AI-300 Practice Test 6 — 10 Questions

Question 1

Which TWO of the following are recommended methods for identifying 'hallucinations' in a RAG system?

Accepted Answer

Using an LLM-as-a-judge to compare against ground truth. Comparing output to source context (groundedness) and using a reference-based evaluation are standard methods.

Answer

Checking for grammatical errors

Answer

Measuring model response speed

Answer

Counting total prompt tokens

Question 2

You are configuring a connection to a vector store. What is the best way to handle the secret credential?

Accepted Answer

Store in Azure Key Vault and reference in the connection.. Key Vault is the centralized store for all application secrets in Azure, which should be linked to the project connection.

Answer

Hardcode the key in the flow configuration.

Answer

Pass it as a parameter in the code.

Answer

Store it in an environment variable.

Question 3

A team is scaling a GenAI application that utilizes multiple models via model-as-a-service. You notice that inference requests are being throttled. What is the most effective infrastructure-level adjustment to handle the increased load?

Accepted Answer

Purchase and deploy Provisioned Throughput Units (PTUs) for the specific model deployment.. Provisioned throughput units (PTUs) allow for dedicated capacity, which is the standard way to prevent throttling for high-scale GenAI workloads in Azure.

Answer

Change the model version to a lower-parameter variant.

Answer

Move the deployment to a different subscription within the same Azure region.

Answer

Increase the number of instances in the associated Azure Kubernetes Service (AKS) cluster.

Question 4

You want to automate the evaluation of your LLM application using a 'Golden Dataset'. What is the primary purpose of this dataset in an MLOps pipeline?

Accepted Answer

To serve as a baseline for measuring performance improvements. A Golden Dataset serves as the ground truth to compare model outputs against during automated evaluation runs.

Answer

To increase the model training speed

Answer

To reduce the number of tokens used

Answer

To generate new prompts for users

Question 5

Which THREE features does Azure AI Foundry provide to help developers?

Accepted Answer

Evaluation.. Model Catalog, Prompt Flow, and Evaluation are key developer-facing features in Azure AI Foundry.

Answer

Azure Active Directory creation.

Answer

Direct physical hardware access.

Question 6

When using Prompt Flow for GenAI, where should you store your evaluation results to visualize them over time?

Accepted Answer

Azure Machine Learning workspace. Prompt Flow logs evaluation runs to the Azure Machine Learning workspace, where they can be viewed in the Runs tab.

Answer

GitHub repository

Answer

Azure Blob Storage

Answer

Local file system

Question 7

When configuring a private endpoint for your Azure AI resource, which TWO of the following must be set up?

Accepted Answer

A virtual network with a dedicated subnet.. A private endpoint requires a target subnet and a private DNS zone for resolution.

Answer

A load balancer.

Answer

A firewall rule allowing all traffic.

Answer

A public IP address.

Question 8

Your team is deploying a large language model in Azure AI Foundry. You need to ensure that the inference endpoint remains highly available across different regions. Which configuration should you implement?

Accepted Answer

Enable global load balancing using Azure Front Door to route requests to multiple regional endpoint deployments.. Azure AI Foundry supports multi-region endpoints via Azure Front Door or traffic manager integration to distribute requests across regional inference endpoints for high availability.

Answer

Configure a Load Balancer at the VNet level for the model endpoint.

Answer

Set the model deployment to 'High Availability' mode in the Azure AI Foundry portal.

Answer

Replicate the underlying storage account for the model weights across regions.

Question 9

You are deploying a model that requires a high-memory compute for inference. You are using a 'Managed Online Endpoint'. Where do you specify the instance type for this deployment?

Accepted Answer

In the 'Deployment' YAML file under 'instance_type'.. The instance type is defined in the 'Deployment' configuration object, typically in the 'instance_type' field.

Answer

In the 'Endpoint' YAML file.

Answer

In the 'Environment' definition.

Answer

In the 'Workspace' settings.

Question 10

You are setting up a CI/CD pipeline for GenAI models. You need to evaluate the model's performance on a benchmark dataset before it is promoted to the production endpoint. Which Azure AI Foundry capability should be integrated into your pipeline?

Accepted Answer

Prompt Flow evaluation runs.. Evaluation runs in Prompt Flow allow for automated testing of model quality against datasets, which is a required step for a robust GenAI CI/CD pipeline.

Answer

Model catalog benchmarking.

Answer

Azure AI Content Safety scanning.

Answer

Azure Machine Learning model drift monitor.