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| Section | Objectives |
|---|---|
| Plan and design AI solutions using Azure AI services | - Requirements gathering and solution architecture
|
| Operationalizing machine learning solutions | - Deployment and monitoring
|
| Design and implement generative AI solutions | - Large language model integration
|
| Implement secure and scalable AI systems | - Security and governance
|
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問題 #94
A data science team completes multiple training runs within an experiment by using MLflow.
The team wants to store a selected model in Azure Machine Learning so that it can be versioned and deployed later.
The model must be versioned centrally for reuse across environments.
You need to version the trained model.
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：A,C
解題說明：
To set up versioning for a trained model in an Azure Machine Learning (Azure ML) workspace using MLflow, you must capture the model artifacts during the training run and then register the model into the centralized registry.
[A]
1. Capture Model Artifacts
During each training run, use the MLflow SDK to log the model. This ensures that all necessary files (the model binary, environment dependencies, and the MLmodel metadata) are stored as run outputs in the workspace.
Manual Logging: Use a flavor-specific method like mlflow.sklearn.log_model(model,
"model_path") within an active run.
Automatic Logging: Call mlflow.autolog() before starting your training. This automatically captures metrics, parameters, and the model artifacts for supported frameworks.
Artifact Location: Once logged, artifacts are typically found in the outputs/ folder of the specific run, accessible via the Azure Machine Learning Studio.
[B]
2. Register the Model
After identifying the best-performing run, you register it to the Model Registry. This creates a named, versioned entity that can be accessed across different environments for deployment.
To set up versioning for an MLflow model in Azure Machine Learning (Azure ML) that is accessible across different environments, you should use a centralized Azure ML Registry. While a standard Azure ML Workspace acts as an MLflow server for individual experiments, an Azure ML Registry is the specifically designed feature for sharing models, environments, and components across multiple workspaces and environments within an Azure tenant.
3. Centralized Reuse
By registering the model in the workspace's registry, you establish a single source of truth. You can then load this specific version in any environment (e.g., staging or production) using its registry URI: models:/<model_name>/<version_or_alias>.
Incorrect:
[Not D]
Must use an Azure ML Registry (Central).
Reference:
https://mlflow.org/docs/latest/ml/model-registry/
問題 #95
You have an Azure subscription named Sub1 that contains an Azure Machine Learning workspace named Workspace1. Workspace1 contains the following assets:
- a registered MLflow model named Model1
- an online endpoint named Endpoint1
Outbound network connectivity from Endpoint1 is blocked.
You need to deploy Model1 to Endpoint1.
What should you do first?
答案：A
解題說明：
To successfully deploy the registered MLflow model to an online endpoint that lacks outbound internet connectivity, you must use model packaging to build a deployment package before deploying.
By default, Azure Machine Learning deploys MLflow models using a "no-code deployment" approach, which requires an outbound internet connection during container runtime to dynamically download and install Python dependencies listed in the model's conda.yaml file.
Because outbound connectivity is blocked, this process fails. Packaging the model bundles the model binaries, dependencies, and environment ahead of time, entirely removing the requirement for runtime internet access.
Reference:
https://docs.azure.cn/en-us/machine-learning/how-to-deploy-mlflow-models-online-endpoints
問題 #96
You manage an Azure Machine learning workspace. You develop a machine learning model.
You must deploy the model to use a low-priority VM with a pricing discount.
You need to deploy the model.
Which compute target should you use?
答案：A
解題說明：
The key concept here is low-priority (spot) VMs, which are available at a significant discount because Azure can reclaim them at any time. Azure Machine Learning compute clusters are the only target in the list that directly supports low-priority VM nodes as a cost-saving configuration. You set the minimum and maximum node counts and specify that new nodes should be provisioned as low-priority. Azure Container Instances (ACI) does not support low-priority pricing. Local deployment runs on the developer ' s machine with no Azure billing model. Azure Kubernetes Service (AKS) does support spot node pools but requires significantly more infrastructure management and is not the primary mechanism for low-priority compute in Azure Machine Learning. The exam tests whether you know that AML compute clusters are the managed way to leverage low-priority discounts inside Azure Machine Learning.
Microsoft Learn Reference Topic: Create and manage Azure Machine Learning compute clusters - Low- priority VMs
問題 #97
A team develops and manages a conversational assistant by using Microsoft Foundry.
The team requires generative AI to automatically evaluate every pull request of an agentic application and fail the build if safety thresholds are exceeded.
You need to automate evaluations as part of CI.
What should you configure?
答案：B
解題說明：
A GitHub Actions workflow can absolutely be configured to automatically evaluate pull requests using Azure AI Foundry and fail the CI build if safety thresholds are exceeded.By integrating the Azure AI Evaluation SDK into your automated testing suite, you can scan agentic applications for safety risks (like jailbreaks, hate speech, or content harms) during the continuous integration (CI) lifecycle.
Reference:
https://medium.com/@cataldi.ricardo/compliance-and-governance-app-with-pyrit-39526bd2e4f8
問題 #98
Drag and Drop Question
You manage an Azure Machine Learning workspace named workspace1 with a compute instance named compute1. You connect to compute1 by using a terminal window from workspace1.
You create a file named "requirements.txt" containing Python dependencies to include Jupyter.
You need to add a new Jupyter kernel to compute1.
Which four commands should you use? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
答案：
解題說明：
Explanation:
Step 1: conda create -n "python_env"
1. Create the environment
conda create -n python_env python=3.10 -y
Step 2: conda activate "python_env"
2. Activate it
conda activate python_env
Step 3: pip install -r "requirements.txt"
3. Install your dependencies (including ipykernel)
pip install -r requirements.txt
Step 4: ipython kernel install --user --name="python_env"
4. Register the environment as a Jupyter kernel
ipython kernel install --user --name="python_env"
問題 #99
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