AI-300 Practice Test 4 — 10 Questions

Question 1

A developer is troubleshooting an agent that is failing to connect to an external tool. Which THREE of the following infrastructure settings should be verified?

Accepted Answer

The Azure AI Foundry 'Connections' configuration for the specific tool.. Option A is correct because the Azure AI Foundry 'Connections' configuration defines the endpoint, authentication, and credentials the agent uses to reach an external tool, so a misconfigured connection would directly cause connection failures. Option B is correct because the Prompt Flow runtime relies on environment variables (for example API keys, endpoint URLs, or proxy settings) to authenticate and route requests to external tools, and missing or incorrect values break connectivity. Option E is correct because outbound traffic from the runtime must be permitted by Network Security Group rules; if the NSG blocks the destination port or IP, the agent cannot establish the connection. Option C is not relevant because the Azure Resource Group location is a metadata/placement attribute and does not govern outbound connectivity or tool authentication. Option D is not relevant because the LLM model version affects model behavior and output quality, not the network or credential path used to connect to an external tool.

Answer

The Azure Resource Group location.

Answer

The model version of the LLM.

Question 2

You need to detect 'jailbreak' attempts in your RAG application. You are implementing a custom evaluation pipeline. Which technique is most effective for identifying adversarial inputs designed to bypass system instructions?

Accepted Answer

Adversarial input classification using a dedicated judge model. Adversarial evaluation using a 'jailbreak' detection model or prompt-based evaluation is the standard approach to identify bypass attempts.

Answer

Token usage tracking

Answer

Latency threshold monitoring

Answer

Semantic similarity scoring

Question 3

You are onboarding a team to Azure AI Foundry. You need to assign the minimum permissions for a developer to deploy and test a model. Which role should you assign?

Accepted Answer

Azure AI Developer. The 'Azure AI Developer' role provides sufficient permissions to manage resources and perform model deployments within the project.

Answer

Owner

Answer

Reader

Answer

Contributor

Question 4

What is the primary function of a 'Prompt Template' in an Azure AI Prompt Flow?

Accepted Answer

To define the structure of the prompt with dynamic inputs. A prompt template allows users to define the structure of the prompt while injecting dynamic variables.

Answer

To cache previous responses

Answer

To perform load balancing

Answer

To automatically retrain the model

Question 5

What is the primary benefit of 'Prompt Versioning' in an MLOps lifecycle?

Accepted Answer

It ensures reproducibility and enables easy rollbacks. Prompt versioning ensures reproducibility by tracking changes to prompts over time, allowing you to revert to a previous version if a new prompt degrades performance. It also enables easy rollbacks, which is critical in MLOps for maintaining stable production systems. It does not directly affect training speed, evaluation necessity, or test generation.

Answer

It increases model training speed

Answer

It eliminates the need for evaluation

Answer

It automatically generates unit tests

Question 6

You are planning a production GenAI deployment. Which THREE of the following are necessary infrastructure considerations?

Accepted Answer

Implementing Private Link for network isolation.. Security, scalability, and monitoring are the three pillars of a production-ready AI infrastructure.

Answer

Using the cheapest SKU regardless of capacity.

Answer

Hardcoding credentials in the code.

Question 7

What is the purpose of a 'System-Assigned Managed Identity' in the context of an Azure AI project?

Accepted Answer

To provide a secure identity for the resource to interact with other Azure services.. Managed identities allow the project or compute resource to securely access other Azure resources (like Key Vault or storage) without managing explicit credentials.

Answer

To automatically encrypt all data in transit.

Answer

To allow users to log in to the project portal.

Answer

To provide public IP connectivity.

Question 8

You want to automate the scaling of your AI agent orchestration. Which TWO components do you need to configure?

Accepted Answer

Autoscale policies.. Auto-scaling is driven by metrics and defined by policies that govern instance counts.

Answer

A manual human-in-the-loop review.

Answer

Hardcoded instance counts.

Answer

Static IP addresses.

Question 9

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 10

You are configuring a CI/CD pipeline for model deployment. Which THREE actions must you perform to ensure model traceability?

Accepted Answer

Register the model in the Azure ML Model Registry with versioning. Option B is correct because registering the model in the Azure ML Model Registry with versioning creates an immutable, uniquely identifiable record (name:version) that links each deployed artifact to its exact lineage, enabling rollback and audit. Option C is correct because adding experiment tags to the model artifact attaches metadata such as run ID, dataset version, and git commit, which are queryable and provide the contextual provenance needed to trace a model back to its training experiment. Option E is correct because storing the training script hash in the model properties creates a cryptographic fingerprint that proves which exact code produced the model, ensuring reproducibility and detecting any drift between the deployed model and its source code. Option A is not appropriate because a shared local folder is not versioned, not immutable, and not accessible across pipeline agents, so it cannot guarantee traceability. Option D is incorrect because deleting training logs destroys the audit trail and provenance evidence required for traceability.

Answer

Use a shared local folder for model artifacts

Answer

Delete all training logs after deployment