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| Section | Weight | Objectives |
|---|---|---|
| Design and implement an MLOps infrastructure | 15–20% | - Implement infrastructure as code for Machine Learning
|
| Implement generative AI quality assurance and observability | 10–15% | - Evaluate and test generative AI applications
|
| Design and implement a GenAIOps infrastructure | 20–25% | - Set up Microsoft Foundry environment
|
| Implement machine learning model lifecycle and operations | 25–30% | - Monitor and maintain models in production
|
| Optimize generative AI systems and model performance | 15–20% | - Optimize model selection and configuration
|
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180. Frage
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?
Antwort: B
Begründung:
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/
181. Frage
Drag and Drop Question
A team deploys a classification model to production and monitors performance and data changes.
The team wants to ensure that significant drops in prediction accuracy automatically trigger the following:
- Stakeholders must be notified of the drops.
- Retraining must be initiated when thresholds are exceeded
You need to configure monitoring to meet the requirements.
Which four actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
Antwort:
Begründung:
182. Frage
You create an Azure Machine Learning workspace and install the MLflow library.
You need to tog different types of data by using the MLflow library.
Which method should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Antwort:
Begründung:
Explanation:
183. Frage
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.
Antwort:
Begründung:
184. Frage
You are reviewing a dataset that will be used for an advanced fine-tuning job in Microsoft Foundry.
The fine-tuning job uses preference comparison data.
You review the following dataset excerpt.
For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.
Antwort:
Begründung:
Explanation:
Preference comparison data with chosen versus rejected response pairs is the input format for Direct Preference Optimization (DPO) or RLHF-style fine-tuning - an advanced fine-tuning technique available in Microsoft Foundry. A valid DPO dataset record must have three fields: a prompt as the input, a chosen field containing the preferred response, and a rejected field containing the less preferred response. The file must be in JSONL format with UTF-8 encoding, where each line represents one complete preference pair. When evaluating statements about this dataset, mark True if the dataset contains all three required fields and chosen responses represent higher-quality outputs than rejected ones. Mark False if the format is incompatible with DPO requirements, if the required rejected field is missing, or if the chosen and rejected responses appear to be of equivalent quality with no clear preference signal.
Microsoft Learn Reference Topic: Advanced fine-tuning with preference data in Microsoft Foundry - DPO dataset format
185. Frage
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