AI-103 Practice Test 29 — 15 Questions

Question 1

When configuring vector embeddings and search indexes in Azure AI Search for RAG, which THREE factors directly impact vector search performance and recall accuracy? (Choose THREE)

Accepted Answer

Embedding model dimension size and consistency with source generation. Option A is correct because the embedding model's dimension size determines the vector field's dimensionality in the Azure AI Search index, and it must match the dimensions produced by the model used to generate the query embeddings; a mismatch causes search failures or degraded recall, and higher dimensions generally increase accuracy at the cost of memory and latency. Option C is correct because HNSW parameters directly govern the approximate nearest neighbor graph: 'm' controls the number of bidirectional links per node (affecting index size and recall), while 'efSearch' (efConstruction/efSearch) tunes the breadth of the search during query time, trading latency for recall. Option D is correct because the similarity metric (Cosine, Dot Product, or Euclidean) must align with how the embedding model was trained; using the wrong metric distorts distance comparisons and reduces recall accuracy. Option B is incorrect because Azure App Service CPU architecture is unrelated to Azure AI Search vector indexing or querying, which runs on the search service's own infrastructure. Option E is incorrect because Azure Front Door DNS CNAME records only affect traffic routing and hostname resolution, not vector search performance or recall.

Answer

Azure App Service CPU architecture (x64 vs ARM)

Answer

Configuring Azure Front Door DNS CNAME records

Question 2

You are setting up cost alerts for your Azure subscription, which contains several Azure AI services resources. You want to receive an email notification when your month-to-date spending reaches 80% of your $5,000 budget. Which feature should you configure?

Accepted Answer

Azure Cost Management budgets and alerts. Azure Cost Management budgets and alerts allow you to set a budget and configure alerts based on actual or forecasted spending thresholds, such as 80% of a $5,000 budget. This is the correct feature for cost monitoring and notifications.

Answer

Microsoft Defender for Cloud security alerts

Answer

Azure Monitor activity log alerts

Answer

Azure Advisor cost recommendations

Question 3

Which TWO techniques are effective for improving the performance of an Azure AI application?

Accepted Answer

Implementing caching for common query results.. Option D is correct because implementing caching for common query results reduces the number of calls made to the Azure AI service, lowering latency and throttling risk while returning frequently requested data faster from a local or distributed cache such as Azure Cache for Redis. Option E is correct because deploying the AI resource in a region closer to end-users reduces network round-trip time (RTT) and thus improves perceived response latency for the application. Option A is not correct because adding subscription keys does not increase throughput or performance; keys are for authentication, and quota/rate limits are tied to the resource and pricing tier, not the number of keys. Option B is not correct because disabling logging does not meaningfully improve AI inference performance and removes valuable diagnostics and monitoring. Option C is not correct because the Free tier has lower rate limits and quotas than paid tiers, so switching to it would degrade rather than improve performance.

Answer

Increasing the number of subscription keys.

Answer

Disabling all logging to reduce overhead.

Answer

Switching to the 'Free' tier.

Question 4

You are managing costs for a multi-tenant Azure AI project. You need to attribute usage costs specifically to the 'Research' department. What is the most effective approach?

Accepted Answer

Implement Azure Policy to enforce mandatory tags on all new resources.. Azure Policy can enforce mandatory tags (such as Department=Research) on resource creation, ensuring consistent cost attribution across the multi-tenant project. Once tags are enforced, Azure Cost Management can group and filter costs by the Department tag, giving accurate per-department chargeback. This is the most effective and scalable approach because it prevents untagged resources from being created in the first place.

Answer

Disable diagnostic logging for the research resources to reduce billing.

Answer

Create a separate resource group for each department.

Answer

Change the pricing tier of the resource to 'Free'.

Question 5

You are configuring a connection between Azure AI Foundry and an Azure OpenAI resource. Which credential type is recommended for secure, keyless authentication between services in Azure?

Accepted Answer

Managed Identity with Azure RBAC. Managed Identity with Azure RBAC is the recommended keyless authentication method because Azure AI Foundry and Azure OpenAI are both Azure services that support Microsoft Entra ID token-based authentication. A system-assigned or user-assigned managed identity eliminates secrets entirely, and RBAC role assignments (such as Cognitive Services OpenAI User) scope exactly what the identity can do. This removes the risk of key leakage, rotation overhead, and credential sprawl in configuration files.

