AI-103 Practice Test 27 — 15 Questions

Question 1

You are deploying a custom AI model using a container image stored in Azure Container Registry (ACR). How do you ensure the deployment can pull the image securely?

Accepted Answer

Assign the 'AcrPull' role to the endpoint's managed identity.. The secure, secretless way to let a deployment pull from ACR is to give the consuming resource a managed identity and assign that identity the AcrPull role scoped to the registry. The platform then authenticates to ACR using the identity's token, so no credentials are stored in configuration. This follows the Azure best practice of eliminating long-lived secrets from deployment pipelines.

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Use the ACR 'admin' password in the configuration.

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Deploy the model from a local machine.

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Make the ACR public.

Question 2

Your enterprise application uses Custom Vision. Due to compliance policies, you must ensure that your training data and model endpoints reside within a specific Azure region with strict data residency guarantees. Which resource configuration should you use?

Accepted Answer

Create a Custom Vision training and prediction resource in the targeted Azure region.. Custom Vision data residency is controlled by the region of the training and prediction resources you create. To guarantee that training data and model endpoints stay in a specific Azure region, you must create both a Custom Vision training resource and a prediction resource in that target region. The service then stores and processes data only within that region.

Answer

Use the Global resource endpoint for Custom Vision.

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Use Azure AI Vision multi-service account with global routing.

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Store images in Azure Blob Storage with geo-replication.

Question 3

You are configuring rate limiting and request throttling for multiple client applications consuming an Azure OpenAI endpoint. Different clients have different tier agreements (Bronze, Silver, Gold). Where should you implement these tiered rate-limiting policies?

Accepted Answer

Azure API Management (APIM). Azure API Management (APIM) is the correct place to implement tiered rate-limiting and throttling policies because it sits in front of the Azure OpenAI endpoint as a gateway and supports per-subscription, per-product, and per-key rate-limit policies. You can create Bronze, Silver, and Gold products/subscriptions in APIM and apply rate-limit or rate-limit-by-key policies with different quotas (calls per minute, tokens per minute) to each tier. Azure OpenAI itself does not expose customer-configurable tiered throttling policies per client.

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Azure Application Gateway custom WAF rules

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Azure OpenAI service built-in tier settings

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Network Security Group (NSG) outbound rules

Question 4

You are troubleshooting a performance issue where your application's calls to Azure AI Vision experience high latency. Your application is hosted in Azure West US, but your Azure AI Vision resource was accidentally created in Azure Southeast Asia. What is the recommended remediation?

Accepted Answer

Recreate the Azure AI Vision resource in the West US region close to your application hosting.. Azure AI Vision is a regional service, and network latency between the application and the service endpoint is dominated by geographic distance. Recreating the resource in West US colocates the service with the application, minimizing round-trip time and eliminating cross-region egress. This is the standard remediation for latency caused by region mismatch.

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Increase the timeout threshold in the HTTP client configuration.

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Enable Azure ExpressRoute between West US and Southeast Asia.

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Switch from REST API to gRPC protocol.

Question 5

You are designing an agentic workflow in Azure AI Agent Service where multiple specialized agents need to collaborate to solve complex, multi-step financial analysis tasks. One agent manages the workflow state and decides which sub-agent to invoke next. What architectural pattern is best suited for this implementation?

Accepted Answer

Orchestrator-workers pattern, where a central agent dynamically assigns sub-tasks to specialized worker agents based on intermediate results.. The orchestrator-workers pattern uses a central orchestrator agent that maintains workflow state, evaluates intermediate results, and dynamically selects which specialized worker agent to invoke next. This matches the requirement that one agent 'manages the workflow state and decides which sub-agent to invoke next' for multi-step financial analysis. It supports conditional branching, iterative refinement, and handoffs, which are essential when the sequence of sub-tasks cannot be known in advance.

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Single monolithic prompt pattern, where all instructions are combined into one massive system prompt without multi-agent handoffs.

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Map-reduce batch processing pattern, where all sub-agents execute independently in parallel without communication.

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Static sequential pipeline, where agents execute strictly in a predefined linear order without conditional branching.

Question 6

You are integrating Document Intelligence with Azure Blob Storage. You need to provide the model with a set of documents for training. How should you prepare the data?

Accepted Answer

Upload files and corresponding .labels.json and .ocr.json files. To train a Document Intelligence model with documents in Azure Blob Storage, you must upload the documents along with their corresponding .labels.json and .ocr.json files. The .labels.json file contains the labeled data (field names and values), and the .ocr.json file contains the OCR results for the document. These files are generated using the Document Intelligence Studio or SDK and are required for training a custom model.

Answer

Convert all files to CSV

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Store files in an Azure SQL database

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Upload raw files only

Question 7

Your development team requires secure, passwordless authentication from an Azure Function to an Azure AI Vision resource. Which authentication mechanism should you implement to adhere to security best practices?

Accepted Answer

Microsoft Entra managed identity. Microsoft Entra managed identity lets the Azure Function authenticate to the Azure AI Vision resource without storing any secret — the platform issues and rotates the token automatically, and you grant the function's identity the appropriate RBAC role (e.g., Cognitive Services User) on the target resource. This is the passwordless, least-privilege pattern Microsoft recommends for service-to-service auth. Keys and SAS tokens are shared secrets that can leak; managed identity eliminates that class of risk entirely.

Answer

Shared Access Signature (SAS) tokens with read-only permissions

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Basic authentication using service administrator username and password

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Primary API Key passed in the Ocp-Apim-Subscription-Key header

Question 8

You are troubleshooting a Custom Vision object detection model that has low precision and high false-positive rates for a specific object class. During data analysis, you notice that negative images (images without the object) lack background variety. What is the recommended remediation step?

