Q01 - Question
Several reviewers will judge the quality of a shopper support agent before launch. The team wants different reviewers to grade the same response the same way. The team also wants to compare the agent's answers with human support staff answers without risking a live customer. Which two actions should the team take? (Select two.)
Domain: Design and implement a GenAIOps infrastructure (20–25%) Type: Multiple select
- A. Define a rubric with named criteria, each with a description of what a good answer looks like.
- B. Write a display prompt that shoppers can click as an example question.
- C. Rely only on smoke tests, because they are fast to run after every update.
- D. Use shadow rating to run the agent silently alongside human support staff.
A and D are correct.
Explanation: A rubric is a scoring guide with named criteria and a description of what a good answer looks like, ensuring different reviewers grade the same response the same way. Shadow rating runs the agent silently alongside human support staff, allowing you to compare answers without risking a live customer.
B is incorrect: A display prompt is an example question a shopper sees and can click. It does not help reviewers grade responses consistently.
C is incorrect: Smoke tests are short checks with known good answers used to catch obvious breakage, not tools for standardizing reviewer grades or comparing against human staff.
Q02 - Question
A team runs a latency-sensitive production copilot with predictable traffic on a provisioned deployment. The team wants the discounted Azure Reservations rate for the PTU billing meter. The team has PTU quota in the target region. What should the team do?
Domain: Design and implement a GenAIOps infrastructure (20–25%) Type: Single choice
- A. Create the provisioned deployment first to confirm that capacity is available, and then purchase the reservation.
- B. Purchase the reservation first, because a reservation guarantees capacity for the deployment.
- C. Delete and re-create the deployment with each traffic change to stay on hourly billing.
- D. Skip the capacity check, because PTU quota guarantees that capacity is available.
A is correct.
Explanation: Reservations are a discount on the PTU billing meter and don't guarantee capacity. Create the deployment first to confirm that capacity is available, and then purchase the reservation to lock in the discounted rate.
B is incorrect: A reservation doesn't guarantee capacity. Reservations and deployments are created independently, so you can buy a reservation and still be unable to deploy.
C is incorrect: Capacity might not be available when you need to scale back up. Continuous hourly billing at high utilization typically exceeds reservation pricing.
D is incorrect: PTU quota is a policy limit and doesn't guarantee capacity. If capacity in the region is insufficient for the requested PTU count, the deployment fails.
Q03 - Question
A company plans a production generative AI application that uses a Foundry Model sold by Azure. Compliance requires inference data to be processed within a geographic data zone. The company also requires a customizable content filtering configuration. Which deployment option and type should the company use?
Domain: Design and implement a GenAIOps infrastructure (20–25%) Type: Single choice
- A. A managed compute deployment on the Foundry project.
- B. A managed compute deployment that uses the company's existing Azure VM quota.
- C. A Serverless API deployment with the Global Standard deployment type.
- D. A Serverless API deployment with the Data Zone Standard deployment type.
D is correct.
Explanation: Serverless API deployments support regional, data zone, and global data processing. They also provide built-in and customizable content filtering. The Data Zone Standard deployment type keeps processing in a data zone (US, EU, or APAC), which meets the compliance requirement.
A is incorrect: Managed compute supports only global data processing. Content filtering isn't available in the public preview, and the preview isn't recommended for production workloads.
B is incorrect: Existing Azure VM quota can't be applied to a managed compute deployment. This option also has the same global-only processing and content filtering limits.
C is incorrect: Global Standard uses global processing, which doesn't meet the requirement to process data within a geographic data zone.
Q04 - Question
A team that builds an agent on pretrained models, prompts, and data connectors must keep governance, security, and compliance in place during exploration, building, and deployment. The team also wants to balance speed of delivery with strict adherence to standards. Which part of the GenAIOps lifecycle addresses this requirement?
Domain: Design and implement a GenAIOps infrastructure (20–25%) Type: Single choice
- A. The Explore loop, which defines the business need and designs the architecture.
- B. The Build loop, which develops the application and evaluates it against quality and safety targets.
- C. The Operationalize loop, which deploys the application for real-world use.
- D. The management loop, which overarches the other loops and focuses on governance, security, and compliance.
D is correct.
