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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
Related Certifications:	Databricks Certified Machine Learning Associate
Real Exam Qty:	59
Available Languages:	English
Exam Format:	Multiple choice, Multi-select
Exam Duration:	120 minutes
Certificate Validity Period:	2 years
Passing Score:	Not publicly disclosed (approximately 70%)
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	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 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 JIT feature values as a need for real-time deployment Describe how to list all webhooks and how to delete a webhook
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
Topic 7	Identify live serving benefits of querying precomputed batch predictions Describe Structured Streaming as a common processing tool for ETL pipelines
Topic 8	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 9	Describe concept drift and its impact on model efficacy Describe summary statistic monitoring as a simple solution for numeric feature drift
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	Identify that data can arrive out-of-order with structured streaming Identify how model serving uses one all-purpose cluster for a model deployment

Databricks Certified Machine Learning Professional Sample Questions (Q151-Q156):

NEW QUESTION # 151
Which of the following is an advantage of using the python_function(pyfunc) model flavor over the built-in library-specific model flavors?

A. python_function can be used to deploy models without worrying about which library was used to create the model
B. python_function can be used to store models in an MLmodel file
C. python_function can be used to deploy models in a parallelizable fashion
D. python_function can be used to deploy models without worrying about whether they are deployed in batch, streaming, or real-time environments
E. python_function provides no benefits over the built-in library-specific model flavors

Answer: C

NEW QUESTION # 152
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. Measure the latency of the model's prediction time
B. Run a statistical test to determine if there are changes in the feature variable distribution over time
C. Obtain the observed values (actual) feature values and compare to the predicted values
D. Retrain the model based on any new feature variables that have been added to the feature set

Answer: B

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 # 153
After a data scientist noticed that a column was missing from a production feature set stored as a Delta table, the machine learning engineering team has been tasked with determining when the column was dropped from the feature set. Which SQL command can be used to accomplish this task?

A. TIMESTAMP
B. DESCRIBE HISTORY
C. VERSION
D. DESCRIBE
E. HISTORY

Answer: B

NEW QUESTION # 154
A Machine Learning Engineer needs to deploy a custom model using Databricks Model Serving.
The model requires an external tokenizer file (for example, a vocabulary or pre-trained tokenizer) to function correctly. They need to ensure this tokenizer file is included with the model so it is available during model serving. How should they package this tokenizer file as part of the model deployment?

A. Use the code_paths parameter in mlflow.pyfunc.log_model() to include the tokenizer file, assuming it's part of the custom Python logic needed by the model.
B. Use the artifacts parameter in mlflow.pyfunc.log_model() to package the tokenizer file, referencing its path in Volumes.
C. Include tokenizer file as part of the model's software dependencies by adding it to the conda_env specification.
D. Access the tokenizer file directly from Volumes at model inference time, which requires pre- provisioning access to the Volume for every model serving endpoint.

Answer: B

Explanation:
The artifacts parameter in mlflow.pyfunc.log_model is designed for packaging non-code assets required at inference time, such as tokenizer files. By logging the tokenizer as a model artifact and referencing its path, MLflow ensures the file is versioned with the model and automatically made available to Databricks Model Serving during inference.

NEW QUESTION # 155
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. There are no necessary changes
B. Replace POST with GET in the call to http request
C. Replace list with view in the endpoint URL
D. Replace list with webhooks in the endpoint URL
E. Replace POST with PUT in the call to http request

Answer: B

NEW QUESTION # 156
......

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