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Databricks Databricks-Machine-Learning-Professional Exam Overview:

Certification Vendor:	Databricks
Exam Name:	Databricks Certified Machine Learning Professional
Exam Number:	Databricks-Machine-Learning-Professional
Exam Format:	Multiple choice
Exam Price:	USD 200
Exam Duration:	120 minutes
Available Languages:	English
Related Certifications:	Databricks Certified Machine Learning Associate
Certificate Validity Period:	2 years
Passing Score:	70%
Real Exam Qty:	60
Sample Questions:	Databricks Databricks-Machine-Learning-Professional Sample Questions
Exam Way:	Online (proctored) or Test Center
Pre Condition:	No formal prerequisites, but 1+ years of hands-on experience performing the machine learning tasks outlined in the exam guide is highly recommended. Recommended courses: Machine Learning at Scale and Advanced Machine Learning Operations (instructor-led or self-paced via Databricks Academy).
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 the requirements for tracking nested runs Describe an MLflow flavor and the benefits of using MLflow flavors
Topic 2	Describe model serving deploys and endpoint for every stage Identify scenarios in which feature drift and or label drift are likely to occur
Topic 3	Identify which code block will trigger a shown webhook Describe the basic purpose and user interactions with Model Registry
Topic 4	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 5	Identify live serving benefits of querying precomputed batch predictions Describe Structured Streaming as a common processing tool for ETL pipelines
Topic 6	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 7	Describe the advantages of using the pyfunc MLflow flavor Manually log parameters, models, and evaluation metrics using MLflow
Topic 8	Identify JIT feature values as a need for real-time deployment Describe how to list all webhooks and how to delete a webhook
Topic 9	Identify less performant data storage as a solution for other use cases Describe why complex business logic must be handled in streaming deployments
Topic 10	Describe concept drift and its impact on model efficacy Describe summary statistic monitoring as a simple solution for numeric feature drift

Databricks Certified Machine Learning Professional Sample Questions (Q140-Q145):

NEW QUESTION # 140
A data scientist has developed a scikit-learn random forest model model, but they have not yet logged model with MLflow. They want to obtain the input schema and the output schema of the model so they can document what type of data is expected as input. Which of the following MLflow operations can be used to perform this task?

A. There is no way to obtain the input schema and the output schema of an unlogged model.
B. mlflow.models.Model.get_input_schema
C. mlflow.models.schema.infer_schema
D. mlflow.models.Model.signature
E. mlflow.models.signature.infer_signature

Answer: E

NEW QUESTION # 141
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 include relevant details of the business case to allow downstream users to understand the purpose of the model
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 access to objects like preprocessing models or custom configuration files
D. The context parameter allows the user to provide the model with completely custom if-else logic for the given application's current scenario
E. The context parameter allows the user to document the performance of a model after it has been deployed

Answer: B

NEW QUESTION # 142
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 # 143
In order to connect an MLflow Model Registry Webhook to a Databricks Job, the Job ID must be provided to the code block used to create the webhook. Which approach can be used to obtain a Databricks Job ID?

A. The Job ID field returned in a query using the Jobs API
B. The Job ID field in the Jobs page in the UI
C. The Job ID field in the Job details section of the specific Job page in the UI
D. All of these approaches

Answer: D

Explanation:
A Databricks Job ID can be obtained in multiple ways - it is displayed directly in the Jobs page, in the Job details section of a specific Job, and can also be retrieved programmatically through the Databricks Jobs API. Any of these methods can be used to supply the Job ID when configuring an MLflow Model Registry Webhook.

NEW QUESTION # 144
Which tool can be used to automatically start a testing Job when a new version of an MLflow Model Registry model is registered?

A. MLflow Model Registry UI
B. MLflow REST API
C. MLflow Model Registry Webhooks
D. MLflow Client API

Answer: C

Explanation:
MLflow Model Registry Webhooks can be configured to automatically trigger actions - such as running a testing job - when events occur, like registering a new model version. This enables automation in CI/CD workflows for machine learning models.

NEW QUESTION # 145
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