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Microsoft AI-300 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Plan and design AI solutions using Azure AI services | - Requirements gathering and solution architecture
|
| Implement secure and scalable AI systems | - Security and governance
|
| Operationalizing machine learning solutions | - ML lifecycle management
|
| Design and implement generative AI solutions | - Large language model integration
|
Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:
An organization is deploying generative AI solutions by using Microsoft Foundry to support multiple production workloads.
The organization has the following workload requirements:
* One workload must be real-time, latency-sensitive, and have predictable global usage patterns that demand consistent performance.
* One workload must have variable performance and be optimized for cost-efficient operation.
You need to select a global deployment type for each workload.
Which type of deployment should you use for each workload requirement? 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.
Correct Answer:
Explanation:
Real-time, predictable: Global Provisioned
Flexible, cost efficient: Global Standard
For the real-time, latency-sensitive workload with predictable global demand , use Global Provisioned .
Microsoft Foundry defines Global Provisioned deployments as providing reserved model processing capacity and predictable throughput through provisioned throughput units (PTUs). Because capacity is reserved, this deployment type provides lower and more consistent latency than Global Standard and is specifically recommended for sustained, predictable, high-throughput production workloads.
For the workload requiring flexibility and cost-efficient operation , use Global Standard . Global Standard operates on a pay-per-token model and dynamically routes requests across Azure ' s global infrastructure.
Microsoft recommends it as the starting deployment type for most workloads because it offers the lowest price, broad regional availability, and high default quota without requiring the customer to reserve PTU capacity. This makes it appropriate when demand varies and paying for permanently reserved throughput would be inefficient.
The architectural distinction is therefore straightforward: Global Provisioned trades reserved-capacity cost for predictable throughput and reduced latency variance, while Global Standard provides elastic, consumption-based operation with greater latency variability under sustained high load. Microsoft's deployment overview likewise identifies provisioned throughput for predictable low-latency performance and standard deployment for general hosted workloads.
Study Guide Reference: Design and implement a GenAIOps infrastructure - Microsoft Foundry deployment types, Global Standard, Global Provisioned, PTUs, latency predictability, throughput, and cost optimization.
A team manages an Azure Machine Learning workspace where they deploy models to online endpoints.
The team needs to introduce a new version of a model to production without disrupting existing users.
The team must validate the new version before full rollout.
You need to reduce risk during deployment.
What should you do?
- A. Route all traffic to the new deployment.
- B. Split traffic between deployments.
- C. Replace the existing endpoint.
- D. Deploy the model to a batch endpoint.
Correct Answer: B 🗳️
Explanation: Only visible for PassReview members. You can sign-up / login (it's free).
You train and register an Azure Machine Learning model
You plan to deploy the model to an online endpoint
You need to ensure that applications will be able to use the authentication method with a non-expiring artifact to access the model.
Solution:
Create a managed online endpoint with the default authentication settings. Deploy the model to the online endpoint.
Does the solution meet the goal?
- A. Yes
- B. No
Correct Answer: B 🗳️
You create an Azure Machine Learning model to include model files and a scorning script. You must deploy the model. The deployment solution must meet the following requirements:
* Provide near real-time inferencing.
* Enable endpoint and deployment level cost estimates.
* Support logging to Azure Log Analytics.
You need to configure the deployment solution.
What should you configure? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
You create an Azure Machine Learning workspace. You use the Azure Machine Learning Python SDK v2 to create a compute cluster.
The compute cluster must run a training script. Costs associated with running the training script must be minimized.
You need to complete the Python script to create the compute cluster.
How should you complete the script? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
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