Explanation: The management loop overarches the Explore, Build, and Operationalize phases. It focuses on governance, security, and compliance, and provides a framework that balances speed in deliverables with strict adherence to standards.
A is incorrect: The Explore loop is where you define the business need and design the architecture, not the overarching loop for governance and compliance.
B is incorrect: The Build loop is where you develop the initial application and evaluate it iteratively, not the overarching loop for governance and compliance.
C is incorrect: The Operationalize loop is where you deploy the application for real-world use, not the overarching loop for governance and compliance.
Q05 - Question
A platform team deploys a Foundry resource by using a Bicep template that sets agent capability settings. The template references an existing Azure Storage account, an existing Azure Cosmos DB account, and an existing Azure AI Search resource. The team wants to avoid a deployment that fails partway through because of missing permissions. Which two role assignments must the deployment principal hold before the deployment? Select two.
Domain: Design and implement a GenAIOps infrastructure (20–25%) Type: Multiple choice
- A. A caller role on the Azure AI Search resource.
- B. Storage Blob Data Contributor on the referenced Azure Storage account.
- C. Data-plane roles for the project managed identity on each referenced resource.
- D. Cosmos DB Operator on the referenced Azure Cosmos DB account.
B and D are correct.
Explanation: When a template sets agent capability settings, the deployment principal needs Storage Blob Data Contributor on every referenced Azure Storage account. It also needs Cosmos DB Operator on every referenced Azure Cosmos DB account. A missing assignment fails the deployment partway through. Run az role assignment list to check the principal's current assignments before you deploy.
A is incorrect: Azure AI Search doesn't require a caller role for capability settings provisioning. The deployment principal still needs permission to create or update every resource that the template declares.
C is incorrect: Provisioning permissions and runtime permissions are separate. Grant the project managed identity its data-plane roles after the deployment succeeds, and then confirm that an agent can read and write.
Q06 - Question
A service principal in a partner application must call the endpoints of one agent in a Foundry project. The project contains several other agents. The service principal must not build or modify agents, and it must not reach the endpoints of the other agents. Which role assignment meets the requirements with the least privilege?
Domain: Design and implement a GenAIOps infrastructure (20–25%) Type: Single choice
- A. Assign Foundry User to the service principal at the Foundry project scope.
- B. Assign Foundry Agent Consumer to the service principal at the agent scope by using Azure CLI.
- C. Assign Foundry Project Manager to the service principal at the Foundry resource scope.
- D. Assign Azure AI Developer to the service principal at the Foundry project scope.
B is correct.
Explanation: Foundry Agent Consumer is the least-privilege role for principals that only interact with agents. An agent-scope assignment grants endpoint access to the specified agent only. The Azure portal supports assigning this role only at the Foundry account scope, so use Azure CLI to assign it at the agent scope.
A is incorrect: Foundry User includes build and develop data actions, which the service principal doesn't need. At project scope, it also isn't limited to one agent.
C is incorrect: Foundry Project Manager can manage projects, publish agents, and assign the Foundry User role. This role is broader than the requirement, and the resource scope is wider than one agent.
D is incorrect: Azure AI Developer is scoped to Azure Machine Learning workspaces and Foundry hubs, not to Foundry projects. Use Foundry roles for Foundry project access.
Q07 - Question
A retail team runs a full automated evaluation on its support agent. The agent scores high, but the test dataset contains only the questions that shoppers ask most often. The team wants scores it can trust before launch. What should the team do?
Domain: Design and implement a GenAIOps infrastructure (20–25%) Type: Single choice
- A. Run the smoke tests after every small update and use those results as the launch score.
- B. Add rare edge cases to the dataset, such as ambiguous requests, out-of-stock items, and questions the agent should refuse.
- C. Remove the questions that the agent should refuse so that the dataset stays focused on common use.
- D. Replace the full automated evaluation with a rubric that has no test data.
B is correct.
Explanation: The evidence states that to trust evaluation scores, you must use a deliberate coverage mix. This means choosing test questions that reflect the full range of real use, including common questions and rare edge cases such as ambiguous requests, out-of-stock items, and questions the agent should refuse.
A is incorrect: Smoke tests are short, quick checks run after small updates to catch obvious breakage, not full automated evaluations that score the agent at scale.
