learning engineer wants to view all of the active MLflow Model Registry Webhooks for a specific model.
They are using the following code block:

Which of the following changes does the machine learning engineer need to make to this code block so it will successfully accomplish the task?

A. Replace list with view in the endpoint URL
B. Replace POST with PUT in the call to http request
C. Replace POST with GET in the call to http request
D. Replace list with webhooks in the endpoint URL
E. There are no necessary changes

Answer: D

NEW QUESTION # 140
Which component manages model versions?

A. MLflow Tracking
B. Feature Store
C. Spark ML
D. Model Registry

Answer: D

Explanation:
Model Registry handles:
versioning
stage transitions
governance.

NEW QUESTION # 141
......

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