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| Section | Objectives |
|---|---|
| Topic 1: Implement generative AI quality assurance and observability | - Monitor latency, token usage, cost, and error rates - Implement logging, tracing, and telemetry for GenAI applications - Evaluate generative AI outputs for quality, safety, and grounding - Conduct red teaming, adversarial testing, and content filtering |
| Topic 2: Design and implement a GenAIOps infrastructure | - Manage API keys, rate limits, and responsible AI guardrails - Implement RAG (Retrieval-Augmented Generation) pipelines and vector search - Set up Microsoft Foundry and Azure AI services for generative AI workloads - Configure prompt orchestration, prompt flows, and agent frameworks |
| Topic 3: Design and implement an MLOps infrastructure | - Configure source control, CI/CD pipelines, and automation for ML workflows - Manage environments, data stores, and model registries - Set up Azure Machine Learning workspace and compute targets - Implement security, governance, and compliance for MLOps |
| Topic 4: Implement machine learning model lifecycle and operations | - Train, register, and version models using Azure Machine Learning - Monitor model performance, data drift, and operational health - Retrain, update, and manage model versions in production - Deploy models to real-time and batch endpoints |
| Topic 5: Optimize generative AI systems and model performance | - Tune prompts, system messages, and grounding strategies - Fine-tune and distill models for specific use cases - Optimize inference performance, caching, and throughput - Implement cost management and scaling strategies for GenAI workloads |
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33. Frage
Drag and Drop Question
You build and manage a model by using Azure Machine Learning workspace.
Before you deploy the model, you must create a Responsible AI dashboard in Azure Machine Learning studio. The dashboard must provide observation of the following:
- metrics that show real-world impact on an outcome of interest due to
taking a treatment policy
- examples with minimal changes to a particular data point such that
the model's prediction changes
You need to implement the components for the Responsible AI dashboard.
Which components should you implement? To answer, move the appropriate components to the correct observations. You may use each component 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.
Antwort:
Begründung:
Explanation:
Box 1: Causal analysis
To observe metrics that show the real-world impact on an outcome of interest due to taking a treatment policy, you must implement the Causal analysis component.
Real-World Intervention: Causal analysis specifically uses historical data to evaluate how changing a particular feature (the treatment) directly impacts a real-world outcome.
Policy Making: It generates policy insights by showing which treatment policy is most effective for a specific cohort to optimize your outcome of interest.
Box 2: Counterfactual analysis
You must implement Counterfactual analysis.
The Counterfactual what-if component of the Azure Machine Learning Responsible AI dashboard is designed specifically to "interrogate" a model. It calculates the exact feature tweaks necessary to reverse or modify a specific forecast:
Minimizing Changes: It generates a set of examples demonstrating the smallest possible adjustments to a data point that cause the model's prediction to flip.
Actionable Scenarios: It surfaces the closest data points with opposing or alternative predictions, allowing you to easily test "what-if" scenarios.Why Other Components Do Not Match Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/concept-responsible-ai-dashboard
34. Frage
You have a deployment of an Azure OpenAI Service base model.
You plan to fine-tune the model.
You need to prepare a file that contains training data.
Which file format should you use?
Antwort: B
Begründung:
Azure OpenAI Service fine-tuning requires training data in JSON Lines (JSONL) format - a text file where each line is a self-contained, valid JSON object representing one training example. For chat fine-tuning, each line contains a messages array with system, user, and assistant turns. JSONL is ideal for machine-learning datasets because it is streamable, easy to generate programmatically, and directly supported by the Azure OpenAI fine-tuning API. CSV (option A) and TSV (option B) are tabular formats that cannot natively represent the nested, multi-turn conversation structure required by chat models. Plain JSON (option D) would require loading the entire file as a single object, which is impractical for large datasets and is not accepted by the Azure OpenAI fine-tuning endpoint. Always use JSONL with UTF-8 encoding for Azure OpenAI training files.
Microsoft Learn Reference Topic: Prepare training data for Azure OpenAI fine-tuning - JSONL file format
35. Frage
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?
Antwort: C
36. Frage
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?
Antwort: B
Begründung:
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
37. Frage
You have an Azure Machine Learning workspace. You are running an experiment on your local computer.
You need to ensure that you can use MLflow Tracking with Azure Machine Learning Python SDK v2 to store metrics and artifacts from your local experiment runs in the workspace.
In which order should you perform the actions? To answer, move all actions from the list of actions to the answer area and arrange them in the correct order.
Antwort:
Begründung:
Explanation:
38. Frage
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