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Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions (Q51-Q56):
NEW QUESTION # 51
You need to run large-scale inference jobs on millions of records periodically. Jobs are not latency-sensitive but must be cost-efficient and scalable. Which deployment option is MOST appropriate?
Answer: A
Explanation:
Batch endpoints are optimized for large-scale, asynchronous inference workloads. They efficiently process large datasets and scale based on demand, making them cost-effective for non-real-time scenarios. Online endpoints are designed for low-latency use cases and are more expensive for batch processing.
NEW QUESTION # 52
A financial services company is deploying Microsoft Foundry to host generative AI workloads that process regulated customer data. The Microsoft Foundry environment must prevent any public network exposure while still allowing services managed by Microsoft Foundry to communicate with dependent Azure resources.
Security auditors require that all traffic to and from the Microsoft Foundry resource remain on private networks, with no public endpoints available.
You need to configure the Microsoft Foundry environment so that network access is restricted while maintaining full platform functionality.
Which two actions should you perform? Each correct answer presents part of the solution.
Choose two.
NOTE: Each correct selection is worth one point.
Answer: A,B
Explanation:
To host generative AI workloads in a Microsoft Foundry environment with strictly private communication and no public network exposure, you must configure a Managed Virtual Network (Managed VNet) with specific isolation settings and disable all public inbound access.
[A]
Enable Managed Virtual Network Isolation
During the creation of your Azure AI Foundry hub, navigate to the Networking tab.
Select the Private with Approved Outbound isolation mode. This mode ensures that all outbound traffic from the managed compute resources is restricted to only the destinations you explicitly approve, such as dependent Azure resources.
Once enabled, this isolation mode cannot be disabled.
[E]
Disable Public Inbound Access
In the Networking tab of your Foundry resource, set Public network access to Disabled.
This action blocks all traffic from the public internet, ensuring the resource is only accessible through private connections.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/how-to/managed-virtual-network
NEW QUESTION # 53
A team plans to deploy a large foundation model in Microsoft Foundry as part of a new enterprise AI capability.
Different business units across the team ' s organization will access the model from various internal applications.
You need to deploy a foundation model by minimizing latency.
Which deployment type should you use?
Answer: D
Explanation:
Data Zone Standard deployment routes requests to multiple datacenters within a defined geographic zone such as the US data zone, providing lower latency by using zone-level load balancing while maintaining data within a broad compliance boundary. This makes it suitable for enterprise internal applications where multiple business units need low-latency access without strict single-region data residency requirements. Developer deployment (option A) is a low-quota, unguaranteed tier designed for prototype testing, not production workloads serving multiple business units. Data Zone Batch (option B) is designed for high-volume asynchronous batch processing, not interactive low-latency real-time requests. Global Batch (option D) is optimized for scheduled bulk inference across all global regions but adds latency and is inappropriate for synchronous interactive use cases.
Microsoft Learn Reference Topic: Foundation model deployment types in Microsoft Foundry - Data Zone Standard for low-latency internal use
NEW QUESTION # 54
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 # 55
You plan to filter your traces to identify issues while observing how the application is responding. The solution must not use an external knowledge base.
You need to select an evaluation metric.
Which built-in evaluator should you use?
Answer: D
Explanation:
A multi-turn chatbot application intermittently produces responses that are grammatically correct and on-topic but contradict earlier turns in the conversation, creating a confusing user experience. CoherenceEvaluator measures exactly this: whether the flow of ideas across a multi-turn conversation is logically consistent and non-contradictory without requiring an external knowledge base. RelevanceEvaluator (option A) measures whether responses are topically on-point but often requires a reference context or knowledge base.
SimilarityEvaluator (option B) requires a reference answer for comparison. QAEvaluator (option C) is a composite evaluator for question-answering tasks that requires a ground-truth context document.
CoherenceEvaluator is the only option that works purely from the conversation history itself with no external knowledge base, perfectly matching the stated constraint and the multi-turn chatbot use case.
Microsoft Learn Reference Topic: Evaluate conversational AI applications in Microsoft Foundry - CoherenceEvaluator for multi-turn chatbots
NEW QUESTION # 56
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