Valid Databricks-Machine-Learning-Professional Test Questions, Mock Databricks-Machine-Learning-Professional Exam

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

Topic	Details
Topic 1	Identify which code block will trigger a shown webhook Describe the basic purpose and user interactions with Model Registry
Topic 2	Describe the advantages of using the pyfunc MLflow flavor Manually log parameters, models, and evaluation metrics using MLflow
Topic 3	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 4	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 5	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 6	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

>> Valid Databricks-Machine-Learning-Professional Test Questions <<

Mock Databricks-Machine-Learning-Professional Exam - Databricks-Machine-Learning-Professional Exam Questions And Answers

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

NEW QUESTION # 90
A machine learning engineer and data scientist are working together to convert a batch deployment to an always-on streaming deployment. The machine learning engineer has expressed that rigorous data tests must be put in place as a part of their conversion to account for potential changes in data formats.
Which of the following describes why these types of data type tests and checks are particularly important for streaming deployments?

A. Because the streaming deployment is always on, there is no practitioner to debug poor model performance
B. Because the streaming deployment is always on, there is a need to confirm that the deployment can autoscale
C. Because the streaming deployment is always on, all types of data must be handled without producing an error
D. All of these statements
E. None of these statements

Answer: B

NEW QUESTION # 91
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 Production stage of the MLflow Model Registry. Which of the following model URIs can be used to query the described model version?

A. https://<databricks-instance>/model/recommender/Production/invocations
B. https://<databricks-instance>/model/recommender/stage-production/invocations
C. https://<databricks-instance>/model-serving/recommender/Production/invocations
D. The version number of the model version in Production is necessary to complete this task.
E. https://<databricks-instance>/model-serving/recommender/stage-production/invocations

Answer: D

NEW QUESTION # 92
A Machine Learning Engineer has created a custom PyFunc model wrapper for a fraud detection system that needs to be registered in Unity Catalog under the schema risk_models in the production catalog. The model requires specific dependencies and must be accessible for governance and version control. The engineer is working on a dedicated cluster with Unity Catalog enabled, but the model registration is failing. Why is the model failing to be registered in this case?

A. The model signature must be provided, as Unity Catalog requires all models to have signatures.
B. The cluster must be configured with shared access mode instead of dedicated mode.
C. The model artifacts must be stored in DBFS rather than Unity Catalog volumes.
D. The PyFunc model must inherit from mlflow.sklearn.Model instead of mlflow.pyfunc.PythonModel.

Answer: B

Explanation:
Unity Catalog model registration requires the cluster to run in shared access mode. Dedicated (single-user) clusters do not support registering models into Unity Catalog because governance, lineage, and access control enforcement rely on shared-mode execution. As a result, even if Unity Catalog is enabled, model registration will fail when attempted from a dedicated cluster.

NEW QUESTION # 93
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. 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.
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. 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.

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 # 94
After a data scientist noticed that a column was missing from a production feature set stored as a Delta table, the machine learning engineering team has been tasked with determining when the column was dropped from the feature set. Which SQL command can be used to accomplish this task?

A. DESCRIBE HISTORY
B. TIMESTAMP
C. DESCRIBE
D. HISTORY
E. VERSION

Answer: A

NEW QUESTION # 95
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