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

Section	Weight	Objectives
Topic 1: Model Deployment	12%	- Model rollout and version management - Deployment strategies - Custom model serving
Topic 2: ML Ops	44%	- Automated retraining workflows - Testing and validation strategies - Environment management with Databricks Asset Bundles - Model monitoring and drift detection with Lakehouse Monitoring
Topic 3: Model Development	44%	- Scalable ML pipelines with SparkML - Feature Store and automated feature pipelines - Distributed training and hyperparameter tuning - Advanced MLflow usage

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Databricks Certified Machine Learning Professional Sample Questions (Q169-Q174):

NEW QUESTION # 169
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. Use the built in ai_query function and provide the endpoints and request 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 predict method and provide the endpoint and input parameters.
D. Import the MLflow Deployments class and use the ai_query method and provide the endpoints and request parameters.

Answer: C

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 # 170
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 of the following Databricks tools can be used to programmatically create the Job?

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

Answer: C

NEW QUESTION # 171
A Machine Learning Engineer has trained a credit scoring model and needs to evaluate fairness metrics across different customer segments while maintaining different levels of granularity for business reporting. They need to compute metrics like precision, recall, and demographic parity at the individual feature level (credit_score_range, income_bracket) as well as intersectional slices (combinations of features). The model outputs are stored in a Delta table with prediction probabilities and actual default labels. The engineer wants to systematically evaluate model performance across these various feature slices and granularities. Which approach will do this?

A. Create Lakehouse Monitoring with slicing expressions for individual features and intersection conditions.
B. Implement custom MLflow evaluation functions that iterate through all possible feature combinations.
C. Build separate Spark DataFrames for each slice and compute metrics using standard DataFrame operations.
D. Use Unity Catalog metric views with dimensions defined for each feature and measures for the fairness metrics.

Answer: A

Explanation:
Lakehouse Monitoring supports defining slicing expressions on one or more columns, allowing metrics such as precision, recall, and fairness indicators to be computed at both individual feature levels and intersectional combinations. This provides a systematic, scalable, and governed way to evaluate model performance and fairness across multiple granularities directly from Delta tables without custom metric pipelines.

NEW QUESTION # 172
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. Replace list with webhooks in the endpoint URL
B. Replace list with view in the endpoint URL
C. Replace POST with GET in the call to http request
D. Replace POST with PUT in the call to http request
E. There are no necessary changes

Answer: C

NEW QUESTION # 173
A data scientist has developed and logged a scikit-learn random forest model model, and then they ended their Spark session and terminated their cluster. After starting a new cluster, they want to review the feature_importances_ of the original model object.
Which of the following lines of code can be used to restore the model object so that feature_importances_ is available?

A. This can only be viewed in the MLflow Experiments UI
B. client.list_artifacts(run_id)["feature-importances.csv"]
C. client.pyfunc.load_model(model_uri)
D. mlflow.load_model(model_uri)
E. mlflow.sklearn.load_model(model_uri)

Answer: D

NEW QUESTION # 174
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