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| Section | Weight | Objectives |
|---|---|---|
| Design and implement an MLOps infrastructure | 15–20% | - Create and manage Machine Learning workspace resources and assets
|
| Implement machine learning model lifecycle and operations | 25–30% | - Monitor and maintain models in production
|
| Implement generative AI quality assurance and observability | 10–15% | - Evaluate and test generative AI applications
|
| Optimize generative AI systems and model performance | 15–20% | - Optimize model selection and configuration
|
| Design and implement a GenAIOps infrastructure | 20–25% | - Set up Microsoft Foundry environment
|
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NEW QUESTION # 40
You use the Azure Machine learning SDK v2 tor Python and notebooks to tram a model. You use Python code to create a compute target, an environment, and a taring script. You need to prepare information to submit a training job.
Which class should you use?
Answer: A
NEW QUESTION # 41
Hotspot Question
You manage a Retrieval-Augmented Generation (RAG) system that uses Azure AI Search to retrieve documents from an indexed knowledge base.
The system must support the following retrieval requirements:
- Queries that include exact policy identifiers must return matching
documents even when semantic similarity is low.
- Natural-language questions must prioritize semantically relevant
documents even when keywords are not an exact match.
You need to configure the retrieval approach to meet the requirements.
How should you configure the retrieval behavior for each requirement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 42
Drag and Drop Question
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.
Answer:
Explanation:
NEW QUESTION # 43
An organization operates a customer-facing generative AI chat service deployed by using Microsoft Foundry. The service processes a predictable, sustained volume of requests. The service must meet strict response time service-level agreements (SLAs) during peak business hours.
The organization requires that:
- Model responses remain consistent during sustained high traffic.
- Latency does not degrade during peak usage periods.
- Capacity planning avoids throttling and unpredictable performance.
You need to ensure that the deployed foundation model can reliably handle sustained, high- volume traffic while meeting performance and availability requirements.
What should you do?
Answer: D
Explanation:
Implement spillover traffic management for excess demand is the best action among the provided options to ensure the system gracefully maintains availability and handles sustained, high-volume traffic without degradation or unpredictable throttling under strict SLAs.
Under strict SLAs and predictable, sustained peak volumes, Azure AI Foundry utilizes provisioned throughput (PTUs) to guarantee latency and throughput. However, if traffic spikes beyond predictable levels, a spillover strategy redirects excess demand to standard pay-as-you- go slots or alternative regions. This prevents the primary deployment from throttling, dropping requests, or suffering from latency degradation.
Reference:
https://smartbridge.com/azure-openai-service-guide/
NEW QUESTION # 44
A team maintains Infrastructure as Code (IaC) templates to provision Azure Machine Learning resources.
Provisioning must be triggered by changes in the templates and executed without manual intervention.
You need to automate resource provisioning.
Which action should you take for each requirement? To answer, move the appropriate actions to the correct requirements. You may use each action 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.
Answer:
Explanation:
Explanation:
Azure Machine Learning infrastructure can be described as Infrastructure as Code using ARM templates, Bicep, or Terraform. To make provisioning fully automated, two separate concerns must be addressed. First, the trigger: a CI/CD system like GitHub Actions or Azure DevOps Pipelines must detect changes to IaC template files in the repository, configured through a branch-push or pull-request trigger. Second, the execution: the CI/CD pipeline runs the provisioning commands such as az ml workspace create or bicep deploy non-interactively using a service principal or managed identity, eliminating the need for a human to run scripts manually. Together, these two mechanisms deliver the GitOps-style automation that modern MLOps practices require, ensuring every template change automatically results in a consistent, auditable infrastructure update.
Microsoft Learn Reference Topic: Automate Azure Machine Learning with GitHub Actions - CI/CD for ML infrastructure
NEW QUESTION # 45
......
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