Microsoft · Free Practice Questions · Last reviewed May 2026
30 real exam-style questions organised by domain, each with the correct answer highlighted and a plain-English explanation of why it's right — and why the others are wrong.
You are monitoring the cost of your GenAI infrastructure. You want to track usage per specific project. Which feature in Azure AI Foundry should you use?
Use 'Resource Tags' on the Azure AI project resource.
Resource Tags applied to the Azure AI project resource let Cost Management attribute and filter spend per project, satisfying the requirement to track usage for a specific project. Tags propagate to billing records, enabling grouped cost reporting without deploying separate subscriptions.
Configure 'Cost Analysis' alerts in the Azure Portal.
Implement 'Azure Policy' to restrict resource types.
Switch to 'Pay-As-You-Go' subscription model.
Your team is using Semantic Kernel for agent orchestration. You notice frequent transient errors when the agent interacts with multiple LLM services. How should you implement infrastructure-level resiliency?
Disable the 'Streaming' flag in the agent request.
Use an 'Azure API Management' policy to implement a retry pattern.
Azure API Management policies apply retry logic at the gateway layer, so transient faults from multiple LLM services are absorbed before reaching Semantic Kernel. This delivers infrastructure-level resiliency without embedding retry code in agent orchestration, satisfying the requirement to handle frequent transient errors centrally.
Switch the model to a higher tier instance type.
Increase the 'Timeout' parameter in the model deployment manifest.
You need to audit all model access logs across your organization. What is the most effective approach?
Review individual request headers in the app logs.
Enable 'Diagnostic Settings' to send logs to a 'Log Analytics Workspace'.
Diagnostic Settings stream resource logs from each model endpoint into a Log Analytics Workspace, giving centralised, queryable retention across the organisation. This satisfies the audit requirement because logs persist beyond transient local storage and can be queried with KQL across subscriptions.
Use 'Azure Advisor' to detect unauthorized access.
Download the 'Model Usage' report from the billing console.
You are implementing an agentic workflow using Azure AI Foundry. The agent needs to access a secure external API. How should you store the API key securely within the project?
Pass the key as an environment variable in the deployment YAML.
Hardcode the key in the prompt template.
Save the key in a local '.env' file in the code repository.
Store the key in an 'Azure Key Vault' and reference it as a 'Connection' in the AI Project.
Azure Key Vault stores the API key as a managed secret, and the AI Project Connection references it, so the agent retrieves credentials at runtime without hard-coding them. This satisfies the stem's requirement for secure storage within the project.
You need to ensure that your LLM responses are filtered for harmful content. Which infrastructure component provides this capability?
Azure Monitor
Azure Key Vault
Azure AI Content Safety
Azure AI Content Safety supplies the filtering layer, analysing prompts and completions for harmful categories such as violence, hate and self-harm. It integrates directly with Azure OpenAI deployments, satisfying the stem's requirement that LLM responses be filtered, unlike storage or identity components such as Microsoft Entra ID.
Azure Bastion
An agent orchestration system is timing out because it waits too long for tool output. How can you optimize the infrastructure handling of tool calls?
Increase the 'CPU count' on the hosting server.
Implement 'Asynchronous' execution patterns for long-running tools.
Asynchronous execution decouples tool invocation from the orchestration wait, letting the agent continue or poll rather than block on a synchronous response. This directly addresses the timeout constraint by removing the blocking call that exceeds the orchestration deadline for long-running tools.
Increase the 'Global Timeout' for the entire agent.
Reduce the 'System Prompt' size.
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Practice this domainA 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?
Shared Key
User-Assigned Managed Identity
A user-assigned managed identity gives the workspace an Entra ID identity whose credentials Azure manages and rotates, so no secrets are stored. Assigning it the Storage Blob Data Reader role on the Gen2 account satisfies the most-secure-access constraint.
Public Access
Account SAS
You want to automate the deployment of a model using GitHub Actions. Which file format is standard for defining the Azure ML CLI v2 deployment configuration?
