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| Section | Objectives |
|---|---|
| Design and implement a GenAIOps infrastructure | - Manage API keys, rate limits, and responsible AI guardrails - Configure prompt orchestration, prompt flows, and agent frameworks - Set up Microsoft Foundry and Azure AI services for generative AI workloads - Implement RAG (Retrieval-Augmented Generation) pipelines and vector search |
| Implement machine learning model lifecycle and operations | - Deploy models to real-time and batch endpoints - 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 |
| Optimize generative AI systems and model performance | - Fine-tune and distill models for specific use cases - Tune prompts, system messages, and grounding strategies - Implement cost management and scaling strategies for GenAI workloads - Optimize inference performance, caching, and throughput |
| Design and implement an MLOps infrastructure | - Configure source control, CI/CD pipelines, and automation for ML workflows - Manage environments, data stores, and model registries - Implement security, governance, and compliance for MLOps - Set up Azure Machine Learning workspace and compute targets |
| Implement generative AI quality assurance and observability | - Implement logging, tracing, and telemetry for GenAI applications - Monitor latency, token usage, cost, and error rates - Evaluate generative AI outputs for quality, safety, and grounding - Conduct red teaming, adversarial testing, and content filtering |
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NEW QUESTION # 60
A team is building a generative AI agent by using Retrieval-Augmented Generation (RAG) in Microsoft Foundry.
The team frequently updates prompt content. The team must be able to track changes across contributors while avoiding full application redeployments.
You need to enable rapid prompt iteration with traceability. Applications consuming the agent must be able to use updated prompts without requiring redeployment.
What should you configure for each requirement? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
For tracking changes across contributors, Git integration is the answer: by connecting the Microsoft Foundry project to a Git repository, every prompt file change is tracked as a commit with author attribution, timestamp, and diff view, and pull requests enforce review before changes reach production. The Git history provides the complete audit trail and rollback capability needed for traceability. For allowing applications to consume updated prompts without requiring redeployment, Microsoft Foundry ' s prompt management feature allows prompts to be stored and versioned as named artifacts in the project. Applications reference prompts by name and load the latest approved version at inference time, rather than having prompt text hard-coded in the application deployment artifact. This decoupling means updating a prompt is a content operation - not a code deployment - so applications automatically pick up the new prompt without any redeployment.
Microsoft Learn Reference Topic: Prompt management in Microsoft Azure AI Foundry - Git integration and dynamic prompt versioning
NEW QUESTION # 61
You manage an Azure Machine Learning workspace.
You must define the execution environments for your jobs and encapsulate the dependencies for your code.
You need to configure the environment from a Docker build context.
How should you complete the rode segment? To answer, select the appropriate option in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
NEW QUESTION # 62
You are authoring a notebook in Azure Machine Learning studio.
You must install packages from the notebook into the currently running kernel. The installation must be limited to the currently running kernel only.
You need to install the packages.
Which magic function should you use?
Answer: D
Explanation:
The correct choice is %pip . Azure Machine Learning compute instances can contain multiple Jupyter kernels, so package installation must target the interpreter associated with the notebook ' s currently active kernel . Microsoft explicitly recommends the %pip or %conda IPython magic commands for this purpose because these commands install packages into the environment associated with the running kernel.
For example, a notebook cell can contain:
%pip install scikit-learn
This ensures that the installed package becomes available to code executed by that notebook kernel.
By contrast, !pip invokes a shell command. Microsoft specifically warns against using !pip or !conda when the intention is to modify only the current notebook kernel, because shell-level package commands can reference environments or packages outside the active kernel.
%load has an entirely different purpose: it loads Python source code into a notebook cell and does not perform package installation.
Therefore, when package installation must be isolated to the active Azure Machine Learning notebook kernel,
%pip is the appropriate magic command .
Study Guide Reference: Design and implement an MLOps infrastructure - Azure Machine Learning development environments, notebook kernels, dependency management, and compute instances.
NEW QUESTION # 63
Hotspot Question
You manage a Microsoft Foundry project. You build a solution that uses a set of PDF documents.
You require two large language models (LLMs):
- An embedding model must help categorize the documents.
- A general-purpose model must generate semantically and contextually
accurate output based on the documents.
You need to select benchmarks to observe the quality of the models.
Which metrics should you use for the benchmarks? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: Accuracy
Embedding model
An embedding model must help categorize the documents.
For an embedding model designed to categorize documents within Azure AI Foundry, cosine similarity is a crucial benchmark metric. It measures the semantic similarity between document embeddings, allowing you to assess how well the model groups related documents together.
Additionally, classification accuracy is important to evaluate how effectively the model assigns documents to predefined categories.
Classification Accuracy:
This metric measures the percentage of documents that are correctly categorized by the model. It directly reflects the model's ability to assign documents to the appropriate category based on their embeddings Box 2: Coherence General-purpose model A general-purpose model must generate semantically and contextually accurate output based on the documents.
Coherence evaluates how well the language model can produce output that flows smoothly, reads naturally, and resembles human-like language.
Incorrect:
* GPTsimilarity
GPT similarity refers to the ability of GPT models to assess and quantify the semantic similarity between pieces of text. This can be achieved by using the model to generate embeddings (numerical representations) of the text and then calculating the similarity (e.g., using cosine similarity) between these embeddings. Essentially, it allows you to determine how closely related two texts are in terms of their meaning.
Reference:
https://learn.microsoft.com/en-us/azure/ai-foundry/concepts/model-benchmarks
https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/concept-model-monitoring-generative-ai-evaluation-metrics
NEW QUESTION # 64
Hotspot Question
You train a model in Azure Machine Learning.
You plan to capture experiment details for later comparison. The training code must log parameters and metrics for each run.
You review the following training script.
You need to verify whether the training script meets the experiment tracking requirement. For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 65
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