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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 Price:	$200 USD
Passing Score:	Not publicly disclosed
Available Languages:	English
Exam Duration:	120 minutes
Exam Format:	Scenario-based questions, Multiple choice, Multiple select
Related Certifications:	Databricks Certified Machine Learning Associate
Real Exam Qty:	Approximately 45–60
Certificate Validity Period:	2 years
Recommended Training:	Databricks Academy Machine Learning Training
Exam Registration:	Databricks Certification Portal
Sample Questions:	Databricks Databricks-Machine-Learning-Professional Sample Questions
Exam Way:	Online proctored exam (typically delivered via Databricks certification partners such as Certiverse or Pearson VUE depending on region and current program structure)
Pre Condition:	Recommended experience with Databricks platform and machine learning workflows; Databricks Certified Machine Learning Associate certification is often recommended but not strictly required.
Official Syllabus URL:	https://www.databricks.com/learn/certification

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Databricks Databricks-Machine-Learning-Professional Exam Syllabus Topics:

Topic	Details
Topic 1	Identify live serving benefits of querying precomputed batch predictions Describe Structured Streaming as a common processing tool for ETL pipelines
Topic 2	Describe the advantages of using the pyfunc MLflow flavor Manually log parameters, models, and evaluation metrics using MLflow
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	Identify which code block will trigger a shown webhook Describe the basic purpose and user interactions with Model Registry
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	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	Identify less performant data storage as a solution for other use cases Describe why complex business logic must be handled in streaming deployments
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

Databricks Certified Machine Learning Professional Sample Questions (Q188-Q193):

NEW QUESTION # 188
A Data Scientist has trained a regression model using Spark's MLlib to predict housing prices.
The model needs to be evaluated to determine how well it predicts on a testing dataset. The Data Scientist wants to use a standard regression metric for the evaluation. They also want to evaluate the model with the least processing overhead required. Which approach will suit their needs?

A. Use the MLlib's RankingEvaluator with the metricName set to rmse to calculate root mean squared error on the testing dataset.
B. Convert the testing dataset to a pandas DataFrame and use scikit-learn's root_mean_squared_error function to calculate the root mean squared error on the testing dataset.
C. Implement a Spark UDF to calculate the root mean squared error for each row in the testing dataset and aggregate these values to obtain the overall RMSE score.
D. Use the MLlib's RegressionEvaluator with the metricName set to rmse to calculate root mean squared error on the testing dataset.

Answer: D

Explanation:
RegressionEvaluator is the Spark ML-native, distributed evaluation API for regression models.
Setting metricName to rmse computes root mean squared error directly on the distributed test DataFrame with minimal overhead, avoiding unnecessary data movement or custom computation logic.

NEW QUESTION # 189
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. At the same time, they would like to archive any model versions that are already in the Production stage. Which code block can they use to accomplish the task?

Answer: D

NEW QUESTION # 190
A machine learning engineer is in the process of implementing a concept drift monitoring solution. They are planning to use the following steps:
1. Deploy a model to production and compute predicted values
2. Obtain the observed (actual) label values
3. _____
4. Run a statistical test to determine if there are changes over time
Which of the following should be completed as Step #3?

A. Measure the latency of the prediction time
B. None of these should be completed as Step #3
C. Retrain the model
D. Compute the evaluation metric using the observed and predicted values
E. Obtain the observed values (actual) feature values

Answer: B

NEW QUESTION # 191
A Machine Learning Engineer is tasked with implementing a simple solution, requiring as little code as possible, to monitor a regression model and ensure that the R2 score does not exceed a certain threshold. If the threshold is exceeded, the solution needs to send an email to a distribution list. Which proposed solution meets the criteria?

A. Configure Lakehouse Monitoring using the Inference profile type. Create a SQL Query that runs on a schedule and evaluates if the R2 score in the profile table exceeds the threshold. If so, the Alert should send an email to the configured email notification destination.
B. Configure Lakehouse Monitoring using the Inference profile type. Create a Workflow that runs on a schedule and executes a SQL query that evaluates the R2 score in the profile table. Use Workflow If/Else condition to execute a Notebook Task that sends the email if the R2 score threshold is exceeded.
C. Configure Lakehouse Monitoring using the Inference profile type. Create a SQL Alert that runs on a schedule and evaluates if the R2 score in the profile table exceeds the threshold. If so, the Alert should send an email to the configured email notification destination.
D. Create a Workflow that runs on a schedule. The Workflow executes a SQL Query that calculates the R2 score. Use Workflow If/Else condition to execute a Notebook Task that sends the email if the R2 score threshold is exceeded.

Answer: C

Explanation:
Lakehouse Monitoring with the Inference profile already computes regression performance metrics such as R2 and stores them in Delta tables. A Databricks SQL Alert can directly evaluate the R2 value against a threshold on a schedule and send an email notification when the condition is met. This requires minimal code and avoids additional workflows or custom logic, making it the simplest and most maintainable solution.

NEW QUESTION # 192
A Data Scientist has created a sales forecasting model, named sales-forecasting. The model is deployed to a model serving endpoint with a schema. They need to invoke this model using Python and would prefer to use a SDK function to make the request. Which method suits these requirements?

A. Import the MLflow Deployments class and use the predict method and provide the endpoint and input parameters.
B. Make an API request to the MODEL_VERSION_URI and provide the dataframe_split as the request parameter.
C. Import the MLflow Deployments class and use the ai_query method and provide the endpoints and request parameters.
D. Use the built in ai_query function and provide the endpoints and request parameters.

Answer: A

Explanation:
The MLflow Deployments SDK provides a predict method specifically designed to invoke Databricks model serving endpoints from Python. This method abstracts the HTTP request details, allows specifying the endpoint name directly, and cleanly passes input parameters that conform to the model's serving schema, making it the preferred SDK-based approach.

NEW QUESTION # 193
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