Databricks-Machine-Learning-Professional Interactive Course | Exam Databricks-Machine-Learning-Professional Certification Cost
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Databricks Certified Machine Learning Professional Sample Questions (Q112-Q117):
NEW QUESTION # 112
Which of the following machine learning model deployment paradigms is the most common for machine learning projects?
- A. Streaming
- B. Batch
- C. Real-time
- D. None of these deployments
- E. On-device
Answer: A
NEW QUESTION # 113
What tool helps avoid feature mismatch between training and inference?
- A. Spark SQL
- B. Model Registry
- C. MLflow Projects
- D. Feature Store
Answer: D
Explanation:
Feature Store ensures:
same feature computation logic
consistency between training and serving.
NEW QUESTION # 114
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 model version entry in the Unity Catalog that has been transitioned from the "Staging" to the
"Production" alias. - C. The model artifact trained and validated in the staging environment on a subset of the data.
- D. The CI/CD-approved model training source code, which will be executed in the production environment on the full production dataset.
Answer: D
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 # 115
A data scientist would like to enable MLflow Autologging for all machine learning libraries used in a notebook. They want to ensure that MLflow Autologging is used no matter what version of the Databricks Runtime for Machine Learning is used to run the notebook and no matter what workspace-wide configurations are selected in the Admin Console.
Which of the following lines of code can they use to accomplish this task?
- A. It is not possible to automatically log MLflow runs.
- B. spark.conf.set("autologging", True)
- C. mlflow.spark.autolog()
- D. mlflow.autolog()
- E. mlflow.sklearn.autolog()
Answer: B
NEW QUESTION # 116
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. Separating code and tests often leads to decreased reliability and poor code quality.
- B. Managing a more complex project structure can be harder to maintain and navigate.
- C. This change would prevent the use of any non-Python environments like Scala.
- D. It makes functions harder to import and reuse across different notebooks.
Answer: B
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 # 117
......
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