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| Section | Objectives |
|---|---|
| Operationalizing machine learning solutions | - ML lifecycle management
|
| Plan and design AI solutions using Azure AI services | - Responsible AI design
|
| Implement secure and scalable AI systems | - Scalability and performance optimization
|
| Design and implement generative AI solutions | - Large language model integration
|
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116. Frage
-
You complete the fine-tuning of a generative model in Microsoft Foundry. The fine-tuned model now appears as a new model variant in your development environment.
The deployment process must ensure that proper validation and control is maintained.
You need to promote the fine-tuned model from development to production.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
Antwort:
Begründung:
Explanation:
Correct sequence:
* Create a Developer Tier deployment.
* Evaluate and validate the model.
* Create a Global Standard deployment.
The fine-tuned model should first be deployed using the Developer Tier because Microsoft specifically positions this deployment type for fine-tuned model candidate evaluation rather than production use .
Developer Tier deployments provide a cost-efficient environment for testing custom models and do not provide a production SLA.
After deployment, the model should be evaluated and validated . Microsoft Foundry supports running the model through the playground and the Evaluations service to assess the deployed candidate against expected quality, safety, and task-performance criteria. Microsoft explicitly documents that evaluations can be executed against a deployed fine-tuned model candidate and compared with other model versions.
Only after the candidate satisfies the required validation criteria should it be promoted to a Global Standard deployment for production inference. Global Standard is designed for general production workloads and provides substantially more appropriate production characteristics than Developer Tier.
Register the fine-tuned model version is the distractor here. The scenario already states that fine-tuning is complete and the resulting model variant exists in Microsoft Foundry; the required promotion workflow is therefore test deployment # validation # production deployment .
Study Guide Reference: Implement machine learning model lifecycle and operations - fine-tuned model evaluation, controlled promotion, Developer Tier deployments, production deployment, and model lifecycle governance.
117. Frage
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A biomedical research company plans to enroll people in an experimental medical treatment trial.
You create and train a binary classification model to support selection and admission of patients to the trial.
The model includes the following features: Age, Gender, and Ethnicity.
The model returns different performance metrics for people from different ethnic groups.
You need to use Fairlearn to mitigate and minimize disparities for each category in the Ethnicity feature.
Which technique and constraint should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Antwort:
Begründung:
Explanation:
Technique: Grid search
Constraint: Demographic parity
The appropriate mitigation technique is GridSearch with a DemographicParity constraint. Fairlearn provides reduction-based mitigation algorithms that retrain a standard estimator using differently weighted training data while enforcing a specified fairness constraint. GridSearch is one of Fairlearn ' s reduction algorithms for binary classification and can search across candidate models representing different trade-offs between predictive performance and fairness.
The appropriate constraint is Demographic parity because the model determines whether individuals receive an opportunity-in this case, selection and admission to a medical trial . Microsoft categorizes decisions that extend or withhold opportunities or resources as allocation harms and specifically identifies demographic parity as a parity constraint intended to mitigate allocation disparities in binary classification.
Demographic parity seeks comparable rates of positive predictions across groups defined by the sensitive attribute. Here, Ethnicity is the sensitive feature, so the objective is to reduce disparities in selection rates between ethnic groups.
False-positive-rate parity has a different purpose: it specifically requires comparable false-positive rates across groups among cases whose true label is negative. The scenario does not identify unequal false-positive rates as the problem.
118. Frage
Drag and Drop Question
You complete the fine-tuning of a generative model in Microsoft Foundry. The fine-tuned model now appears as a new model variant in your development environment.
The deployment process must ensure that proper validation and control is maintained.
You need to promote the fine-tuned model from development to production.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
Antwort:
Begründung:
119. Frage
A team is developing a generative AI assistant. The team is experimenting with multiple prompt variants to improve the user experience.
When comparing prompt variants, the team plans to assess whether the generated responses are grammatically correct.
You need to evaluate the quality of the language from the generated responses.
Which evaluator should you use?
Antwort: D
Begründung:
The best evaluator within the Microsoft ecosystem for checking the grammatical correctness of generative AI responses--especially when testing multiple prompt variants--is the Azure AI Evaluator for Fluency, available within Azure AI Foundry.
Fluency Evaluator (builtin.fluency)
Purpose: Specifically designed to measure the effectiveness and clarity of written communication.
Grammatical Focus: It assesses grammatical accuracy, sentence structure, punctuation, and vocabulary usage in AI-generated text.
Result: It provides a 1-5 Likert scale score, allowing you to compare which prompt variants produce the most grammatically correct, natural-sounding responses.
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/concept-model-monitoring- generative-ai-evaluation-metrics
120. Frage
Hotspot Question
You manage a Retrieval-Augmented Generation (RAG) system that retrieves internal policy documents from a vector index.
Recent analysis shows that:
- Retrieved results frequently include duplicated content from the same document.
- Retrieved chunks sometimes span unrelated policy sections.
You review the following retrieval and ingestion configurations:
You need to reduce duplicated retrieval results and improve chunk relevance across policy sections. For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Antwort:
Begründung:
121. Frage
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