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| Section | Objectives |
|---|---|
| Implement generative AI quality assurance and observability | - Evaluate generative AI outputs for quality, safety, and grounding - Conduct red teaming, adversarial testing, and content filtering - Monitor latency, token usage, cost, and error rates - Implement logging, tracing, and telemetry for GenAI applications |
| Optimize generative AI systems and model performance | - Optimize inference performance, caching, and throughput - Tune prompts, system messages, and grounding strategies - Implement cost management and scaling strategies for GenAI workloads - Fine-tune and distill models for specific use cases |
| Design and implement an MLOps infrastructure | - Implement security, governance, and compliance for MLOps - Configure source control, CI/CD pipelines, and automation for ML workflows - Set up Azure Machine Learning workspace and compute targets - Manage environments, data stores, and model registries |
| Implement machine learning model lifecycle and operations | - Monitor model performance, data drift, and operational health - Train, register, and version models using Azure Machine Learning - Retrain, update, and manage model versions in production - Deploy models to real-time and batch endpoints |
| Design and implement a GenAIOps infrastructure | - Set up Microsoft Foundry and Azure AI services for generative AI workloads - Implement RAG (Retrieval-Augmented Generation) pipelines and vector search - Manage API keys, rate limits, and responsible AI guardrails - Configure prompt orchestration, prompt flows, and agent frameworks |
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NEW QUESTION # 179
You have a Microsoft Foundry project.
You plan to use the Microsoft Foundry portal to fine-tune a base Azure OpenAI Service model that can accept both text and images as input.
You need to choose the suitable model.
Which model should you choose?
Answer: D
Explanation:
GPT-4o is OpenAI ' s multimodal model that accepts both text and image inputs natively. The ' o ' in GPT-4o stands for ' omni, ' reflecting its ability to process multiple modalities in a single inference call. It is available in Microsoft Foundry for both inference and fine-tuning, and it supports vision inputs alongside text prompts, making it the correct choice for any fine-tuning scenario requiring multimodal input. Davinci-002 (option A) is a legacy text-completion model with no vision capabilities and limited fine-tuning support for modern chat formats. GPT-3.5-Turbo (option C) is a text-only chat model that does not accept image inputs. GPT-4 (option D) has a standard version that is text-only; the vision variant GPT-4V is distinct from GPT-4o and has more limited fine-tuning availability in Foundry. For multimodal text plus image fine-tuning, GPT-4o is the only correct choice.
Microsoft Learn Reference Topic: Azure OpenAI Service models available for fine-tuning - GPT-4o multimodal capabilities
NEW QUESTION # 180
You create a workspace by using Azure Machine Learning Studio.
You must run a Python SDK v2 notebook in the workspace by using Azure Machine Learning Studio. You must preserve the current values of variables set in the notebook for the current instance.
You need to maintain the state of the notebook.
What should you do?
Answer: D
NEW QUESTION # 181
A team provisions an Azure Machine Learning environment by triggering pull requests.
Deployments must be automated, auditable, and require approval before running.
You need to select a deployment automation tool.
Which tool should you use?
Answer: A
Explanation:
GitHub Actions is the appropriate automation tool because the deployment process originates from pull requests and requires CI/CD automation, traceability, and an approval gate. Azure Machine Learning integrates directly with GitHub Actions for automated machine learning workflows, including infrastructure and asset deployment. Workflows can be triggered by repository events such as pull requests, merges, or pushes, providing an auditable record of who initiated the change, which commit was deployed, and whether the workflow succeeded.
Critically, GitHub environments can enforce required reviewers before a deployment job executes. Microsoft documents that when an environment requires approval, a job cannot access the environment ' s protected secrets until one of the required reviewers approves the deployment. This directly satisfies the requirement that deployment must require approval before running.
Azure Machine Learning pipelines orchestrate ML workflow steps such as preprocessing, training, evaluation, and registration, but they are not primarily the repository-level pull-request approval mechanism. MLflow provides experiment tracking and model lifecycle functionality, not CI orchestration. Azure Monitor provides telemetry and alerting rather than source-driven deployment automation.
Therefore, GitHub Actions provides the required combination of pull-request integration, automation, auditability, and protected deployment approval.
Study Guide Reference: Design and implement an MLOps infrastructure - CI/CD, GitHub Actions, pull- request automation, deployment approvals, protected environments, and Azure Machine Learning integration.
NEW QUESTION # 182
You manage an Azure Machine Learning workspace that includes a batch endpoint. You plan to deploy a model to the batch endpoint. You need to configure compute for the deployment. Which compute should you use?
Answer: D
NEW QUESTION # 183
Hotspot Question
You manage an Azure OpenAI deployment of the gpt-4o base model.
You plan to fine-tune the deployed model.
You need to prepare a file that contains training data.
Which keys should you include in each line of the training data file? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
For fine-tuning conversational models like gpt-4o on Azure OpenAI, the training file must be formatted in a JSON Lines (.jsonl) file, where each line represents a single training conversation.
Each line must strictly contain a messages key wrapping an array of message objects.
Box 1: role
role: Defines the sender of the message. The accepted strings for fine-tuning are "system",
"user", or "assistant".
Box 2: content
content: Contains the actual text string or multi-modal payload of the message.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/openai/how-to/reinforcement-fine-tuning
NEW QUESTION # 184
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