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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	Identify the requirements for tracking nested runs Describe an MLflow flavor and the benefits of using MLflow flavors
Topic 3	Identify less performant data storage as a solution for other use cases Describe why complex business logic must be handled in streaming deployments
Topic 4	Describe the advantages of using the pyfunc MLflow flavor Manually log parameters, models, and evaluation metrics using MLflow
Topic 5	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 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 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 8	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

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Databricks Certified Machine Learning Professional Sample Questions (Q24-Q29):

NEW QUESTION # 24
A machine learning engineer would like to compute predictions on inference data as it becomes available through the pipeline in microbatches. The predictions should be stored in a table for query later. Which deployment strategy can the engineer use?

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

Answer: C

Explanation:
A streaming deployment strategy processes incoming data continuously or in microbatches, allowing predictions to be computed as new data arrives. The results can then be written to a table for later querying. This approach balances near-real-time inference with manageable resource use, making it ideal for scenarios where data flows steadily through a pipeline.

NEW QUESTION # 25
A data scientist set up a machine learning pipeline to automatically log feature importance data from a CSV with each run. They now want to view the feature importance data in Databricks.
Which location in Databricks will show this data?

A. The MLflow Model Registry Model page
B. The Artifacts section of the MLflow Run page
C. The Artifacts section of the MLflow Experiment page
D. The Data section of the MLflow Run page/span>

Answer: B

Explanation:
When a data scientist logs feature importance data (e.g., as a CSV or JSON file) using MLflow, it is stored as an artifact of that specific run. These artifacts are visible under the Artifacts section of the MLflow Run page in Databricks, where users can view or download the logged files associated with that particular experiment run.

NEW QUESTION # 26
Which of the following statements describes streaming with Spark as a model deployment strategy?

A. The inference of all types of records in real-time
B. The inference of incrementally processed records as soon as a Spark job is run
C. The inference of batch processed records as soon as a Spark job is run
D. The inference of incrementally processed records as soon as trigger is hit
E. The inference of batch processed records as soon as a trigger is hit

Answer: B

NEW QUESTION # 27
A machine learning engineer wants to deploy a model for real-time serving using MLflow Model Serving. For the model, the machine learning engineer currently has one model version in each of the stages in the MLflow Model Registry. The engineer wants to know which model versions can be queried once Model Serving is enabled for the model. Which of the following lists all of the MLflow Model Registry stages whose model versions are automatically deployed with Model Serving?

A. Staging, Production
B. None, Staging, Production, Archived
C. Staging, Production, Archived
D. Production
E. None, Staging, Production

Answer: A

NEW QUESTION # 28
A Data Scientist is building a product recommendation model that suggests additional items based on a customer's current shopping basket for an online merchant. The model needs to access the real-time basket contents at inference time to avoid recommending items already in the basket. The model will be deployed using Databricks Model Serving. The data scientist now wants to implement real-time feature engineering to incorporate the current basket contents into the model's prediction. Which approach will do this?

A. Store basket data in a Delta table and configure automatic feature lookup during model serving.
B. Write basket data to a feature table and publish to the online store for low-latency access.
C. Create a custom pyfunc model that processes the basket contents as part of the model's predict() method.
D. Implement feature preprocessing in a separate microservice that feeds the model serving endpoint.

Answer: C

Explanation:
Real-time basket contents are request-specific and only available at inference time, making them unsuitable for precomputed feature tables or online stores. Implementing a custom pyfunc model allows the predict() method to directly consume and process the basket contents included in the request payload, enabling real-time feature engineering while keeping the logic tightly coupled with model inference in Databricks Model Serving.

NEW QUESTION # 29
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