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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
Certificate Validity Period:	2 years
Exam Duration:	120 minutes
Exam Price:	USD 200
Exam Format:	Multiple choice
Real Exam Qty:	60
Passing Score:	70%
Related Certifications:	Databricks Certified Machine Learning Associate
Available Languages:	English
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	Describe model serving deploys and endpoint for every stage Identify scenarios in which feature drift and or label drift are likely to occur
Topic 2	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 3	Identify the requirements for tracking nested runs Describe an MLflow flavor and the benefits of using MLflow flavors
Topic 4	Identify less performant data storage as a solution for other use cases Describe why complex business logic must be handled in streaming deployments
Topic 5	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 6	Identify live serving benefits of querying precomputed batch predictions Describe Structured Streaming as a common processing tool for ETL pipelines
Topic 7	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 (Q10-Q15):

NEW QUESTION # 10
A Data Scientist is building a product recommendation model that suggests additional items based on a customer's current shopping basket for an online merchant. The model needs to access the real-time basket contents at inference time to avoid recommending items already in the basket. The model will be deployed using Databricks Model Serving. The data scientist now wants to implement real-time feature engineering to incorporate the current basket contents into the model's prediction. Which approach will do this?

A. Implement feature preprocessing in a separate microservice that feeds the model serving endpoint.
B. Store basket data in a Delta table and configure automatic feature lookup during model serving.
C. Create a custom pyfunc model that processes the basket contents as part of the model's predict() method.
D. Write basket data to a feature table and publish to the online store for low-latency access.

Answer: C

Explanation:
Real-time basket contents are request-specific and only available at inference time, making them unsuitable for precomputed feature tables or online stores. Implementing a custom pyfunc model allows the predict() method to directly consume and process the basket contents included in the request payload, enabling real-time feature engineering while keeping the logic tightly coupled with model inference in Databricks Model Serving.

NEW QUESTION # 11
Which of the following describes label drift?

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

Answer: A

NEW QUESTION # 12
A machine learning engineer wants to programmatically create a new Databricks Job whose schedule depends on the result of some automated tests in a machine learning pipeline. Which Databricks tool can be used to programmatically create the Job?

A. AutoML APIs
B. Databricks REST APIs
C. MLflow Client
D. MLflow APIs
E. Jobs cannot be created programmatically

Answer: B

NEW QUESTION # 13
A machine learning engineer has created a webhook with the following code block:

Which of the following code blocks will trigger this webhook to run the associate job?

Answer: C

NEW QUESTION # 14
A data scientist has computed updated rows that contain new feature values for primary keys already stored in the Feature Store table features. The updated feature values are stored in the DataFrame features_df. They want to update the rows in features if the associated primary key is in features_df. If a row's primary key is not in features_df, they want the row to remain unchanged in features. Which code block can they use to perform this task using the Feature Store Client fs?

Answer: C

Explanation:
To update existing rows based on primary keys while leaving other rows unchanged, the correct mode to use with fs.write_table() is "merge". This performs an upsert operation - updating rows where keys match and keeping others intact - making it ideal for updating feature values in a Feature Store table.

NEW QUESTION # 15
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