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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 Price:	$200 USD
Passing Score:	Not publicly disclosed (approximately 70%)
Certificate Validity Period:	2 years
Exam Format:	Multi-select, Multiple choice
Real Exam Qty:	59
Available Languages:	English
Exam Duration:	120 minutes
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	Create, overwrite, merge, and read Feature Store tables in machine learning workflows View Delta table history and load a previous version of a Delta table
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 which code block will trigger a shown webhook Describe the basic purpose and user interactions with Model Registry
Topic 4	Identify live serving benefits of querying precomputed batch predictions Describe Structured Streaming as a common processing tool for ETL pipelines
Topic 5	Describe concept drift and its impact on model efficacy Describe summary statistic monitoring as a simple solution for numeric feature drift
Topic 6	Identify that data can arrive out-of-order with structured streaming Identify how model serving uses one all-purpose cluster for a model deployment
Topic 7	Describe the advantages of using the pyfunc MLflow flavor Manually log parameters, models, and evaluation metrics using MLflow
Topic 8	Identify the requirements for tracking nested runs Describe an MLflow flavor and the benefits of using MLflow flavors
Topic 9	Describe model serving deploys and endpoint for every stage Identify scenarios in which feature drift and or label drift are likely to occur
Topic 10	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 11	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

Databricks Certified Machine Learning Professional Sample Questions (Q175-Q180):

NEW QUESTION # 175
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 code block can they use to accomplish the task?

Answer: D

Explanation:
The correct method for transitioning a model version to a new stage using the MLflow Client is client.transition_model_version_stage(name, version, stage). This updates the stage of a specific model version in the Model Registry, such as moving it from "Staging" to "Production". The stage parameter specifies the target stage only -- there is no from argument.

NEW QUESTION # 176
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: D

NEW QUESTION # 177
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 provide the model access to objects like preprocessing models or custom configuration files
B. 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
C. The context parameter allows the user to provide the model with completely custom if-else logic for the given application's current scenario
D. 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
E. The context parameter allows the user to document the performance of a model after it has been deployed

Answer: B

NEW QUESTION # 178
Which of the following describes label drift?

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

Answer: D

NEW QUESTION # 179
Which of the following describes concept drift?

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

Answer: E

NEW QUESTION # 180
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