JSON
XML
Bicep
YAML
YAML is the standard declarative format for Azure ML CLI v2 deployment configuration, letting GitHub Actions invoke az ml online-deployment create with a version-controlled spec. This satisfies the stem's automation requirement by defining the deployment reproducibly in CI.
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?
Increase the idle time for the compute cluster
Enable auto-scale to zero
Change the VM size to one with higher memory
Selecting a VM size with more GPU memory increases available device memory, resolving the out-of-memory failure without altering model code. This satisfies the stem's constraint of fixing the job while leaving the training script unchanged.
Update the workspace storage account
You are creating a CI/CD pipeline in Azure DevOps. Which extension is essential to integrate Azure ML tasks into your build/release pipeline?
Visual Studio Code extension
Terraform extension
Azure Machine Learning extension
The Azure Machine Learning extension adds pipeline tasks to Azure DevOps, letting build and release pipelines trigger Azure ML jobs, register models and deploy endpoints. Without it, no native Azure ML task exists in the pipeline, so the CI/CD integration fails.
Kubernetes extension
You are setting up an Azure Machine Learning workspace and need to ensure that the workspace is not accessible from the public internet. Which networking feature should you configure?
Azure Bastion
Application Gateway
Private Endpoint
A private endpoint creates a private IP address within your virtual network for the Azure Machine Learning workspace, eliminating public internet exposure entirely. This directly satisfies the requirement that the workspace must not be accessible from the public internet, since traffic traverses the Microsoft backbone network rather than public routes.
Service Tags
You need to monitor the health of your deployed Azure ML models. Which service is integrated directly with Azure ML to provide automated metrics and logging?
Azure Event Grid
Azure Application Insights
Application Insights integrates directly with Azure ML, receiving telemetry from deployed endpoints and automatically collecting request latency, failure rates and dependency traces. This satisfies the requirement for automated metrics and logging without custom instrumentation, unlike Log Analytics or Monitor, which require manual wiring.
Azure Policy
Azure Data Factory
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Practice this domainYou are fine-tuning a model on Azure OpenAI and notice the training loss curve is fluctuating significantly. What is the most likely cause?
The model's temperature is set too high.
The training dataset size is too large.
The model lacks enough epochs.
The learning rate is too high.
An excessively high learning rate makes each gradient step overshoot the loss minimum, so the loss oscillates or diverges rather than settling. Lowering it, or applying a warmup and decay schedule, stabilises convergence and smooths the fluctuating curve.
Which parameter in the Azure OpenAI API should be adjusted to make the model's output more deterministic and repeatable?
max_tokens
presence_penalty
temperature
Temperature scales the randomness of token sampling; lowering it towards zero sharpens the probability distribution so the highest-likelihood token is chosen consistently. This directly satisfies the requirement for deterministic, repeatable output rather than varied creative responses.
frequency_penalty
To optimize costs for an enterprise chatbot, you want to implement token usage monitoring. Which Azure service should you integrate to track token consumption per user?
Azure Data Factory.
Azure Cognitive Search.
Azure SQL Database.
Azure Monitor with Log Analytics.
Azure Monitor with Log Analytics captures per-request token metrics emitted by Azure OpenAI, letting you query consumption grouped by authenticated user via Microsoft Entra ID. This satisfies the stem's requirement to track token usage per user, enabling cost attribution and optimisation across the enterprise chatbot's workload.
You are optimizing a long-context application. Which technique is most effective for reducing context window costs in Azure OpenAI?
Using a smaller batch size.
Summarizing conversation history before passing to the next prompt.
Summarising prior turns replaces the full verbatim history with a condensed summary, shrinking the prompt token count that drives context window charges. This directly reduces per-request cost while preserving conversational continuity for the next prompt.
Disabling streaming responses.
Increasing the frequency penalty.
Your Azure OpenAI deployment is experiencing high latency during peak hours. You observe that input tokens are consistently high. Which strategy is most effective for reducing latency while maintaining quality?