Answer

Account primary access key stored in application configuration

Answer

Hardcoded personal access tokens (PAT)

Answer

Anonymous public access with IP whitelisting

Question 6

You are migrating a custom extraction solution from a Custom Template model to a Custom Neural model in Azure AI Document Intelligence. What is a key advantage of choosing a Custom Neural model over a Custom Template model?

Accepted Answer

Custom Neural models can automatically generalize across documents with varying visual layouts and structures.. Custom Neural models in Azure AI Document Intelligence use deep learning to learn the semantic and structural patterns of documents, enabling them to generalize across varying visual layouts, fonts, and formats. This is the key advantage over Custom Template models, which rely on fixed positional anchors and fail when layouts shift. Neural models are therefore preferred for documents with high variability.

Answer

Custom Neural models only support English language documents.

Answer

Custom Neural models require zero training documents.

Answer

Custom Neural models do not support table extraction.

Question 7

Your application needs to scan user input for Personally Identifiable Information (PII) such as Social Security numbers, credit card numbers, and physical addresses before storing logs in a cloud database. Which Azure AI Language service feature should you invoke?

Accepted Answer

Named Entity Recognition (NER) with PII detection. NER with PII detection is the Azure AI Language capability that identifies and classifies sensitive entity types — SSNs, credit card numbers, addresses, names, emails — and returns their offsets so you can redact or block them before persisting logs. It is the only option that both detects and categorizes privacy-sensitive data at the entity level. The other features analyze tone, summarize, or extract topics, none of which identify PII.

Answer

Sentiment analysis

Answer

Text summarization

Answer

Key phrase extraction

Question 8

You are configuring an agent in Azure AI Foundry that needs to perform mathematical calculations accurately. Large language models often make arithmetic errors. How should you equip the agent to handle calculations reliably?

Accepted Answer

Provide a code interpreter tool so the model can write and execute Python code for calculations.. Large language models are probabilistic token predictors and frequently make arithmetic errors, especially with multi-digit operations. Providing a code interpreter tool lets the agent generate and execute Python code, delegating computation to a deterministic runtime that returns exact results. This is the reliable pattern for math in agentic workflows.

Answer

Set the model temperature to 0.0 and ask it to guess the result.

Answer

Increase the presence penalty to force new tokens.

Answer

Instruct the model in the system prompt to double-check its arithmetic mentally.

Question 9

Which THREE of the following are common Azure cost management tools that you should use to manage your AI solution budget?

Accepted Answer

Azure Budgets.. Azure Budgets (A) is correct because it lets you set spending thresholds on a scope such as a subscription, resource group, or resource and trigger alerts or actions when actual or forecasted costs approach or exceed the limit, which is essential for controlling an AI solution budget. Azure Advisor cost recommendations (B) is correct because it analyzes your resource usage and configuration and surfaces specific, actionable suggestions — such as right-sizing underutilized VMs or removing idle resources — that directly reduce AI workload spend. Azure Cost Analysis (C) is correct because it provides the reporting and visualization layer for cost management, allowing you to break down and filter actual costs by service, resource, tag, and time period so you can understand where AI-related spending is going. Microsoft Entra ID (D) is an identity and access management service, not a cost management tool, and Azure Container Registry (E) is a private Docker/OCI image registry for storing and managing container images, so neither addresses budget monitoring or optimization.

Answer

Azure Active Directory.

Answer

Azure Container Registry.

Question 10

You are exploring Azure AI Language features. Which TWO tasks can be accomplished using prebuilt named entity recognition (NER)? (Choose two.)

Accepted Answer

Extracting person names from text. Prebuilt NER in Azure AI Language is designed to identify and categorize entities in text into general types such as Person, Location, Organization, DateTime, and Quantity. Option B is correct because extracting person names from text is a core prebuilt NER capability, returning entities of the Person category. Option C is correct because identifying geographic locations maps directly to the Location (and related GPE) entity categories that prebuilt NER recognizes. Option A is not part of NER; language detection is a separate Azure AI Language feature that returns the detected language and confidence score. Option D is not NER; redacting credit card numbers is handled by PII detection/redaction, which identifies sensitive data and can mask it, rather than general named entity extraction. Option E is not NER; classifying entire documents into custom business categories is a custom text classification task that requires training a model, not the prebuilt NER model.

