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

Topic	Details
Topic 1	Identify the requirements for tracking nested runs Describe an MLflow flavor and the benefits of using MLflow flavors
Topic 2	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 3	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 4	Describe the advantages of using the pyfunc MLflow flavor Manually log parameters, models, and evaluation metrics using MLflow
Topic 5	Describe concept drift and its impact on model efficacy Describe summary statistic monitoring as a simple solution for numeric feature drift
Topic 6	Identify which code block will trigger a shown webhook Describe the basic purpose and user interactions with Model Registry
Topic 7	Identify JIT feature values as a need for real-time deployment Describe how to list all webhooks and how to delete a webhook

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Databricks Certified Machine Learning Professional Sample Questions (Q186-Q191):

NEW QUESTION # 186
A machine learning engineer has developed a machine learning pipeline that produces a scikit- learn model model and computes the RMSE rmse. MAE mae, and R-squared r2 values for the test set. They now want to log these values with the MLflow run. These values are stored in the dictionary metrics.
They run the following code block:

The code block produces an error.
Which changes to the code block will successfully complete the task?

A. Replace metrics with rmse, mae, r2
B. Replace log_metric with log_metrics
C. Replace metrics with model
D. Replace mlflow.log_metric with mlflow.sklearn.log_metric

Answer: B

Explanation:
The method mlflow.log_metric() logs a single metric, while mlflow.log_metrics() is used to log multiple metrics at once from a dictionary. Since metrics is a dictionary containing rmse, mae, and r2, the correct function is mlflow.log_metrics(metrics).

NEW QUESTION # 187
A Machine Learning Engineer needs to develop fraud detection models with Databricks. They need to ensure seamless collaboration between data engineers and data scientists while maintaining strict governance, version control, and traceability as models progress from development to production. So, they need to choose the Databricks feature that will enable centralized model lineage tracking, cross-workspace access control, and automated synchronization of model versions with their training data. Which Databricks feature will do this?

A. Mosaic AI Model Serving for deploying models as REST APIs
B. MLflow Tracking for logging parameters and metrics during experimentation
C. Lakehouse Monitoring for tracking data quality and model drift
D. Unity Catalog for governing models via a centralized registry with role-based access

Answer: D

Explanation:
Unity Catalog provides centralized governance for models, including lineage tracking between models and their training data, cross-workspace access control, and consistent version management as models move from development to production. This enables secure collaboration, traceability, and enterprise-grade model governance across teams.

NEW QUESTION # 188
How can you save a trained Spark ML PipelineModel?

A. pipeline.persist()
B. pipelineModel.write().save()
C. pipeline.store()
D. pipeline.save()

Answer: B

Explanation:
Example:
pipelineModel.write().overwrite().save("/model")

NEW QUESTION # 189
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 store models in an MLmodel file
B. python_function can be used to deploy models in a parallelizable fashion
C. python_function can be used to deploy models without worrying about whether they are deployed in batch, streaming, or real-time environments
D. python_function provides no benefits over the built-in library-specific model flavors
E. python_function can be used to deploy models without worrying about which library was used to create the model

Answer: B

NEW QUESTION # 190
A machine learning engineer needs to select a deployment strategy for a new machine learning application. The machine learning application requires central prediction computation and exceedingly fast results, but only a handful of predictions need to be computed at a time. Which deployment strategy can be used to meet these requirements?

A. Edge/on-device
B. Batch
C. Streaming
D. Real-time

Answer: D

Explanation:
Real-time deployment is the appropriate strategy when predictions need to be computed centrally with very low latency, even if only a small number of predictions are required at a time. This ensures fast responses for applications requiring immediate inference.

NEW QUESTION # 191
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