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Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions (Q33-Q38):
NEW QUESTION # 33
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?
- A. fairlearn.postprocessing.ThresholdOptimizer
- B. fairiearn.reductions.ExponentiatedGradient
- C. fairlearn.preprocessing.CorrelationRemover
- D. fairlearn.reductions.GridSearch
Answer: A
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 # 34
A data science team trains a classification model that predicts loan approval outcomes.
Before registering the model, the team must ensure the following:
- Predictions must not disproportionately impact protected groups.
- Prediction errors can be evaluated across different data segments.
You need to assess whether the model meets Responsible AI expectations.
Which two approaches should you use? Each correct answer presents part of the solution.
Choose two.
NOTE: Each correct selection is worth one point.
- A. Analyze error rates across defined demographic cohorts.
- B. Validate inference schema compatibility.
- C. Measure endpoint latency under load.
- D. Analyze error rates across the global cohort.
- E. Evaluate feature importance for prediction transparency.
Answer: A,E
Explanation:
[D]
To evaluate a trained loan classification model for Responsible AI expectations--ensuring no disproportionate impact on protected groups, evaluating error across segments, and verifying prediction transparency--you can employ SHAP (SHapley Additive exPlanations) values to assess feature importance.
This approach allows you to identify which variables (e.g., credit history, debt levels) drive the model's predictions, fostering trust and fairness.
Feature Importance for Transparency: Use SHAP (model-agnostic) or LIME (local approximations) to explain why the model approved or denied a loan. These techniques identify how each feature contributes to individual predictions.
[E]
To ensure a trained loan approval classification model meets responsible AI expectations-- specifically, that it does not disproportionately impact protected groups and that errors can be evaluated across segments--you should analyze error rates across defined demographic cohorts using Fairness-Aware Machine Learning metrics.
Reference:
https://urfpublishers.com/journal/artificial-intelligence/article/view/explainable-aiml-testing- ensuring-transparency-accountability-and-compliance
https://timvero.com/blog/ethics-in-automated-lending-can-ai-make-fair-credit-decisions
NEW QUESTION # 35
Drag and Drop Question
A team manages prompts that are used by a generative AI application built on Microsoft Foundry.
Multiple developers contribute prompt updates, and changes must be reviewed and tracked over time.
The team requires that:
- Prompt changes are reviewed before being applied to the version in
production.
- Previous prompt versions can be restored if issues occur.
- Prompt updates follow the same governance practices as the
application code.
You need to implement a controlled process for managing and updating prompts in production.
How should you manage prompt updates to meet the requirements? To answer, move the appropriate actions to the correct requirements. You may use each action 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 # 36
You want to ensure ML pipelines produce identical results when executed in different regions or workspaces. Which factor is MOST critical to control for reproducibility?
- A. Same user identity
- B. Versioned datasets and environments
- C. Same dataset name
- D. Same compute SKU
Answer: B
Explanation:
Reproducibility across environments depends on consistent versioning of datasets and environments. Differences in data or dependencies can lead to inconsistent results even if compute resources are identical. Proper asset versioning ensures experiments can be reliably reproduced in any workspace or region.
NEW QUESTION # 37
Hotspot Question
A machine learning model is deployed to production in Azure Machine Learning and is actively serving predictions for a business application. The model was trained by using a historical dataset that represented expected input patterns at the time of deployment.
The team working on the model must ensure the following:
- Changes in input data distribution are detected.
- Appropriate actions are triggered when predefined thresholds are
exceeded.
You need to configure monitoring to meet the requirements.
Which configuration should you use for each requirement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 38
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