Answer

Detecting the written language of a document

Answer

Redacting credit card numbers for security

Answer

Classifying entire documents into custom business categories

Question 11

You are monitoring an Azure OpenAI model deployed in Azure AI Foundry. Which TWO Azure diagnostic metrics or logs should you analyze to track operational health and performance? (Choose TWO)

Accepted Answer

Request duration (latency) in milliseconds. Option A, request duration (latency) in milliseconds, is correct because Azure OpenAI in Azure AI Foundry emits latency metrics that directly reflect the responsiveness and operational health of the deployed model endpoint, making it a primary signal for performance monitoring. Option C, prompt tokens and completion tokens consumed per request, is correct because token consumption metrics are emitted by Azure OpenAI and are essential for tracking throughput, capacity, quota usage, and cost/performance behavior of the model deployment. Options B, D, and E are incorrect because ExpressRoute circuit bandwidth saturation, Azure SQL Database deadlock frequency, and VM disk IOPS consumption are metrics for networking, database, and compute infrastructure respectively, not for an Azure OpenAI model deployment's operational health and performance.

Answer

Azure ExpressRoute circuit bandwidth saturation

Answer

Azure SQL Database deadlock frequency

Answer

Virtual machine disk IOPS consumption

Question 12

You are creating an Azure AI Services resource using Azure CLI. Which command parameter specifies the pricing tier of the resource?

Accepted Answer

--sku. The Azure CLI command 'az cognitiveservices account create' uses the --sku parameter to specify the pricing tier (e.g., S0, F0) of the Azure AI Services resource. This is the standard parameter name across Azure CLI resource creation commands for specifying the SKU.

Answer

--tier

Answer

--pricing

Answer

--kind

Question 13

Your company is provisioning a new Azure AI Services multi-service resource. The security team mandates that all network traffic between the client applications and the Azure AI endpoint must remain entirely within the Microsoft backbone network without traversing the public internet. Which feature should you configure?

Accepted Answer

Azure Private Endpoint. Azure Private Endpoint creates a private IP address for the Azure AI Services resource inside your virtual network, so traffic between clients and the endpoint stays on the Microsoft backbone and never traverses the public internet. This is the standard feature for meeting private-connectivity mandates.

Answer

Cross-Origin Resource Sharing (CORS)

Answer

Service Principal Authentication

Answer

Azure AI Custom Subdomain

Question 14

You are managing cost allocation for multiple Azure AI services across different departments. Which TWO Azure tools or features can help you analyze and attribute these costs accurately? Each correct answer presents part of the solution.

Accepted Answer

Azure resource tags combined with Cost Management cost analysis grouping. Option D is correct because Azure resource tags let you attach department or cost-center metadata to each Azure AI service resource, and Cost Management cost analysis can then group and filter costs by those tag keys, directly attributing spend to the right department. Option E is correct because Azure Cost Management budgets track spend against thresholds and its exported CSV cost reports provide the raw, granular usage and charge data needed for accurate offline analysis and chargeback. Option A is wrong because Azure Bastion session logs record RDP/SSH connectivity activity, not billing or cost data. Option B is wrong because Entra ID (Azure AD) enterprise group memberships govern identity and access, not cost attribution. Option C is wrong because Microsoft Defender for Cloud security recommendations address security posture, not cost analysis or allocation.

Answer

Azure Bastion session logs

Answer

Azure Active Directory enterprise user group memberships

Answer

Microsoft Defender for Cloud security recommendations

Question 15

You are designing an edge solution where a Custom Vision exported model runs inside a Linux container on an IoT Edge device without internet connectivity. How must you configure billing for the container?

Accepted Answer

Provide the container with a billing endpoint URL and API key, and ensure periodic connectivity to Azure to report billing metrics.. Custom Vision exported containers (Docker for Linux or Windows) require two environment variables at runtime: a billing endpoint URL and an API key from a Custom Vision resource, plus periodic connectivity to Azure to report billing metrics. The container can run offline for inference, but it must periodically reach the billing endpoint to validate the key and report usage; otherwise it stops serving predictions.

Answer

Use Custom Vision Training API keys for container runtime billing.

Answer

Purchase an Azure Storage account to store offline billing logs locally.

Answer

Deploy the container with no environment variables; billing is handled automatically by IoT Hub.