AI-300測試引擎：Operationalizing Machine Learning and Generative AI Solutions壹次通過考試，Microsoft AI-300

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面對競爭激勵的世界，唯有考取和別人不一樣的證照，才可以充實自己，知識就是力量。購買 Microsoft AI-300 題庫，可以免費享受一年的更新題庫的售后服務，在購買前享有免費試用部分考題DEMO。我們提供PDF和軟體格式的考題，其中PDF版本可以列印，軟體版的題庫可以模擬真實的 Microsoft 的 AI-300 考試。正確率100%，考生可以參照最新的 AI-300 認證部分考題。

最新的 Microsoft Certified AI-300 免費考試真題 (Q61-Q66):

問題 #61
Drag and Drop Question
A company is standardizing generative AI development across multiple teams.
Each team requires an isolated workspace. Governance and shared connections must be centrally managed.
You need to implement a Microsoft Foundry environment structure that supports centralized governance and team isolation.
Which type of configuration should you use for each requirement? To answer, move the appropriate configurations to the correct requirements. You may use each configuration 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 #62
Your model requires access to external APIs using sensitive credentials during inference. You must ensure credentials are not exposed in code, logs, or environment variables. What should you implement?

答案：A

解題說明：
Azure Key Vault with managed identity ensures secure access to sensitive credentials without exposing them in code or configuration. Managed identities eliminate the need for hardcoded secrets. Other approaches, such as config files or environment variables, increase the risk of accidental exposure.


問題 #63
Drag and Drop Question
A company plans to deploy a foundation model in Microsoft Foundry.
The mode must support the following workloads:
- A customer support workload used across multiple regions
- A marketing workload that must remain within a specific region due to data residency requirements You need to select the deployment type.
Which deployment type should you use for each workload? To answer, move the appropriate deployment types to the correct requirements. You may use each deployment type 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 #64
You must ensure full reproducibility of experiments including dataset, code, and environment across multiple runs and workspaces. Which combination of practices is MOST appropriate?

答案：B

解題說明：
Reproducibility requires versioning all components: code, datasets, and environments. Missing any of these elements prevents exact replication of experiments. For example, the same code with different data or dependencies can produce different results, making debugging and auditing difficult.


問題 #65
A team deploys a model to a real-time endpoint in Azure Machine Learning. You deploy some updates to the endpoint.
The endpoint returns errors after the new deployment is released.
You need to restore the service as quickly as possible.
What should you do first?

答案：C

解題說明：
To restore the service as quickly as possible, you can roll back traffic to the previous deployment by updating the traffic allocation settings of your Azure Machine Learning online endpoint.
Azure Machine Learning managed online endpoints support multiple deployments under a single endpoint, allowing for blue-green deployment strategies where you can shift traffic between versions instantly.
Key Benefits of This Approach
Instant Recovery: Traffic shifting is a routing change and does not require redeploying the previous model's code or environment, making it the fastest recovery method.
No Downtime: Because the previous deployment remains "warm" (active but receiving no traffic), the switch happens without interrupting the service.
Isolation for Debugging: You can keep the failing deployment at 0% traffic to inspect its logs using az ml online-deployment get-logs without affecting end users How to Roll Back Traffic If your new deployment (e.g., "green") is returning errors, you can reallocate 100% of the traffic back to the known stable deployment (e.g., "blue") using the following methods:
* Azure CLI: Use the az ml online-endpoint update command to set the traffic percentage:
az ml online-endpoint update --name <your-endpoint-name> --traffic "blue=100 green=0"
* Azure Machine Learning Studio:
Navigate to Endpoints in the left menu.
Select your specific real-time endpoint.
Go to the Details or Live Traffic tab.
Adjust the traffic percentages so the previous deployment receives 100% and the failing deployment receives 0%.
Select Update or Save to apply the changes immediately
Reference:
https://learn.microsoft.com/en-us/answers/questions/1275110/azure-ml-v2-yaml-code-for-live- traffic-allocation


問題 #66
......

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