Accepted Answer

Add diverse negative images containing background environments similar to where false positives occur.. Low precision with high false positives on a specific class almost always means the model has not learned to distinguish the target object from background contexts it mistakenly associates with that class. Adding diverse negative images that mirror the environments where false positives occur teaches the model that those backgrounds alone do not constitute the object, directly reducing false positives and improving precision.

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Increase the probability threshold in the prediction API call.

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Remove all tagged bounding boxes and retrain as a multi-class classification model.

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Apply aggressive data augmentation to duplicate existing positive images.

Question 9

You need to ensure that your agent's response is formatted as valid JSON. How should you approach this?

Accepted Answer

Define the JSON schema in the system prompt instructions.. Defining the JSON schema in the system prompt instructions guides the model to generate output that conforms to the specified structure. This approach leverages the model's instruction-following capabilities to produce valid JSON without external parsing. It is the most direct and reliable method for ensuring format compliance.

Answer

Set the temperature to 0.5.

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Use an external library to convert text to JSON.

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Ask the user to request JSON.

Question 10

What is the primary function of the 'layout' model in Azure AI Document Intelligence?

Accepted Answer

To extract text, tables, and document structure. The layout model in Azure AI Document Intelligence extracts text, tables, selection marks, and document structure (such as paragraphs, headings, and reading order) without requiring a trained custom model. It is the general-purpose model for understanding a document's structural layout, making it the correct answer.

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To translate text into different languages

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To classify document types

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To extract key-value pairs from receipts

Question 11

You are provisioning an Azure AI services account and need to select the kind of resource. Which kind parameter value should you select for an Azure OpenAI deployment?

Accepted Answer

OpenAI. For an Azure OpenAI deployment, the resource kind must be 'OpenAI'. This kind provisions an Azure OpenAI resource that provides access to OpenAI models like GPT-4, GPT-3.5, DALL-E, and Whisper through Azure's infrastructure. The 'OpenAI' kind is specifically designed for Azure OpenAI Service, enabling features such as model deployment, fine-tuning, and content filtering. Other kinds correspond to different Azure AI services and do not support OpenAI model deployments.

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CognitiveSearch

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MachineLearning

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BotService

Question 12

You are designing an image analysis solution using Azure AI Vision 4.0. Which TWO visual features can be requested in a single Image Analysis API analyze request? (Choose two.)

Accepted Answer

tags. The Image Analysis 4.0 analyze request accepts a 'visualFeatures' parameter that can include 'tags' to return content tags describing the image. Option C (read) is also correct because 'read' is a valid visual feature in the same 'visualFeatures' list, enabling OCR text extraction within the same analyze call. Both features belong to the Azure AI Vision Image Analysis 4.0 API surface and can be combined in one request, e.g., visualFeatures=tags,read. Option B (Custom Vision Classification) is not part of the Image Analysis API; it is a separate Custom Vision service with its own prediction endpoint. Option D (Azure AI Face Analyze) is a distinct Face service API, not a visual feature of Image Analysis. Option E (Azure AI Document Intelligence Layout) is a separate Document Intelligence model/API and cannot be requested through the Image Analysis analyze operation.

Answer

Custom Vision Classification

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Azure AI Face Analyze

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Azure AI Document Intelligence Layout

Question 13

You are reviewing the pricing details for Azure AI Language. How is billing calculated for pre-built text analysis features such as sentiment analysis and key phrase extraction?

Accepted Answer

Number of text records (documents) processed. Azure AI Language pre-built features such as sentiment analysis and key phrase extraction are consumption-based services billed per text record (document) processed. Each API call that submits a document for analysis counts as one billable transaction, regardless of document length within the per-document size limit. This pay-per-use model aligns cost directly with the volume of text analyzed rather than with infrastructure uptime or training activity.

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Hourly uptime of the resource endpoint

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Number of unique client application connections

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Total gigabytes of training data uploaded

Question 14

You are using Custom Vision to train an object detection model. You want to evaluate model performance before publishing an iteration. Which metric represents the overall accuracy of the model across all classes at various confidence thresholds?

Accepted Answer

Mean Average Precision (mAP). Mean Average Precision (mAP) is the standard metric for object detection models because it aggregates precision across all classes and Intersection over Union (IoU) thresholds. It computes the average precision (AP) for each class—the area under the precision-recall curve—and then averages those AP values. This gives a single score that reflects how well the model localizes and classifies objects across different confidence thresholds, which is exactly what Custom Vision reports for object detection iterations.

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Perplexity

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Word Error Rate (WER)

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BLEU Score

Question 15

When configuring role-based access control (RBAC) in Azure AI Foundry and Azure OpenAI, which TWO built-in Azure roles are commonly assigned to developers and services? (Choose TWO)

Accepted Answer

Cognitive Services OpenAI User. Option B, Cognitive Services OpenAI User, is correct because it is the built-in role that grants data-plane access to Azure OpenAI resources, allowing developers and services to call inference endpoints such as chat completions and embeddings without granting management permissions. Option E, Azure AI Developer, is correct because it is the built-in role designed for building and managing Azure AI Foundry projects and hubs, giving developers the permissions needed to create projects, connections, and deploy models while scoping access appropriately. Option A, Storage Blob Data Owner, is not appropriate here since it governs blob storage data access, not Azure OpenAI or AI Foundry RBAC. Option C, Virtual Machine Contributor, manages VMs and is unrelated to AI service access. Option D, Network Contributor, manages networking resources and does not grant the AI/OpenAI data-plane permissions required for this scenario.

Answer

Storage Blob Data Owner

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Virtual Machine Contributor

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Network Contributor