Implement prompt caching for static instructions.
Prompt caching stores the static instruction prefix so repeated requests reuse its computed key-value state, cutting prefill work on every call. Since the stem identifies consistently high input tokens as the driver of peak-hour latency, caching that unchanging prefix directly reduces time-to-first-token while leaving model quality untouched.
Increase the top_p parameter.
Enable dynamic scaling in Azure App Service.
Decrease the number of parallel requests.
Use a higher model version (e.g., o1-preview).
Which feature in Azure OpenAI allows you to reserve throughput for a consistent user experience during high demand?
Regional failover.
Auto-scaling groups.
Provisioned Throughput Units (PTUs).
Provisioned Throughput Units reserve dedicated model capacity on Azure OpenAI, guaranteeing consistent latency and throughput under load. Unlike pay-as-you-go token billing, which shares capacity and throttles during demand spikes, PTUs satisfy the stem's requirement for a consistent user experience during high demand.
Token throttling.
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Practice this domainIn an LLM evaluation workflow, what does the 'Coherence' metric measure?
The length of the response
The logical flow and structure of the output
Coherence assesses whether sentences and ideas connect logically, producing a structured, readable response. This satisfies the stem's focus on the metric's definition within LLM evaluation, distinguishing it from fluency (grammatical smoothness) and relevance (alignment to the prompt). It measures organisation and logical progression across the output.
The presence of offensive language
The factual accuracy of the answer
You are monitoring an Azure OpenAI deployment and need to identify if a model is outputting content that violates safety policies. Which Azure AI Content Safety feature should you enable to categorize harmful content?
Azure Monitor Logs
Application Insights Profiler
Content Safety API text analysis
Content Safety API text analysis returns severity scores across four harm categories — hate, violence, sexual and self-harm — directly satisfying the requirement to categorise violating output. Unlike prompt shields, which detect injection attacks, or groundedness detection, it classifies the model's generated content itself, enabling automated monitoring and filtering decisions.
Prompt Flow Guardrails
You are building a RAG application and notice that the model sometimes hallucinates information not present in the retrieved documents. Which evaluation metric should you prioritize to mitigate this?
Groundedness
Groundedness measures whether the model's response is factually supported by the retrieved source documents. Prioritising it directly targets hallucination, since low groundedness scores flag answers containing claims absent from the retrieved context, satisfying the mitigation requirement.
Relevance
Performance
Fluency
You are deploying a GenAI app to production and want to track the 'Token Usage' and 'Latency' metrics per user session. Which service should you integrate with your application?
Azure Application Insights
Azure Application Insights captures custom telemetry such as token counts and latency, letting you attach per-session properties to each event. This satisfies the requirement to track Token Usage and Latency metrics per user session, since Application Insights supports custom metrics and dimensions that can be segmented by session identifier.
Azure Container Registry
Azure Key Vault
Azure Policy
Azure AI Search
When using Prompt Flow for GenAI, where should you store your evaluation results to visualize them over time?
GitHub repository
Azure Blob Storage
Azure Machine Learning workspace
Prompt Flow logs evaluation runs and metrics into a linked Azure Machine Learning workspace, which persists results and provides run history. This lets you compare evaluation scores across runs over time, satisfying the requirement to visualise results longitudinally.
Local file system
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?
Adversarial input classification using a dedicated judge model
A dedicated judge model trained to classify adversarial inputs detects manipulation attempts by pattern and intent, rather than relying on static keyword rules. This catches novel jailbreak phrasing designed to bypass system instructions, which signature-based filtering misses.
Token usage tracking
Latency threshold monitoring
Semantic similarity scoring
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Practice this domainYou need to implement a retraining trigger based on performance degradation. Which TWO metrics should you monitor to decide when to retrain?
CPU usage of the inference cluster
Network latency
Number of active users
Model prediction precision
Precision measures the proportion of positive predictions that are actually correct, so a sustained drop signals the model is degrading on the positive class. Monitoring it alongside recall directly satisfies the stem's requirement to detect performance degradation and trigger retraining, rather than tracking infrastructure or data-drift signals alone.
