AI-300 Practice Test 2 — 30 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

Question 11

You are managing a multi-workspace environment. You need to restrict the ability to create new compute clusters to only specific users. Where should you apply this control?

Accepted Answer

Azure RBAC on the Workspace. RBAC (Role-Based Access Control) at the workspace level determines which users can interact with compute resources.

Answer

Network Security Group

Answer

Azure Storage Access Policies

Answer

Azure ML Datastore configuration

Question 12

In Azure ML, what is a 'Component'?

Accepted Answer

A reusable pipeline step. A component is a reusable, versioned building block for an ML pipeline.

Answer

A storage account

Answer

A user role

Answer

A physical server

Question 13

Which Azure feature is used to define infrastructure as code for your Azure ML workspace?

Accepted Answer

Azure Bicep. Azure Bicep is the recommended tool for defining and deploying Azure resources as code.

Answer

Azure Portal

Answer

PowerShell

Answer

Azure CLI

Question 14

You want to automate the testing of your prompt flows as part of your CI/CD pipeline. Which tool should you use?

Accepted Answer

Prompt Flow CLI. The 'pf' (Prompt Flow) CLI is designed to run flows and evaluations programmatically in CI/CD pipelines.

Answer

Azure Logic Apps

Answer

Azure Functions

Answer

Azure AI Search test console

Question 15

Which THREE steps are necessary to successfully fine-tune an Azure OpenAI model?

Accepted Answer

Prepare and upload a training dataset in JSONL format.. Option B is correct because Azure OpenAI fine-tuning requires a training dataset uploaded as a JSONL file with chat-completion formatted examples (messages with role/content pairs), which the service uses to adapt the model. Option D is correct because you must choose a base model that supports fine-tuning (for example gpt-35-turbo, gpt-4o-mini, or babbage-002/davinci-002) and that is available in the region where you create the fine-tune job. Option E is correct because after submitting the fine-tuning job you track its status and metrics (training loss, token usage, and completion state) via the Azure OpenAI Studio or the REST API, and you only deploy the resulting model once the job succeeds. Option A is not needed because Azure OpenAI manages the weight updates internally; you never edit model weights in code. Option C is not required because content filtering is a separate safety configuration and disabling the input filter is neither necessary nor recommended for fine-tuning.

Answer

Manually update the model weights in the code.

Answer

Disable the input filter in the portal.

Question 16

When implementing LLM-as-a-judge for evaluation, which TWO factors can influence the reliability of your results?

Accepted Answer

The specific prompt instructions provided to the judge LLM. Option B is correct because the prompt instructions given to the judge LLM directly shape its evaluation criteria, rubric interpretation, and output format, so vague or biased instructions produce inconsistent, unreliable judgments. Option E is correct because the judge LLM's own capability—its reasoning ability, context window, training quality, and susceptibility to position or verbosity bias—bounds how accurately it can assess responses, making a weaker judge less reliable than a stronger one. Options A, C, and D do not belong: dashboard color is purely cosmetic, workspace token quota affects capacity/cost rather than judgment reliability, and storage account location affects latency, residency, and compliance but not the judge's evaluation quality.

Answer

The color of the application dashboard

Answer

The number of tokens available in the workspace quota

Answer

The location of the storage account

Question 17

A data scientist needs to access data in an Azure Data Lake Storage Gen2 account from Azure ML. You need to ensure the workspace uses the most secure method to access this data. What should you configure?

Accepted Answer

User-Assigned Managed Identity. A User-Assigned Managed Identity lets the Azure ML workspace authenticate to ADLS Gen2 via Microsoft Entra ID without storing credentials, and it can be scoped to specific resources with least-privilege RBAC (e.g., Storage Blob Data Reader). This is Microsoft's recommended, most secure access pattern for workspace-to-storage connectivity.

Answer

Shared Key

Answer

Public Access

Answer

Account SAS

Question 18

You need to monitor the 'latency' and 'token usage' of your model deployments in real-time. Which tool provides the most granular view?

Accepted Answer

Azure Monitor with diagnostic logs. Azure Monitor with diagnostic logs is the correct choice because it captures granular, per-request telemetry from Azure OpenAI and Azure AI model deployments, including latency, token usage (prompt/completion tokens), HTTP status codes, and model-level metrics. Diagnostic settings route these logs to Log Analytics, where KQL queries can slice latency and token consumption by deployment, model, and time window in near real-time. This is the only option that exposes token-level metrics rather than aggregate cost or activity metadata.

Answer

Azure Billing dashboard

Answer

Azure Activity Log

Answer

Azure AI Foundry 'Overview' tab

Question 19

Which THREE security features are essential for a production Azure ML deployment?

Accepted Answer

Key Vault integration.. Key Vault integration (A) is essential because it securely stores and centrally manages secrets, keys, and certificates used by the Azure ML workspace, compute, and endpoints, keeping credentials out of code and configuration files. Managed Identity (B) is essential because it gives the workspace and its compute resources an automatically managed identity in Microsoft Entra ID, enabling passwordless, least-privilege access to Key Vault, Storage, and ACR without embedding credentials. Virtual Network (VNet) (D) is essential because it provides network isolation for the workspace, compute clusters/instances, and private endpoints, restricting traffic to approved subnets and preventing exposure over the public internet. Opening inbound ports in the NSG (C) is not a security feature but a weakening of the perimeter, since production workloads should minimize inbound exposure and instead use private endpoints or Azure Bastion/JIT access. Public IP exposure (E) is not a security feature either, as assigning public IPs increases the attack surface and contradicts the private-networking posture expected in production.

Answer

Open inbound ports in NSG.

Answer

Public IP exposure.

Question 20

Your Azure ML training job fails with an 'Out of Memory' error on a GPU cluster. You need to ensure the job runs successfully without modifying the model code. What should you do?

Accepted Answer

Change the VM size to one with higher memory. The 'Out of Memory' error on a GPU cluster indicates the VM size has insufficient GPU memory (VRAM) for the training job. Changing to a VM size with higher memory (e.g., from NC6 to NC12 or ND series) directly addresses the root cause without modifying model code. Azure ML supports resizing the compute cluster by selecting a different VM SKU, which provides more GPU memory and allows the job to run successfully.

Answer

Increase the idle time for the compute cluster

Answer

Enable auto-scale to zero

Answer

Update the workspace storage account