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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 Duration:	120 minutes
Exam Format:	Multiple choice
Available Languages:	English
Certificate Validity Period:	2 years
Exam Price:	USD 200
Related Certifications:	Databricks Certified Machine Learning Associate
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	Describe concept drift and its impact on model efficacy Describe summary statistic monitoring as a simple solution for numeric feature drift
Topic 2	Identify the requirements for tracking nested runs Describe an MLflow flavor and the benefits of using MLflow flavors
Topic 3	Identify JIT feature values as a need for real-time deployment Describe how to list all webhooks and how to delete a webhook
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	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 6	Identify which code block will trigger a shown webhook Describe the basic purpose and user interactions with Model Registry
Topic 7	Identify live serving benefits of querying precomputed batch predictions Describe Structured Streaming as a common processing tool for ETL pipelines
Topic 8	Describe model serving deploys and endpoint for every stage Identify scenarios in which feature drift and or label drift are likely to occur
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 the advantages of using the pyfunc MLflow flavor Manually log parameters, models, and evaluation metrics using MLflow
Topic 11	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

Databricks Certified Machine Learning Professional Sample Questions (Q161-Q166):

NEW QUESTION # 161
A machine learning engineering manager has asked all of the engineers on their team to add text descriptions to each of the model projects in the MLflow Model Registry. They are starting with the model project "model" and they'd like to add the text in the model_description variable.
The team is using the following line of code:

Which change does the team need to make to the above code block to accomplish the task?

A. Replace description with artifact
B. Replace update_registered_model with update_model_version
C. There no changes necessary
D. Add a Python model as an argument to update_registered_model
E. Replace client.update_registered_model with mlflow

Answer: C

NEW QUESTION # 162
A machine learning engineer is using the following code block as part of a batch deployment pipeline:

Which of the following changes needs to be made so this code block will work when the inference table is a stream source?

A. Replace schema(schema) with option("maxFilesPerTriqqer", 1}
B. Replace formatfdelta") with format("stream")
C. Replace "inference" with the path to the location of the Delta table
D. Replace predict with a stream-friendly prediction function
E. Replace spark.read with spark.readStream

Answer: A

NEW QUESTION # 163
A Machine Learning Engineer is using Lakehouse Monitoring to track the performance of ML models deployed in their environment. They want to monitor significant distributional drift in categorical features with a metric bounded on [0,1] for easy interpretation. Which statistical method should they use?

A. Wasserstein Distance
B. Jensen-Shannon Distance
C. Kolmogorov-Smirnov Test
D. Kullback-Leibler Distance

Answer: B

Explanation:
Jensen-Shannon Distance is well suited for measuring distributional drift in categorical features. It is symmetric, numerically stable, and bounded between 0 and 1, which makes it easy to interpret and ideal for monitoring categorical feature drift in Lakehouse Monitoring.

NEW QUESTION # 164
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 arrival of a new feature table in the Feature Store
B. The launch of a new cost-efficient job cluster
C. The arrival of a new model version in the MLflow Model Registry
D. CI/CD pipelines are not needed for machine learning pipelines
E. The launch of a new cost-efficient SQL endpoint

Answer: B

NEW QUESTION # 165
A machine learning engineer is in the process of implementing a feature drift monitoring solution.
They are planning to use the following steps:
1. Measure the distributions of each feature variable in the training
set
2. Deploy a model to production
3. Measure the distributions of each feature variable in inference
4. _______
Which action should be completed as Step #4?

A. Obtain the observed values (actual) feature values and compare to the predicted values
B. Retrain the model based on any new feature variables that have been added to the feature set
C. Run a statistical test to determine if there are changes in the feature variable distribution over time
D. Measure the latency of the model's prediction time

Answer: C

Explanation:
The final step in a feature drift monitoring solution is to run a statistical test (e.g., Kolmogorov- Smirnov test) to determine whether the feature distributions in production have significantly diverged from those in the training set. This helps detect drift and maintain model reliability.

NEW QUESTION # 166
......

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