AI-103 Practice Test 12 — 15 Questions

Question 1

You are using Document Intelligence to classify documents into different types (e.g., invoices, bank statements). Which model is most appropriate?

Accepted Answer

Custom classification model. A custom classification model is purpose-built to categorize documents into user-defined types (e.g., invoice vs. bank statement) before or alongside extraction. It is trained on labeled examples of each document class and returns a document type with confidence. Prebuilt-invoice extracts fields from invoices but does not classify arbitrary document types.

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Prebuilt-invoice

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Read model

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Custom extraction model

Question 2

You are configuring an Azure AI Document Intelligence orchestration model to automatically classify and extract data from incoming customer correspondence, which includes invoices, work orders, and purchase orders. One of the component models is failing to classify documents correctly. How should you resolve this issue?

Accepted Answer

Retrain the failing component custom model with more distinct sample documents that represent the specific document class.. The most effective way to resolve misclassification by a component model in an orchestration model is to retrain that custom model with more distinct sample documents that clearly represent the specific document class. This improves the model's ability to differentiate between classes.

Answer

Modify the orchestration model routing threshold in the Azure portal settings.

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Switch the orchestration model from a template-based classifier to a neural classifier using the REST API.

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Update the API version to bypass classification and force direct extraction via the general document model.

Question 3

Your team needs to extract data from a large volume of standardized tax forms using Azure AI Document Intelligence. The forms have a fixed layout where fields are always in the exact same location. Which model type provides the fastest training and highest accuracy for this specific use case?

Accepted Answer

Custom template model. A custom template model is designed for documents with a consistent, fixed layout, and it trains quickly because it relies on layout/template matching rather than deep semantic learning. For standardized tax forms where fields are always in the same location, the template model achieves high accuracy with minimal training data and fast training time. This is the classic use case Microsoft documents for the template model type.

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Prebuilt receipt model

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Custom neural model

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General document model

Question 4

You are implementing indirect prompt injection protection for an agent that reads emails and summarizes them. An incoming email contains malicious instructions designed to trick the agent into exfiltrating user data. Which Azure AI feature should you configure to detect this threat?

Accepted Answer

Prompt Shields for indirect prompt injection in Azure AI Content Safety. Prompt Shields in Azure AI Content Safety is specifically designed to detect both direct (user) and indirect (document/email-embedded) prompt injection attacks. For an agent that reads emails, indirect prompt injection is the threat model — malicious instructions hidden in email content — and Prompt Shields analyzes the content to flag it before the agent acts on it. This is the only option that addresses prompt injection at the AI layer.

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Azure DDoS Protection standard tier

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Azure Web Application Firewall (WAF) SQL injection rules

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Microsoft Defender for Cloud threat detection

Question 5

You need to secure the communication between your app and Document Intelligence using a private endpoint. What is the primary benefit?

Accepted Answer

Traffic stays within the Azure private network. A private endpoint for Document Intelligence creates a private IP address in your virtual network via Azure Private Link, so all traffic between your app and the Document Intelligence resource traverses the Microsoft backbone and never the public internet. The primary benefit is network isolation — traffic stays within the Azure private network, reducing exposure to internet-based threats and satisfying compliance requirements for data-in-transit isolation.

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Reduced cost

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Faster inference speeds

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Increased throughput limits

Question 6

You are building an autonomous agentic workflow using Microsoft Foundry and Semantic Kernel. You need to ensure the agent operates safely and adheres to human oversight requirements. Which THREE design patterns should you apply? (Choose three.)

Accepted Answer

Implement human-in-the-loop approval gates before the agent executes high-impact tool actions such as database updates or financial transactions.. Option B is correct because human-in-the-loop approval gates are the canonical oversight control for high-impact, irreversible actions such as database updates or financial transactions, ensuring a human authorizes the action before the agent's tool call is dispatched. Option C is correct because idempotency keys make external tool calls safely retryable: if the agent or orchestrator retries after a timeout or transient failure, the downstream service deduplicates the request so a payment or write is not executed twice. Option D is correct because circuit breakers are a resilience pattern that trips when error rates or token consumption exceed thresholds, halting or degrading agent execution to prevent runaway loops, cost blowups, and cascading failures. Option A is not appropriate because granting root-level administrative access violates least privilege and dramatically expands the blast radius of any agent error or prompt-injection attack. Option E is not appropriate because unlimited recursive tool execution without loop detection invites infinite loops, resource exhaustion, and uncontrolled cost, directly contradicting safe agent design.

Answer

Grant the agent root-level administrative access to all cloud infrastructure services.

Answer

Allow the agent unlimited recursive tool execution without loop detection.

Question 7

Your security team discovers that an application using Azure AI Services is intermittently failing with authentication errors due to expired client secrets. You need to refactor the application to use Microsoft Entra ID workﬂows with managed identities. Which authentication library should your application code use to acquire tokens securely without embedding secrets?

Accepted Answer

Microsoft Authentication Library (MSAL). Microsoft Authentication Library (MSAL) is the recommended library for acquiring tokens from Microsoft Entra ID, including tokens for managed identities and service principals. MSAL handles token caching, refresh, and secure acquisition without embedding secrets in code. Using MSAL with managed identities lets the application authenticate without storing client secrets, directly addressing the expired-secret failures.

Answer

Azure Storage Data Movement Library

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System.Net.Http WebRequestHandler

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Azure SDK for .NET Management Client

Question 8

You are configuring model fine-tuning for an Azure OpenAI model in Azure AI Foundry. You have prepared a JSONL training dataset. Which evaluation approach is recommended to verify that fine-tuning successfully improved model performance without causing catastrophic forgetting?

