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| Section | Objectives |
|---|---|
| Design and implement generative AI solutions | - Large language model integration
|
| Implement secure and scalable AI systems | - Scalability and performance optimization
|
| Operationalizing machine learning solutions | - ML lifecycle management
|
| Plan and design AI solutions using Azure AI services | - Requirements gathering and solution architecture
|
>> AI-300 Latest Exam Notes <<
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NEW QUESTION # 58
You train models on GPU-enabled clusters but deploy them on CPU-based endpoints. Recently, inference failures occur due to incompatible dependencies. What should you do to ensure consistency?
Answer: D
Explanation:
Defining and reusing environment configurations ensures that dependencies remain consistent between training and inference. This prevents runtime errors caused by mismatched libraries.
Using identical compute resources is unnecessary and inefficient, as consistency depends on environment configuration rather than hardware.
NEW QUESTION # 59
Hotspot Question
You create an Azure Machine Learning workspace.
You are developing a Python SDK v2 notebook to perform custom model training in the workspace. The notebook code imports all required packages.
You need to complete the Python SDK v2 code to include a training script, environment, and compute information.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: MLClient
Box 2: command
Example, Submit a script run, SDK v2
#connect to the workspace
ml_client = MLClient.from_config(DefaultAzureCredential())
# set up pytorch environment
env = Environment(
image="mcr.microsoft.com/azureml/openmpi3.1.2-ubuntu18.04",
conda_file="pytorch-env.yml",
name="pytorch-env"
)
# define the command
command_job = command(
code="./src",
command="train.py",
environment=env,
compute="cpu-cluster",
)
returned_job = ml_client.jobs.create_or_update(command_job)
returned_job
Note: create_or_update
Creates or updates an Azure ML resource.
create_or_update(entity: T, **kwargs) -> T
Parameters
entity
Union[Job , Model, Environment, Component , Datastore]
Required
The resource to create or update.
Returns
The created or updated resource.
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/migrate-to-v2-command-job
https://learn.microsoft.com/en-us/python/api/azure-ai-ml/azure.ai.ml.mlclien
NEW QUESTION # 60
A team manages an Azure Machine Learning workspace and deploys a model to an endpoint.
A deployed online endpoint shows inconsistent response times during periods of high traffic.
You need to identify potential performance degradation.
Which three metrics should you monitor? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point. Choose three
Answer: B,C,E
Explanation:
During high traffic, the question is whether the endpoint is keeping up with demand. Requests per minute (B) tells you the actual request load on the endpoint, letting you correlate traffic spikes with degradation.
Connections active (C) reveals whether the endpoint ' s connection pool is saturating - too many concurrent connections without adequate scaling causes queuing and timeouts. Request latency (E) is the gold-standard measure of user-perceived performance; rising latency under load is the clearest signal of degradation. Feature count (A) is a model-design attribute, not a runtime performance metric. Dataset size (D) is a training-time concern unrelated to endpoint performance. Azure Machine Learning online endpoints expose these metrics through Azure Monitor, and Microsoft recommends configuring alert rules on latency and request rate thresholds for all production endpoints.
Microsoft Learn Reference Topic: Monitor Azure Machine Learning online endpoints - Azure Monitor metrics for managed endpoints
NEW QUESTION # 61
A team manages an Azure Machine Learning workspace and deploys a model to an endpoint.
A deployed online endpoint shows inconsistent response times during periods of high traffic.
You need to identify potential performance degradation.
Which three metrics should you monitor? Each correct answer presents part of the solution.
Choose three.
NOTE: Each correct selection is worth one point.
Answer: B,C,E
Explanation:
To locate potential performance degradation in an Azure Machine Learning online endpoint during high traffic, you should monitor these three metrics:
Requests per minute: This metric tracks the volume of incoming traffic and helps identify if spikes in load correlate with slower response times.
Connections active: This monitors the total number of concurrent TCP connections from clients, which can indicate if the endpoint is reaching its capacity limits during peak periods.
Request latency: This directly measures the time taken to respond to requests, allowing you to observe exactly when and by how much performance is degrading.
Reference:
https://oneuptime.com/blog/post/2026-02-16-how-to-deploy-a-machine-learning-model-as-a-real- time-endpoint-in-azure-machine-learning/view
NEW QUESTION # 62
An organization validates generative AI applications during CI/CD Microsoft Foundry.
Evaluation must run automatically and block releases when quality thresholds are NOT met.
Manual evaluation is no longer acceptable.
Evaluation must use both predefined quality metrics and custom safety checks.
You need to implement an automated evaluation workflow that supports both built-in and custom metrics.
What should you do?
Answer: B
Explanation:
To implement an automated evaluation step in GitHub Actions for Microsoft Foundry AI, you can use the Microsoft Foundry Evaluation GitHub Action (or the Azure AI Evaluation SDK).
This setup allows you to run both built-in metrics (like groundedness or coherence) and custom safety checks, then fail the build if scores fall below your defined thresholds.
Implementation Steps
1. Define Your Evaluators
You need to configure which metrics to use. Microsoft Foundry supports two main types:
Built-in Metrics: Pre-trained models that score quality (coherence, fluency) and safety (hate, violence, self-harm).
Custom Metrics: Python-based evaluators you define to check domain-specific requirements.
2. Configure the GitHub Actions Workflow
Create a .yml file in your .github/workflows directory. This workflow will:
Trigger on a pull request or commit.
Authenticate with Azure/Foundry.
Run Evaluation using the microsoft/ai-agent-evals action.
Enforce Thresholds to block the release if quality is insufficient.
Key Components for "Block Release" Logic
To ensure the release is blocked, your workflow must include a gating step that interprets the evaluation results:
Reference:
https://learn.microsoft.com/en-us/training/modules/automated-evaluation-genaiops
NEW QUESTION # 63
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