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| Section | Weight | Objectives |
|---|---|---|
| Optimize generative AI systems and model performance | 15–20% | - Improve efficiency and cost-effectiveness
|
| Implement generative AI quality assurance and observability | 10–15% | - Evaluate and test generative AI applications
|
| Implement machine learning model lifecycle and operations | 25–30% | - Orchestrate model training and experimentation
|
| Design and implement an MLOps infrastructure | 15–20% | - Create and manage Machine Learning workspace resources and assets
|
| Design and implement a GenAIOps infrastructure | 20–25% | - Implement infrastructure for generative AI workloads
|
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NEW QUESTION # 22
Drag and Drop Question
An organization is adopting Microsoft Foundry to support multiple generative AI projects across different teams. Microsoft Foundry platform administrators require centralized governance.
Development teams need isolated environments for experimentation and deployment.
Shared policies must be enforced consistently while allowing teams to work independently.
You need to configure the Microsoft Foundry environment to meet the requirements.
Which Microsoft Foundry components should you configure? To answer, move the appropriate configurations to the correct responsibilities. 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:
NEW QUESTION # 23
A company is creating an internal tool that summarizes long meeting transcripts and extracts action items.
The model must:
Process text inputs up to 200k tokens long.
Generate concise summaries in seconds.
Support interactive testing before integration into the app.
You need to select, deploy, and test a model that supports summarization with low latency.
How should you complete the configuration plan? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
For a tool that must process text inputs up to 200k tokens long, generate concise summaries in seconds, and support interactive testing, the configuration must address three requirements. The large token context window points to GPT-4o, which supports up to 128k tokens and is among the largest-context Azure OpenAI models available in Foundry, making it suitable for long documents such as meeting transcripts. Low latency with the need to generate responses in seconds rules out batch deployment types; Data Zone Standard provides the best latency for single-tenant enterprise use cases with zone-level routing. Interactive testing before integration points directly to Microsoft Foundry ' s built-in Chat Playground or Prompt Playground, where you can paste transcripts, adjust system prompts, and evaluate outputs interactively before writing any application integration code.
Microsoft Learn Reference Topic: Deploy and test models in Microsoft Foundry - Model selection for long- context summarization
NEW QUESTION # 24
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 # 25
A team runs training and inference jobs in Azure Machine Learning.
The team experiences inconsistent runtime dependencies that cause variation in results.
You need to ensure that all jobs use the same execution dependencies.
Which asset should you define?
Answer: C
Explanation:
To guarantee consistent runtime dependencies in Azure Machine Learning, you must use Azure ML Environments configured with custom Docker images or pinned Conda dependencies.
An Environment asset encapsulates the exact Python packages, environment variables, and software settings for your training and inference workloads.
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-set-up-training-targets
NEW QUESTION # 26
An Azure Machine Learning workspace processes sensitive training data.
The workspace must NOT be accessible from the public internet.
You need to restrict network access.
Which configuration should you implement?
Answer: D
NEW QUESTION # 27
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