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| Section | Objectives |
|---|---|
| Topic 1: Optimize generative AI systems and model performance | - Implement cost management and scaling strategies for GenAI workloads - Fine-tune and distill models for specific use cases - Optimize inference performance, caching, and throughput - Tune prompts, system messages, and grounding strategies |
| Topic 2: Design and implement an MLOps infrastructure | - Set up Azure Machine Learning workspace and compute targets - Implement security, governance, and compliance for MLOps - Manage environments, data stores, and model registries - Configure source control, CI/CD pipelines, and automation for ML workflows |
| Topic 3: Implement machine learning model lifecycle and operations | - Deploy models to real-time and batch endpoints - Retrain, update, and manage model versions in production - Monitor model performance, data drift, and operational health - Train, register, and version models using Azure Machine Learning |
| Topic 4: Implement generative AI quality assurance and observability | - Monitor latency, token usage, cost, and error rates - Evaluate generative AI outputs for quality, safety, and grounding - Implement logging, tracing, and telemetry for GenAI applications - Conduct red teaming, adversarial testing, and content filtering |
| Topic 5: Design and implement a GenAIOps infrastructure | - Configure prompt orchestration, prompt flows, and agent frameworks - Implement RAG (Retrieval-Augmented Generation) pipelines and vector search - Manage API keys, rate limits, and responsible AI guardrails - Set up Microsoft Foundry and Azure AI services for generative AI workloads |
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NEW QUESTION # 46
You manage an Azure Machine Learning workspace.
You must create and configure a compute cluster for a training job by using Python SDK v2.
You need to create a persistent Azure Machine Learning compute resource, specifying the fewest possible properties.
Which two properties should you define? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer: C,D
NEW QUESTION # 47
You manage a Microsoft Foundry project. You build a multi-turn chatbot application.
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: C
Explanation:
In a Microsoft Foundry project for a multi-turn chatbot, the best built-in evaluator to use-- especially when you want to avoid an external knowledge base--is the Coherence evaluator or a combination of Agentic Evaluators.
Because your application is multi-turn, you need to observe how the AI maintains a logical flow and resolves user intent over several exchanges. Since you specifically want to avoid an external knowledge base (eliminating "Groundedness" or "Retrieval" metrics), you should focus on quality metrics that only require the conversation history itself.
Reference:
https://www.cekura.ai/blogs/why-single-turn-testing-falls-short-in-evaluating-conversational-ai
NEW QUESTION # 48
You develop a Prompt flow in Microsoft Foundry project.
You plan to use variants and invoke a custom API in the flow.
You need to add tools to the flow that will implement the planned functionality. Your solution must minimize development efforts.
Which tools should you use? To answer, move the appropriate tools to the correct functionalities. You may use each tool 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:
Implement variants: LLM tool
Invoke a custom API: Python tool
For implementing variants , use the LLM tool . Microsoft documents that Prompt flow variants are supported specifically on LLM tool nodes. A variant represents an alternative configuration of the same node, such as different prompt text, temperature values, deployment settings, or other LLM parameters. This allows multiple prompt/model configurations to be tested without duplicating the entire flow, which directly minimizes development effort.
For invoking a custom API , use the Python tool . The Python tool executes custom Python logic within a Prompt flow node and can integrate with external or third-party services. Microsoft explicitly describes Prompt flow tools as supporting integration with third-party APIs and Python packages, and the Python tool can also consume a custom connection when authentication credentials are required. This makes it the appropriate choice for calling an API that is not covered by a built-in Prompt flow tool.
The Embedding tool is designed to generate vector embeddings for text and is therefore unrelated to either requirement. It does not provide variant functionality and is not the general-purpose mechanism for invoking a custom API.
Study Guide Reference: Design and implement a GenAIOps infrastructure - Prompt flow tools, LLM variants, Python nodes, custom integrations, and flow experimentation.
NEW QUESTION # 49
An organization operates a generative AI application in production by using Microsoft Foundry. The application serves live user traffic and is updated by a data scientist team regularly as prompts and models evolve.
The application intermittently times out during production use, which requires ongoing visibility into runtime behavior.
The team must also validate model quality and safety before releasing new updates to avoid introducing regressions.
You need to apply the correct mechanisms for continuous runtime monitoring and for release time validation.
Which mechanisms should you use for each requirement? To answer, move the appropriate mechanisms to the correct requirements. You may use each mechanism 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:
The two requirements target fundamentally different stages of the AI application lifecycle. Continuous runtime monitoring addresses production behavior - an application that intermittently times out needs real- time visibility into latency, error rates, and request volumes for every call in production. Tracing backed by Application Insights or OpenTelemetry exporters is correct here because it captures granular, per-request telemetry continuously without requiring manual triggering - it is always-on by design. Release-time validation addresses pre-release quality gates - before new prompts or model updates go live, you need to verify they have not introduced regressions. An evaluation pipeline in Microsoft Foundry ' s prompt flow is correct here: it runs the updated application against a predefined test dataset, scores outputs on quality and safety metrics, and passes or fails the release based on thresholds. Evaluation pipelines run on demand triggered by CI/CD, not continuously.
Microsoft Learn Reference Topic: Microsoft Foundry observability - Tracing for runtime monitoring vs.
Evaluation pipelines for release gates
NEW QUESTION # 50
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 in 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: Configure Azure Monitor to collect logs from the workspace. Use the logs to perform prompt evaluation.
Does the solution meet the goal?
Answer: B
Explanation:
Correct:
* In Microsoft Foundry, turn on Tracing for the prompt flow of the project and execute test runs to produce trace data.
Incorrect:
* Configure Azure Monitor to collect logs from the workspace. Use the logs to perform prompt evaluation.
* 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 # 51
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