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

Topic	Details
Topic 1	Describe model serving deploys and endpoint for every stage Identify scenarios in which feature drift and or label drift are likely to occur
Topic 2	Identify live serving benefits of querying precomputed batch predictions Describe Structured Streaming as a common processing tool for ETL pipelines
Topic 3	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 4	Identify which code block will trigger a shown webhook Describe the basic purpose and user interactions with Model Registry
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 the requirements for tracking nested runs Describe an MLflow flavor and the benefits of using MLflow flavors
Topic 8	Identify JIT feature values as a need for real-time deployment Describe how to list all webhooks and how to delete a webhook
Topic 9	Describe the advantages of using the pyfunc MLflow flavor Manually log parameters, models, and evaluation metrics using MLflow

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Databricks Certified Machine Learning Professional Sample Questions (Q90-Q95):

NEW QUESTION # 90
A Machine Learning Engineer is considering moving their functions and unit tests from notebooks into separate Python files (e.g., modules and test scripts) to take advantage of the numerous benefits of this approach like automated execution, code reusability, and version control. Which challenge should the engineer consider with this approach?

A. Managing a more complex project structure can be harder to maintain and navigate.
B. Separating code and tests often leads to decreased reliability and poor code quality.
C. It makes functions harder to import and reuse across different notebooks.
D. This change would prevent the use of any non-Python environments like Scala.

Answer: A

Explanation:
Moving code and unit tests into separate Python modules introduces a more structured project layout, which can increase complexity. Engineers must manage directories, dependencies, and imports carefully, making the project slightly harder to navigate and maintain compared to simple notebook-based workflows, especially for teams new to this structure.

NEW QUESTION # 91
Which MLflow function registers a model to the registry?

A. mlflow.deploy_model()
B. mlflow.log_param()
C. mlflow.register_model()
D. mlflow.create_model()

Answer: C

Explanation:
mlflow.register_model() promotes a model into the Model Registry.

NEW QUESTION # 92
A Machine Learning Engineer is using joblibspark and MLflowCallback to perform a distributed hyperparameter tuning experiment via Optuna. Assuming they have a single objective they are optimizing for, what will be the default sampler implemented by Optuna?

A. GridSampler
B. TPESampler
C. NSGAIISampler
D. RandomSampler

Answer: B

Explanation:
For single-objective optimization, Optuna uses the Tree-structured Parzen Estimator (TPE) sampler by default. TPESampler is a Bayesian optimization method that efficiently explores the hyperparameter space by modeling promising parameter regions, making it well suited for scalable hyperparameter tuning with joblibspark and MLflowCallback.

NEW QUESTION # 93
A data scientist set up a machine learning pipeline to automatically log a data visualization with each run. They now want to view the visualizations in Databricks.
Which of the following locations in Databricks will show these data visualizations?

A. The Figures section of the MLflow Run page
B. The Artifacts section of the MLflow Run page
C. The Artifacts section of the MLflow Experiment page
D. Logged data visualizations cannot be viewed in Databricks
E. The MLflow Model Registry Model paqe

Answer: A

NEW QUESTION # 94
A Machine Learning Engineer needs to build a credit risk model using Databricks. Due to strict data governance, production data cannot be accessed from development or staging environments. To manage MLOps, the engineer uses a "deploy code" strategy with separate development, staging, and production environments mapped to different catalogs in Unity Catalog. The CI/CD pipeline automates environment transitions. What is the primary architectural component promoted from staging to production to generate the final, production-ready model in this scenario?

A. A containerized image of the complete inference pipeline, including feature transformations.
B. The CI/CD-approved model training source code, which will be executed in the production environment on the full production dataset.
C. The model artifact trained and validated in the staging environment on a subset of the data.
D. The model version entry in the Unity Catalog that has been transitioned from the "Staging" to the
"Production" alias.

Answer: B

Explanation:
In a deploy-code strategy, models are not promoted as trained artifacts across environments.
Instead, the approved training code is promoted through CI/CD and re-executed in the production environment against production data to produce the final model. This approach satisfies strict data governance requirements by ensuring production data is only accessed in production while maintaining reproducibility and traceability.

NEW QUESTION # 95
......

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