AI-300 Exam Cram Review, AI-300 Passing Score

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Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions (Q43-Q48):

NEW QUESTION # 43
Hotspot Question
A team trains an MLflow model that scores customer churn risk. The model will be consumed by different downstream systems.
One system requests predictions synchronously during customer interactions.
Another system submits files containing millions of records for scheduled scoring.
You need to deploy the model by using managed inference options that match each usage pattern.
Which option should you use for each usage pattern? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:


NEW QUESTION # 44
A company requires that only models meeting predefined performance thresholds are registered and deployed. The solution must be fully automated within the ML workflow. What should you implement?

Answer: D

Explanation:
Conditional logic in pipelines allows automated decisions based on evaluation metrics, ensuring only models that meet performance thresholds are registered and deployed. This maintains full automation. Manual approval steps interrupt automation and are not scalable in continuous integration and deployment workflows.


NEW QUESTION # 45
Drag and Drop Question
A team deploys a machine learning model to a managed online endpoint. The team monitors model performance and data quality metrics in production.
When monitoring thresholds are exceeded, the team requires an automated operational response that notifies downstream systems.
You need to configure the monitoring solution to meet the requirements.
Which configuration should you associate with each requirement as a first step? To answer, move the appropriate configurations to the correct requirements. You may use each configuration once, more than once, or not at all. You may need to move the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:


NEW QUESTION # 46
You create a binary classification model. You use the Fairlearn package to assess model fairness.
You must eliminate the need to retrain the model.
You need to implement the Fairlearn package.
Which algorithm should you use?

Answer: D

Explanation:
The best algorithm to implement within the Fairlearn package for a binary classification model without retraining is the ThresholdOptimizer.
Why ThresholdOptimizer?
This algorithm is a post-processing technique specifically designed to mitigate unfairness after a model has already been trained. By setting the prefit parameter to True, it treats your existing model as a "black box," adjusting its decision thresholds for different groups to satisfy fairness constraints (like demographic parity or equalized odds) without changing the underlying model weights.
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/concept-fairness-ml


NEW QUESTION # 47
A team manages an Azure Machine Learning workspace and deploys a model to an endpoint.
A deployed online endpoint shows inconsistent response times during periods of high traffic.
You need to identify potential performance degradation.
Which three metrics should you monitor? Each correct answer presents part of the solution.
Choose three.
NOTE: Each correct selection is worth one point.

Answer: B,D,E

Explanation:
To locate potential performance degradation in an Azure Machine Learning online endpoint during high traffic, you should monitor these three metrics:
Requests per minute: This metric tracks the volume of incoming traffic and helps identify if spikes in load correlate with slower response times.
Connections active: This monitors the total number of concurrent TCP connections from clients, which can indicate if the endpoint is reaching its capacity limits during peak periods.
Request latency: This directly measures the time taken to respond to requests, allowing you to observe exactly when and by how much performance is degrading.
Reference:
https://oneuptime.com/blog/post/2026-02-16-how-to-deploy-a-machine-learning-model-as-a-real- time-endpoint-in-azure-machine-learning/view


NEW QUESTION # 48
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