Model prediction accuracy
Prediction accuracy directly quantifies performance degradation, so monitoring it reveals when the model's outputs drift below an acceptable threshold. This satisfies the stem's requirement for a metric that triggers retraining, since declining accuracy signals the deployed model no longer generalises to current data.
You are configuring a CI/CD pipeline for model deployment. Which THREE actions must you perform to ensure model traceability?
Use a shared local folder for model artifacts
Register the model in the Azure ML Model Registry with versioning
Registering the model in the Azure ML Model Registry with versioning assigns each artefact an immutable identifier and lineage record. This satisfies the stem's traceability requirement, letting any deployed endpoint be mapped back to the exact registered model version used in the pipeline.
Add experiment tags to the model artifact
Experiment tags attach searchable metadata to the model artefact, linking it to the run, dataset and configuration that produced it. This satisfies the stem's traceability requirement by making the artefact's provenance queryable within the CI/CD pipeline.
Delete all training logs after deployment
Store the training script hash in the model properties
Storing the training script hash in the model properties creates a cryptographic link between the artefact and the exact code that produced it. This satisfies the stem's traceability requirement, proving which source revision generated the deployed model.
You are building an Azure Machine Learning pipeline. You need to ensure that the pipeline components are reusable and versioned independently. What should you use?
Azure ML Datasets
Azure ML Pipelines Steps
Azure ML Components
Azure ML Components define each pipeline step as a self-contained, versioned artefact with declared inputs, outputs and environments, so steps can be registered, reused and versioned independently across pipelines. This directly satisfies the stem's reusability and independent versioning constraint, unlike monolithic pipeline scripts or inline command jobs.
Azure Container Registry images
You need to monitor the data drift of a model deployed in Azure ML. What is the first step you must take?
Configure Application Insights
Run a manual evaluation script
Create a Data Drift Monitor
A Data Drift Monitor must exist before drift can be detected; creating it defines the baseline dataset, target dataset and schedule. Without this monitor resource, Azure ML has no comparison to evaluate, so it is the prerequisite first step.
Enable Azure Monitor logs
Your team needs to share a model across different workspaces. What is the most efficient way to achieve this in Azure Machine Learning?
Export the model as a pickle file and upload to each workspace.
Duplicate the workspace storage account.
Re-train the model in every workspace.
Use a shared Azure Machine Learning Registry.
A Microsoft Entra ID-backed Azure Machine Learning Registry stores models once at the tenant level, so workspaces in different regions or subscriptions consume the same versioned artefact by reference rather than copying it, directly satisfying the cross-workspace sharing requirement with minimal duplication.
You are implementing a retraining trigger for a demand forecasting model. You want to trigger a pipeline execution only when the drift metric for the 'Price' feature exceeds a predefined threshold. Which service should you integrate with Azure Machine Learning?
Azure Batch schedules.
Azure Data Factory triggers.
Azure Monitor alerts on logs.
Azure Event Grid and Logic Apps.
Azure Event Grid routes the drift event emitted by Azure Machine Learning to Logic Apps, which evaluates the 'Price' threshold and triggers the pipeline. This event-driven integration fires only on threshold breach, unlike scheduled retraining.
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Practice this domainThe vendor does not publish an exact question count for the AI-300; the exam lasts 100 minutes. The passing score is 700 (scaled 1–1,000). Always confirm current details on the vendor's official exam page.
Scenario-based questions covering exam objectives with detailed answer explanations.
The exam covers 5 domains: Genaiops Infrastructure, Mlops Infrastructure, Generative AI Optimization, Generative AI Quality Assurance And Observability, ML Model Lifecycle And Operations. Questions are weighted by domain — higher-weight domains appear more on your actual exam.
No. These are original exam-style practice questions written against the official Microsoft AI-300 exam objectives. They are not copied from the real exam. Courseiva focuses on genuine understanding, not memorisation of braindumps.
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