C is incorrect: Questions the agent should refuse are a necessary part of the coverage mix. Removing them hides failures that only show up in those situations.
D is incorrect: A rubric is a scoring guide, but you still need test data to run a full automated evaluation and prove the agent works.
Q08 - Question
A security team must restrict outbound traffic from Foundry Agents to a curated list of destinations to reduce the risk of data exfiltration. The list includes some fully qualified domain names (FQDNs). The team doesn't want to build or maintain its own virtual network. Which configuration should the team use?
Domain: Design and implement a GenAIOps infrastructure (20–25%) Type: Single choice
- A. A managed virtual network in Allow internet outbound mode.
- B. A managed virtual network in Disabled mode.
- C. A managed virtual network in Allow only approved outbound mode with outbound rules.
- D. A custom virtual network with a customer-provided Azure Firewall.
C is correct.
Explanation: Allow only approved outbound mode restricts outbound traffic by using service tags, private endpoints, and optional FQDN rules. Microsoft provisions and manages the network, so the team doesn't build its own virtual network. When you add an FQDN rule, a managed Azure Firewall is created. FQDN rules support only ports 80 and 443, and the firewall adds cost.
A is incorrect: Allow internet outbound mode allows all outbound traffic to the internet. It doesn't limit destinations to a curated list.
B is incorrect: Disabled means managed virtual network isolation isn't enabled, unless you use a custom virtual network. It doesn't provide a managed network boundary.
D is incorrect: A custom virtual network requires the team to set up and maintain the network, such as subnet delegation. This conflicts with the requirement to avoid building and maintaining a virtual network.
Q09 - Question
A shopper agent is live on a website. The team wants to see which tools the agent called for each request, track latency, token usage, and error rates, and detect when answer quality drifts from the rubric. Which approach meets these requirements?
Domain: Design and implement a GenAIOps infrastructure (20–25%) Type: Single choice
- A. Use tracing, collect the traces and metrics in Azure Monitor and Application Insights, and run continuous evaluation that re-scores live answers against the same rubric.
- B. Use the Azure Developer CLI (azd) to reprovision the environment from the AI app template after each deployment.
- C. Test shopper questions in the Chat playground in the Foundry portal and compare how natural the answers sound.
- D. Fine-tune the model on past support chats to keep the brand tone consistent.
A is correct.
Explanation: Tracing records each request, including which tools the agent called. Azure Monitor and Application Insights collect those traces alongside metrics like latency, token usage, and error rates. Continuous evaluation re-scores live answers against the rubric to detect if quality drifts.
B is incorrect: The Azure Developer CLI (azd) provisions and deploys the environment. It does not track live metrics, traces, or answer quality.
C is incorrect: The Chat playground in the Foundry portal is used to explore models before committing, not to monitor a live agent in production.
D is incorrect: Fine-tuning trains the model on examples to lock in a tone. It does not provide tracing, metrics, or continuous evaluation of live traffic.
Q10 - Question
An outdoor-gear retailer builds a shopper agent. The product catalog changes constantly as items come and go. The company also wants the agent to answer in a consistent, warm brand voice that matches past support chats. Which design meets both requirements?
Domain: Design and implement a GenAIOps infrastructure (20–25%) Type: Single choice
- A. Fine-tune the model on the current catalog and retrain it each time stock changes.
- B. Use a reasoning model such as o1 so that the agent handles catalog and tone requirements.
- C. Use Retrieval Augmented Generation (RAG) for current catalog data and fine-tune the model on past support chats for tone.
- D. Use only prompt engineering and keep the catalog inside the command prompt.
C is correct.
Explanation: Retrieval Augmented Generation (RAG) lets the agent search external data, such as a constantly changing product catalog, so it always pulls the latest data. Fine-tuning retrains the model on task-specific examples, such as past support chats, to lock in a consistent brand voice and tone.
A is incorrect: A fine-tuned model requires retraining every time stock changes, making it unsuitable for a constantly changing catalog.
B is incorrect: Reasoning models excel at math and complex logic. A chat completion model is better suited for generating natural, conversational text in a specific tone.
D is incorrect: Prompt engineering refines the command prompt, but the evidence recommends RAG for current data and fine-tuning for consistent tone, rather than keeping the catalog inside the prompt.