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

Topic	Details
Topic 1	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 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 live serving benefits of querying precomputed batch predictions Describe Structured Streaming as a common processing tool for ETL pipelines
Topic 4	Describe the advantages of using the pyfunc MLflow flavor Manually log parameters, models, and evaluation metrics using MLflow
Topic 5	Identify which code block will trigger a shown webhook Describe the basic purpose and user interactions with Model Registry
Topic 6	Describe concept drift and its impact on model efficacy Describe summary statistic monitoring as a simple solution for numeric feature drift
Topic 7	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 8	Identify less performant data storage as a solution for other use cases Describe why complex business logic must be handled in streaming deployments

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Databricks Certified Machine Learning Professional Sample Questions (Q144-Q149):

NEW QUESTION # 144
A machine learning engineer has deployed a model recommender using MLflow Model Serving.
They now want to query the version of that model that is in the Staging stage of the MLflow Model Registry. Which model URI can be used to query the described model version?

A. It is not possible to query a model version in the Staging stage.
B. The version number of the model version in Production is necessary to complete this task.
C. https://<databricks-instance>/model/recommender/Staging/invocations
D. https://<databricks-instance>/model-serving/recommender/Staging/invocations

Answer: C

Explanation:
To query a model version in a specific stage (like Staging) using MLflow Model Serving on Databricks, the correct model URI format is:
https://<databricks-instance>/model/<model-name>/<stage>/invocations
So, for the model named recommender in the Staging stage, the correct URI is:
https://<databricks-instance>/model/recommender/Staging/invocations.

NEW QUESTION # 145
Which is a benefit of logging an input example with an MLflow model?

A. The environment requirements are packaged with the MLmodel file
B. An example record is available to test serving applications
C. An example prediction is available to test post-inference pipelines
D. The model evaluation metrics are stored with the model

Answer: B

Explanation:
Logging an input example with an MLflow model provides a concrete sample of the data format expected by the model. This helps serving applications and users understand the model's input structure and verify that inference requests are properly formatted when deploying or testing the model in production.

NEW QUESTION # 146
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. Obtain the observed values (actual) feature values
B. Compute the evaluation metric using the observed and predicted values
C. None of these should be completed as Step #3
D. Retrain the model
E. Measure the latency of the prediction time

Answer: C

NEW QUESTION # 147
Which of the following is an obstacle related to streaming machine learning applications?

A. Out-of-order data
B. End-to-end fault tolerance
C. All of these
D. None of these

Answer: C

Explanation:
Streaming machine learning applications face multiple challenges, including end-to-end fault tolerance (ensuring recovery from failures without data loss) and out-of-order data (handling events that arrive late or out of sequence). Both are common obstacles in building reliable real- time ML systems.

NEW QUESTION # 148
A Machine Learning Engineer is responsible for maintaining a fraud detection model deployed on Databricks. They want to implement a retraining pipeline that automatically starts when the model's F1 score drops below a threshold or when input feature distributions change significantly.
Which two actions should the engineer take to implement this automated retraining? (Choose two.)

A. Schedule a recurring query on the Lakehouse monitoring table.
B. Use MLflow to manually log metrics and retrain the model offline.
C. Use Databricks SQL to create alerts on model performance and data drift metrics stored in Delta tables.
D. Set up a manual retraining schedule to run every week regardless of alerts.
E. Configure these alerts to send webhook notifications that trigger the model training job.

Answer: C,E

Explanation:
Databricks Lakehouse Monitoring stores model performance and data drift metrics in Delta tables, which can be monitored using Databricks SQL alerts. By creating alerts on F1 score degradation or significant feature drift and configuring those alerts to send webhook notifications, the engineer can automatically trigger a retraining job whenever predefined conditions are met, enabling event-driven, automated retraining aligned with MLOps best practices.

NEW QUESTION # 149
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