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| Section | Objectives |
|---|---|
| Topic 1: Operationalizing machine learning solutions | - ML lifecycle management
|
| Topic 2: Plan and design AI solutions using Azure AI services | - Requirements gathering and solution architecture
|
| Topic 3: Design and implement generative AI solutions | - Large language model integration
|
| Topic 4: Implement secure and scalable AI systems | - Scalability and performance optimization
|
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NEW QUESTION # 144
-
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 # 145
You use Azure Machine Learning to tram a model.
You must use Baylean sampling to Tune hyperparaters.
You need to select a learning_rate parameter distribution.
Which two distributions can you use? Each correct answer presents a complete solution.
NOTE Each correct selection is worth one point.
Answer: A,C
NEW QUESTION # 146
A company is standardizing generative AI development across multiple teams.
Each team requires an isolated workspace. Governance and shared connections must be centrally managed.
You need to implement a Microsoft Foundry environment structure that supports centralized governance and team isolation.
Which type of configuration should you use for each requirement? 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:
Explanation:
An Azure AI Hub is the top-level governance container in Microsoft Foundry: it holds shared connections to Azure OpenAI, Azure AI Search, Azure Storage, and other services; it defines network isolation policies; it manages billing and quota at the organizational level. Multiple teams share these resources without each team needing to configure their own connections or negotiate quota independently. An Azure AI Project sits inside the Hub and provides team-level isolation: each project has its own experiments, deployments, prompt flows, evaluations, and fine-tuning jobs, all governed by the Hub ' s shared infrastructure. Different teams get their own project with independent access controls via RBAC, while the platform team manages the shared Hub.
This pattern eliminates redundant resource configurations across teams while maintaining clear team-level boundaries - the correct structure for centralized governance with team isolation.
Microsoft Learn Reference Topic: Microsoft Azure AI Foundry hub and project architecture - Centralized governance and team isolation
NEW QUESTION # 147
-
You use Azure Machine Learning to implement hyperparameter tuning for an Azure ML Python SDK v2- based model training.
Training runs must terminate when the primary metric is lowered by 25 percent or more compared to the best performing run.
You need to configure an early termination policy to terminate training jobs.
Which values should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Termination policy type: Bandit
Termination policy parameter: slack_factor
The correct early termination policy is Bandit because the requirement explicitly compares each training run against the best-performing run . Microsoft defines BanditPolicy as an early termination mechanism that stops runs that fall outside a permitted performance margin relative to the best run observed so far.
The correct parameter is slack_factor because the threshold is expressed as a percentage , not as an absolute difference in the metric. Microsoft defines slack_factor as the ratio representing the allowable distance from the best-performing run. Therefore, a 25% tolerance would be configured conceptually as:
slack_factor = 0.25
A trial whose primary metric falls outside that permitted 25% band is eligible for early termination at the configured evaluation interval. Microsoft provides the same pattern in SDK v2 examples, such as BanditPolicy (slack_factor=0.15, ...) for a 15% relative tolerance.
Median stopping compares runs with the median performance across completed or running trials rather than strictly against the current best run. Truncation selection terminates a specified percentage of the worst- performing trials, so neither matches this requirement.
NEW QUESTION # 148
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear on the review screen.
You work in Microsoft Foundry with a prompt flow.
You must manually evaluate prompts and compare results across prompt variants.
You need to capture the inputs, outputs, token usage, and latencies for each flow run for the evaluation.
Solution: In Microsoft Foundry, turn on Tracing for the prompt flow of the project and execute test runs to produce trace data.
Does the solution meet the goal?
Answer: A
Explanation:
Correct:
* In Microsoft Foundry, turn on Tracing for the prompt flow of the project and execute test runs to produce trace data.
Incorrect:
* Create prompt variants and compare their outputs in the Evaluation experience.
* Use the prompt flow SDK to enable tracing for the flow before executing runs. Then run the flow to generate traceable results.
Note:
In Azure AI Foundry, you can capture and compare these metrics by enabling Tracing and using the Bulk Test feature. This allows you to systematically evaluate different prompt variants against a common dataset.
Steps to Evaluate and Compare Prompt Variants
*-> 1. Enable Tracing
Navigate to your Prompt Flow project.
Locate the Tracing toggle at the top of the flow authoring page.
Switch it to On.
This ensures every execution captures latency, token counts, and node-level inputs/outputs.
2. Create Prompt Variants
Within your flow, identify the LLM node you want to test.
Click Variants to create multiple versions of your prompt (e.g., Variant_0, Variant_1).
This allows you to test different instructions or few-shot examples side-by-side.
3. Run a Bulk Test (Evaluation)
4. Analyze the Results
Reference:
https://www.linkedin.com/pulse/streamlining-generative-ai-development-azure-foundry-tracing- taneja-mbwze
NEW QUESTION # 149
......
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