對於AI-300認證考試，你已經準備好了嗎？考試近在眼前，你可以信心滿滿地迎接考試嗎？如果你還沒有通過考試的信心，在這裏向你推薦一個最優秀的參考資料。只需要短時間的學習就可以通過考試的最新的AI-300考古題出現了。这个考古題是由PDFExamDumps提供的。
| Section | Objectives |
|---|---|
| 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 |
| 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 - Configure prompt orchestration, prompt flows, and agent frameworks - Manage API keys, rate limits, and responsible AI guardrails |
| Design and implement an MLOps infrastructure | - Configure source control, CI/CD pipelines, and automation for ML workflows - Set up Azure Machine Learning workspace and compute targets - Implement security, governance, and compliance for MLOps - Manage environments, data stores, and model registries |
| Implement machine learning model lifecycle and operations | - Deploy models to real-time and batch endpoints - Monitor model performance, data drift, and operational health - Retrain, update, and manage model versions in production - Train, register, and version models using Azure Machine Learning |
| Implement generative AI quality assurance and observability | - Conduct red teaming, adversarial testing, and content filtering - Implement logging, tracing, and telemetry for GenAI applications - Evaluate generative AI outputs for quality, safety, and grounding - Monitor latency, token usage, cost, and error rates |
對於AI-300認證考試，你已經準備好了嗎？考試近在眼前，你可以信心滿滿地迎接考試嗎？如果你還沒有通過考試的信心，在這裏向你推薦一個最優秀的參考資料。只需要短時間的學習就可以通過考試的最新的AI-300考古題出現了。这个考古題是由PDFExamDumps提供的。
問題 #118
You need to run large-scale inference jobs on millions of records periodically. Jobs are not latency-sensitive but must be cost-efficient and scalable. Which deployment option is MOST appropriate?
答案：D
解題說明：
Batch endpoints are optimized for large-scale, asynchronous inference workloads. They efficiently process large datasets and scale based on demand, making them cost-effective for non-real-time scenarios. Online endpoints are designed for low-latency use cases and are more expensive for batch processing.
問題 #119
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.
答案：
解題說明：
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
問題 #120
Drag and Drop Question
You develop a Prompt flow in Microsoft Foundry project.
You plan to use variants and invoke a custom API in the flow.
You need to add tools to the flow that will implement the planned functionality. Your solution must minimize development efforts.
Which tools should you use? To answer, move the appropriate tools to the correct functionalities.
You may use each tool once, more than once, or not at all. You may need to move the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
答案：
解題說明：
問題 #121
You create an Azure Machine Learning workspace. You train an MLflow-formatted regression model by using tabular structured data.
You must use a Responsible AI dashboard to assess the model.
You need to use the Azure Machine Learning studio UI to generate the Responsible AI dashboard.
What should you do first?
答案：B
解題說明：
The first step you must take is to register the model with the workspace.
To access the no-code, guided wizard for generating a Responsible AI dashboard directly within the Azure Machine Learning studio UI, the trained model must first exist as a recognized asset inside your workspace's model registry.
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-responsible-ai-dashboard
問題 #122
Fabrikam Inc. must improve its deployment process because traditional machine learning models are deployed manually and the organization has limited rollback capability .
You need to recommend a deployment approach that supports staged rollout and rollback while minimizing operational overhead.
Which deployment approach should you recommend?
答案：B
解題說明：
Managed online endpoints with traffic splitting are the best fit because Azure Machine Learning supports multiple deployments behind a single online endpoint and allows production traffic to be distributed between them. This enables a blue-green or progressive rollout strategy : deploy the new model version alongside the current version, validate it independently, send a small percentage of production traffic to it, monitor behavior, and then gradually increase traffic. If problems occur, traffic can be redirected immediately to the previous deployment, providing a practical rollback mechanism.
This approach also aligns with Fabrikam's requirement to favor managed services and automation because the team has limited Azure DevOps experience. Managed online endpoints handle much of the serving, scaling, security, and monitoring infrastructure automatically and support both traffic splitting and mirrored traffic for safer validation.
Option B can also implement blue-green deployment, but AKS introduces substantially more infrastructure and operational management than Fabrikam requires. Option A would require custom deployment, routing, scaling, and rollback logic. Option D is intended for asynchronous batch inference rather than staged real-time production serving.
Study Guide Reference: Implement machine learning model lifecycle and operations - managed online endpoints, multiple deployments, traffic splitting, blue-green deployment, staged rollout, monitoring, and rollback.
問題 #123
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