Accepted Answer

Run an evaluation dataset comparing baseline and fine-tuned models using Azure AI Foundry evaluation metrics. To verify that fine-tuning improved model performance without catastrophic forgetting, you should run an evaluation dataset comparing the baseline (pre-fine-tuning) and fine-tuned models using Azure AI Foundry evaluation metrics. This approach measures improvements on the target task and checks for degradation on general capabilities, providing quantitative evidence of fine-tuning success and detecting catastrophic forgetting.

Answer

Inspect Azure Key Vault access audit logs

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Review Azure Cost Management invoices for training hours

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Check Azure Monitor CPU utilization percentages of the hosting cluster

Question 9

You are building an agentic workflow in Azure AI Foundry. You need the agent to perform a multi-step task: search a database, summarize results, and then send an email. Which component is best suited to manage this orchestration?

Accepted Answer

Azure AI Agent Service.. Azure AI Agent Service is purpose-built to orchestrate multi-step agentic workflows, including tool/function calling, planning, and chaining actions like database queries, summarization, and email dispatch. It manages conversation state, tool invocation, and step sequencing natively, which is exactly what this scenario requires.

Answer

Azure OpenAI fine-tuning service.

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Azure AI Search indexer.

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Azure AI Content Safety API.

Question 10

When implementing prompt engineering for generative AI applications in Azure AI Foundry, which TWO practices are considered best practices for maintaining prompt maintainability and security? (Choose TWO)

Accepted Answer

Storing and versioning prompt templates as assets in Azure AI Foundry. Option C is correct because Azure AI Foundry supports storing and versioning prompt templates as managed assets, which enables change tracking, rollback, reuse across deployments, and consistent governance instead of scattering prompts through application code. Option E is correct because separating system instructions from untrusted user input with clear delimiters or XML tags helps prevent prompt injection and instruction-override attacks, and it makes the prompt structure easier to read and maintain. Options A, B, and D are not best practices: unformatted single-paragraph prompts are hard to review and maintain, embedding API keys in prompt strings exposes secrets that can leak through logs or model output, and hardcoding user names and passwords into system prompts creates a serious credential disclosure risk and violates the principle of keeping secrets out of prompts.

Answer

Writing all prompts as single unending paragraphs without formatting

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Embedding API keys directly inside prompt strings

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Hardcoding user names and passwords into system prompts

Question 11

You are implementing Retrieval-Augmented Generation (RAG) in Azure AI Foundry using Azure AI Search as the grounding data source. Which indexing feature should you enable to ensure the system can perform semantic ranking alongside traditional keyword search?

Accepted Answer

Semantic Search. Semantic Search in Azure AI Search enables the semantic ranker, which re-ranks an initial BM25 keyword result set using deep-learning models to better capture query intent. It is the feature that layers semantic ranking on top of traditional keyword (and optionally vector) search, improving relevance for natural-language queries.

Answer

Fuzzy Search

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Lucene Standard Analyzer

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Vector Normalization

Question 12

You are performing Retrieval-Augmented Generation. You need to ensure the retrieved data includes metadata for filtering. Where should this metadata be defined?

Accepted Answer

Azure AI Search Index Fields. In a RAG architecture, metadata used for filtering (such as category, source, date, or access level) must be defined as fields in the Azure AI Search index schema so that queries can apply OData filter expressions against them. The index is the authoritative store of both vector/content data and its associated filterable attributes.

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Environment variables

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Prompt Flow input schema

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System message content

Question 13

You are configuring a webhook to receive notifications when an asynchronous batch text summarization job completes in Azure AI Language. Which Azure service should you use to capture and route the webhook events to your backend function?

Accepted Answer

Azure Event Grid. Azure Event Grid is the event-routing service designed to deliver event notifications from Azure services to subscribers such as webhooks or Azure Functions. Azure AI Language batch jobs publish completion events that Event Grid captures and routes to the configured endpoint. This makes Event Grid the correct service for receiving and forwarding the webhook when the summarization job finishes.

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Azure API Management

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Azure Stream Analytics

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Azure Logic Apps

Question 14

You are training a Custom Vision multiclass classification model for retail products. After training, you notice that the precision is high, but the recall is low for a specific product category. What does low recall indicate?

Accepted Answer

The model is missing many actual instances of that product category (high false negatives).. Recall measures the proportion of actual positive instances that the model correctly identifies. Low recall means the model is missing many actual instances of the product category, resulting in a high number of false negatives. High precision with low recall indicates the model is conservative: when it predicts the category it is usually right, but it fails to catch many true cases.

Answer

The training dataset had zero images for that category.

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The model is incorrectly predicting that product when it is not present (high false positives).

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The probability threshold is set too high for all classes uniformly.

Question 15

Your development team needs to deploy an Azure OpenAI model deployment in an enterprise environment. They require provisioned throughput units (PTUs) to guarantee predictable latency and throughput for a mission-critical chatbot. Where can you purchase and manage PTU allocations?

Accepted Answer

Azure Portal or Azure OpenAI Studio during model deployment creation by selecting the Provisioned tier.. Provisioned Throughput Units (PTUs) for Azure OpenAI are purchased and managed through the Azure Portal or Azure OpenAI Studio when creating or updating a model deployment by selecting the Provisioned deployment type. This is where you specify the PTU quantity, associate it with the Azure OpenAI resource, and manage capacity reservations for predictable latency and throughput.

Answer

Azure DevOps pipeline YAML configuration files.

Answer

Microsoft 365 Admin Center under billing subscriptions.

Answer

Visual Studio Enterprise subscription benefit portal.