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

Topic	Details
Topic 1	Describe concept drift and its impact on model efficacy Describe summary statistic monitoring as a simple solution for numeric feature drift
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	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 5	Identify which code block will trigger a shown webhook Describe the basic purpose and user interactions with Model Registry
Topic 6	Describe model serving deploys and endpoint for every stage Identify scenarios in which feature drift and or label drift are likely to occur
Topic 7	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 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
Topic 9	Identify less performant data storage as a solution for other use cases Describe why complex business logic must be handled in streaming deployments
Topic 10	Identify the requirements for tracking nested runs Describe an MLflow flavor and the benefits of using MLflow flavors
Topic 11	Identify live serving benefits of querying precomputed batch predictions Describe Structured Streaming as a common processing tool for ETL pipelines

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Databricks Certified Machine Learning Professional Sample Questions (Q115-Q120):

NEW QUESTION # 115
A data scientist has computed updated rows that contain new feature values for primary keys already stored in the Feature Store table features. The updated feature values are stored in the DataFrame features_df. They want to update the rows in features if the associated primary key is in features_df. If a row's primary key is not in features_df, they want the row to remain unchanged in features. Which code block can they use to perform this task using the Feature Store Client fs?

Answer: B

Explanation:
To update existing rows based on primary keys while leaving other rows unchanged, the correct mode to use with fs.write_table() is "merge". This performs an upsert operation - updating rows where keys match and keeping others intact - making it ideal for updating feature values in a Feature Store table.

NEW QUESTION # 116
A Machine Learning Engineer has a real-time fraud detection model deployed that approves or blocks millions of transactions daily. They need to deploy a new version of the model with improved detection accuracy to this high-traffic, business-critical application. Because any model downtime could result in lost revenue or customer dissatisfaction, the engineer must ensure zero downtime and minimal disruption for end users. Leadership also requires that any rollback to the previous version be immediate if issues are detected with the new model in production. Which deployment strategy meets these requirements?

A. Use a blue-green deployment for the new model, maintaining two separate production environments (one "blue," one "green") and switching user traffic to the new version only after confirming it is healthy, enabling instant rollback if needed.
B. Deploy the new model in parallel with the old version, monitor both for a set period, and then notify all stakeholders to manually switch over to the new version at a coordinated time.
C. Use a canary deployment by initially routing a small percentage of user traffic to the new model version, monitoring results, and gradually increasing exposure until all traffic uses the new version if no problems are detected.
D. Replace the current production model with the new version during a scheduled maintenance window, notify affected users of potential brief disruptions, and prepare to reroute requests to a backup if failures occur after deployment.

Answer: A

Explanation:
A blue-green deployment maintains two fully operational production environments and shifts traffic between them instantly. This approach provides zero downtime during deployment and allows immediate rollback to the previous model version if issues arise, which is critical for high- traffic, business-critical real-time systems.

NEW QUESTION # 117
A Machine Learning Engineer is implementing integration tests for an ML pipeline in Databricks.
The current integration test runs the complete workflow but takes four hours to execute due to large dataset processing and extensive model training. They need to select an approach that will be the most effective for optimizing integration test execution while maintaining test reliability. The approach should also be based on MLOps best practices. Which approach will do this?

A. Replace integration tests with unit tests for each pipeline component to reduce execution time and focus on individual component validation in a staging environment that closely matches production.
B. Skip the model training phase entirely and only test data transformations and API endpoints using mock model predictions.
C. Run integration tests only in the production environment using full datasets to ensure complete accuracy of the testing process.
D. Use small subsets of production-like data and reduce training iterations while maintaining the same pipeline structure and validation checkpoints in a staging environment that closely matches production.

Answer: D

Explanation:
Using smaller, production-like datasets and reduced training iterations preserves the full pipeline structure while significantly reducing execution time. This aligns with MLOps best practices by maintaining high-fidelity integration testing in a staging environment that mirrors production behavior, without the cost and delay of running full-scale training workloads.

NEW QUESTION # 118
A data scientist has developed a scikit-learn model sklearn_model and they want to log the model using MLflow.
They write the following incomplete code block:

Which of the following lines of code can be used to fill in the blank so the code block can successfully complete the task?

A. mlflow.spark.log_model(sklearn_model, "model")
B. mlflow.sklearn.log_model(sklearn_model, "model")
C. mlflow.sklearn.load_model("model")
D. mlflow.sklearn.track_model(sklearn_model, "model")
E. mlflow.spark.track_model(sklearn_model, "model")

Answer: E

NEW QUESTION # 119
What does an Estimator do in Spark ML?

A. Transforms a dataset
B. Stores features
C. Evaluates model performance
D. Trains a model using data

Answer: D

Explanation:
An Estimator learns from data and produces a Transformer.

NEW QUESTION # 120
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