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| Section | Objectives |
|---|
| Design and implement generative AI solutions | - Large language model integration
- 1. Use Azure OpenAI Service capabilities
- 2. Prompt engineering and prompt flow design
- RAG (Retrieval Augmented Generation) solutions
- 1. Knowledge grounding and retrieval design
- 2. Vector search integration
|
| Operationalizing machine learning solutions | - ML lifecycle management
- 1. Model versioning and registry usage
- 2. Model training and evaluation in Azure Machine Learning
- Deployment and monitoring
- 1. Deploy models to endpoints
- 2. Monitor performance and drift
|
| Plan and design AI solutions using Azure AI services | - Requirements gathering and solution architecture
- 1. Select appropriate Azure AI services
- 2. Identify business requirements for AI solutions
- Responsible AI design
- 1. Fairness, transparency, and accountability considerations
- 2. Responsible AI mitigation strategies
|
| Implement secure and scalable AI systems | - Scalability and performance optimization
- 1. Cost optimization strategies
- 2. Autoscaling AI workloads
- Security and governance
- 1. Data privacy and compliance considerations
- 2. Identity and access management for AI services
|
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Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions (Q11-Q16):
NEW QUESTION # 11
Drag and Drop Question
A real-time endpoint is deployed in Azure Machine Learning to serve predictions to a web application.
Users report intermittent failures and unexpected responses when calling the endpoint.
You need to identify the appropriate troubleshooting action for each reported issue.
Which troubleshooting action should you perform for each issue? To answer, move the appropriate troubleshooting actions to the correct issues. You may use each troubleshooting 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:

NEW QUESTION # 12
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 # 13
You use an Azure Machine Learning workspace.
You must monitor cost at the endpoint and deployment level.
You have a trained model that must be deployed as an online endpoint. Users must authenticate by using Microsoft Entra ID.
What should you do?
- A. Deploy the model to a managed online endpoint. During deployment, set the token_auth_mode parameter of the target configuration object to true.
- B. Deploy the model to Azure Kubernetes Service (AKS). During deployment, set the auth.mode parameter to configure the authentication type.
- C. Deploy the model lo Azure Kubernetes Service (AKS). During deployment, set the token_auth_mode parameter of the target configuration object to true.
- D. Deploy the model to a managed online endpoint. During deployment, set the auth_mode parameter to configure the authentication type.
Answer: D
NEW QUESTION # 14
You manage an Azure Machine Learning workspace named workspace1.
You must register an Azure Blob storage datastore in workspace1 by using an access key. You develop Python SDK v2 code to import all modules required to register the datastore.
You need to complete the Python SDK v2 code to define the datastore.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:
Explanation:

Explanation:

NEW QUESTION # 15
A team is working in Microsoft Foundry to test and compare large language model (LLM) prompt variants in a development environment.
The team requires consistent inputs to evaluate prompt variants without relying on live user traffic.
You need to create a controlled evaluation of input data.
Which action should you perform first?
- A. Apply a blocklist.
- B. Generate synthetic interaction data.
- C. Enable observability metrics.
- D. Configure content filters.
Answer: B
Explanation:
In Microsoft Foundry, you can test and compare LLM prompt variants using Prompt Flow. This environment allows you to create a controlled development workflow with consistent inputs, bypassing the need for live user traffic.
1. Generate Synthetic Interaction Data
Generating synthetic data is the recommended first step to build a robust, diverse test dataset without manual effort.
2. Compare Prompt Variants
Once your synthetic dataset is ready, use Prompt Flow to iterate on your prompts.
3. Controlled Evaluation
To measure performance objectively, run automated Eval Runs.
Reference:
https://learn.microsoft.com/en-us/azure/foundry-classic/concepts/prompt-flow
NEW QUESTION # 16
......
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