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| Section | Objectives |
|---|---|
| Topic 1: Design and implement generative AI solutions | - Large language model integration
|
| Topic 2: Implement secure and scalable AI systems | - Security and governance
|
| Topic 3: Operationalizing machine learning solutions | - Deployment and monitoring
|
| Topic 4: Plan and design AI solutions using Azure AI services | - Requirements gathering and solution architecture
|
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NEW QUESTION # 165
A team develops multiple AI applications in Microsoft Foundry that rely on shared prompt templates.
The team requires a centralized way to track, version, and reuse prompt content across projects.
You need to recommend a solution to track and reuse prompt content.
Which approach should you recommend?
Answer: D
Explanation:
In Microsoft Foundry, centralizing prompt templates in a Git repository (such as GitHub) is a recommended GenAIOps practice to ensure consistency, versioning, and reuse across multiple AI applications.
Storing prompts as versioned files in a Git repository is an excellent approach for Microsoft Foundry applications. It treats prompts like code (PromptOps), enabling clear audit trails, peer reviews via Pull Requests, and easy rollbacks.
To make this centralized system effective, you should consider these three components:
Centralized Repository: Use a single "Prompt Bank" repo where templates are stored in a structured format like JSON or YAML. Include metadata like model version, temperature, and input variables.
Prompt Registry / SDK: Develop a small internal utility (or use a tool like Azure AI Studio's Prompt Flow) that allows applications to fetch the "latest" or a "tagged" version of a prompt via API or as a Git submodule.
CI/CD Integration: Automate testing so that when a prompt file is updated, it triggers a "prompt evaluation" pipeline to ensure the change doesn't degrade AI performance across the dependent projects.
Reference:
https://www.getmaxim.ai/articles/best-practices-for-prompt-management-in-ai-applications
NEW QUESTION # 166
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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.
Answer:
Explanation:
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.
NEW QUESTION # 167
A product team is building a customer support assistant that must respond consistently across multiple channels.
Early testing shows that small wording changes in prompts cause large differences in tone and factual accuracy.
The team needs prompts that are reliable, reusable, and adaptable across multiple use cases without retraining the underlying model.
You need to design prompts that improve response quality while remaining flexible for future changes.
Which two actions should you perform? Each correct answer presents part of the solution. (Choose two.)
Answer: B,E
Explanation:
B and C provide the strongest architecture for reusable and predictable prompts. Separating persistent application instructions from dynamic user input establishes a clear instruction hierarchy . The stable portion of the prompt can define application behavior while channel-specific or request-specific content remains variable. This reduces coupling between user wording and the behavioral rules of the assistant and makes the prompt easier to reuse, version, test, and modify.
The system prompt should establish the model ' s role, behavioral boundaries, tone, communication style, response format, and other persistent rules. Microsoft specifically describes system messages as high-priority instructions used to define an assistant ' s role and boundaries and to set tone and communication style.
Fine-tuning for every conversational variation is unnecessarily expensive and contradicts the requirement to remain adaptable without retraining. Increasing temperature increases randomness and therefore works against consistent responses. Although repeating important instructions can sometimes mitigate recency effects, Microsoft treats this as an optional prompt-engineering technique to experiment with, not the primary architectural technique for producing reusable prompts.
Study Guide Reference: Design and implement a GenAIOps infrastructure - design and develop prompts, prompt management, prompt variants, system instructions, and reusable prompt structures.
NEW QUESTION # 168
You have an Azure Machine Learning workspace named workspaces.
You must add a datastore that connects an Azure Blob storage container to workspaces. You must be able to configure a privilege level.
You need to configure authentication.
Which authentication method should you use?
Answer: B
NEW QUESTION # 169
You use the Azure Machine learning SDK v2 tor Python and notebooks to tram a model. You use Python code to create a compute target, an environment, and a taring script. You need to prepare information to submit a training job.
Which class should you use?
Answer: B
NEW QUESTION # 170
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