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| Section | Objectives |
|---|---|
| Topic 1: Implement machine learning model lifecycle and operations | - Train, register, and version models using Azure Machine Learning - Deploy models to real-time and batch endpoints - Monitor model performance, data drift, and operational health - Retrain, update, and manage model versions in production |
| Topic 2: Implement generative AI quality assurance and observability | - 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 - Monitor latency, token usage, cost, and error rates |
| Topic 3: 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 |
| Topic 4: Design and implement an MLOps infrastructure | - Manage environments, data stores, and model registries - 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 |
| Topic 5: Design and implement a GenAIOps infrastructure | - 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 - Manage API keys, rate limits, and responsible AI guardrails |
為了通過Microsoft AI-300 認證考試，請選擇我們的Testpdf來取得好的成績。你不會後悔這樣做的，花很少的錢取得如此大的成果這是值得的。我們的Testpdf不僅能給你一個好的考試準備，讓你順利通過Microsoft AI-300 認證考試，而且還會為你提供免費的一年更新服務。
問題 #169
A Retrieval-Augmented Generation (RAG) solution returns incomplete answers because relevant content is inconsistently retrieved from the knowledge source.
You need to improve RAG accuracy without changing the embedding model currently in use. You need to achieve this goal while minimizing operational costs.
Which two actions should you perform? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point. Choose two .
答案：A,C
解題說明：
Microsoft ' s RAG optimization guidance identifies two high-impact, low-cost improvements for retrieval quality that do not require changing the embedding model. First, tuning chunk size and overlap (option A):
chunk size determines how much context each retrieved piece contains - too large and irrelevant content dilutes the signal; too small and answers may be split across chunks. Adjusting these parameters requires only re-indexing the documents with zero additional compute cost. Second, implementing a re-ranker (option B): a re-ranker is a cross-encoder model that takes the top-N retrieved chunks and re-scores them based on their specific relevance to the query, significantly improving precision by filtering out contextually irrelevant chunks. Re-rankers add modest compute cost but are far cheaper than changing the embedding model, which would require re-embedding the entire knowledge base. Increasing token limits (option C) and optimizing embedding vector length (option D) do not address retrieval accuracy without an embedding model change.
Microsoft Learn Reference Topic: Optimize RAG pipelines - Chunk size tuning and re-ranking in Azure AI Search and Azure Machine Learning
問題 #170
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 have an Azure Machine Learning workspace. You connect to a terminal session from the Notebooks page in Azure Machine Learning studio.
You plan to add a new Jupyter kernel that will be accessible from the same terminal session.
You need to perform the task that must be completed before you can add the new kernel.
Solution: Create an environment.
Does the solution meet the goal?
答案：B
解題說明：
Correct:
* Create an environment.
Incorrect:
* Delete the Python 3.6 - AzureML kernel.
* Delete the Python 3.8 - AzureML kernel.
Note:
Before you can add a new Jupyter kernel on an Azure Machine Learning compute instance terminal, you must create a Conda environment.
Required Workflow
To officially provision and expose the new kernel to your Azure Machine Learning studio Notebooks, you need to execute the following full process from your terminal session:
Create the environment: Provision a new isolated environment (e.g., using conda create -n newenv python=3.10).
Activate the environment: Run conda activate newenv.
Install dependencies: Add the required ipykernel package using conda install ipykernel or pip install ipykernel.
Register the kernel: Bind the new environment configuration to the global Jupyter directory by running:
python -m ipykernel install --user --name newenv --display-name "My New Kernel" Reference:
https://docs.azure.cn/en-us/machine-learning/how-to-access-terminal
問題 #171
You create a multi-class image classification model with automated machine learning in Azure Machine Learning.
You need to prepare labeled image data as input for model training in the form of an Azure Machine Learning tabular dataset.
Which data format should you use?
答案：C
解題說明：
Azure Machine Learning, you should use the JSON Lines (.jsonl) format.
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-prepare-datasets-for-automl-images?view=azureml-api-2
問題 #172
You use an Azure Machine Learning workspace.
You must monitor cost at the endpoint and deployment level.
You have a trained model that must be deployed as an online endpoint. Users must authenticate by using Microsoft Entra ID.
What should you do?
答案：C
問題 #173
A company's platform engineers manage the resource settings and governance of Microsoft Foundry.
Developers must be able to create and update project assets but must not be able to change resource-level configurations.
You need to enforce least privilege access for the engineers and developers.
Which two actions should you perform? Each correct answer presents part of the solution.
Choose two.
NOTE: Each correct selection is worth one point.
答案：B,D
解題說明：
[A]
Engineer Permissions (Hub Scope)
Engineers require the ability to manage infrastructure, networking, and global security settings.
Role: Azure AI Administrator or Contributor.
Assignment Scope: Assign at the Foundry Hub/Resource level.
Capabilities: They can manage virtual networks, customer-managed keys, and shared connections (e.g., Azure OpenAI) that all projects inherit.
[C]
Developer Permissions (Project Scope)
Developers should be restricted from changing the underlying resource configurations but need full access to their specific AI workloads.
Role: Azure AI Developer or Project Contributor.
Assignment Scope: Assign strictly at the Foundry Project level.
Capabilities: This allows them to create and update project assets (agents, flows, evaluations) and deploy models without permission to modify Hub-level infrastructure or security settings Reference:
https://learn.microsoft.com/en-us/azure/foundry/concepts/rbac-foundry
問題 #174
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