AI-103 Practice Test 3 — 25 Questions

Question 1

You are building a custom text classification solution using Azure AI Language in Language Studio. You have a dataset where each document can belong to multiple categories simultaneously (e.g., a news article can be tagged as both 'Technology' and 'Finance'). What classification type should you configure?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Multi-label classification. Multi-label classification is designed for scenarios where a single document can be assigned more than one category at the same time. In Azure AI Language, multi-label projects let you apply multiple labels to each document during training and return all applicable labels at inference. This matches the requirement that a news article can be tagged as both 'Technology' and 'Finance'.

Answer

Custom named entity recognition

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Single-label classification

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Extractive summarization

Question 2

You are implementing opinion mining as part of sentiment analysis in Azure AI Language. An incoming review states, 'The room was spacious, but the staff was rude.' What does opinion mining return for this sentence?

Accepted Answer

Aspect targets ('room', 'staff') along with their corresponding evaluations ('spacious', 'rude') and individual sentiments.. Opinion mining in Azure AI Language returns aspect-based sentiment analysis, identifying aspect targets (e.g., 'room', 'staff') and their corresponding evaluations (e.g., 'spacious', 'rude') with individual sentiment polarities. For the given review, it would return 'room' as positive and 'staff' as negative, providing granular insights beyond document-level sentiment.

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A binary classification indicating whether the review is spam or genuine.

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A single neutral sentiment score for the entire document.

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A translated version of the review in English.

Question 3

You are testing a Custom Vision model via the REST API. You receive an HTTP 400 Bad Request error with the message 'InvalidProjectId'. What is the most likely cause?

Accepted Answer

The project ID GUID in the request URL is incorrect or does not exist in this Custom Vision account.. The Custom Vision REST API returns HTTP 400 with 'InvalidProjectId' when the project ID GUID supplied in the request URL path (e.g., /customvision/v3.0/training/projects/{projectId}/...) cannot be resolved to a project in the account tied to the submitted training or prediction key. This is a client-side request error, meaning the GUID is malformed, belongs to a different Custom Vision resource/account, or the project was deleted. Because the error is specifically named 'InvalidProjectId', the failure is at the identifier-validation stage, before any image payload is parsed.

Answer

The image file format is invalid.

Answer

The subscription has reached its monthly quota limit.

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The prediction key header was omitted.

Question 4

Which HTTP status code does the Azure AI Vision Read API return upon successfully initiating an asynchronous OCR analysis job?

Accepted Answer

202 Accepted. The Read API is asynchronous: when you POST an image or URL to the Read endpoint, it returns 202 Accepted with an Operation-Location header. You then poll that URL until the job completes and returns 200 OK with results.

Answer

204 No Content

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200 OK

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201 Created

Question 5

You notice your Azure AI agent is producing offensive content. You need to configure a guardrail that intercepts the model output before it is sent to the user. Which service should you integrate?

Accepted Answer

Azure AI Content Safety. Azure AI Content Safety is the dedicated Azure service that provides guardrails for harmful content, including hate, violence, sexual, and self-harm categories, and can be applied to both prompts and completions. Integrating it on the output path lets you intercept and block offensive model responses before they reach the user. This is the purpose-built service for content moderation in Azure AI workloads.

Answer

Azure AI Search Filters

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Prompt Template Validation

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Azure Monitor

Question 6

When configuring Azure OpenAI Custom Model Evaluation in Azure AI Foundry, which TWO metrics can be computed using LLM-as-a-judge evaluators? (Choose two.)

Accepted Answer

Coherence. Options D and E are correct because Azure AI Foundry's LLM-as-a-judge evaluators are designed to assess the quality of generative AI outputs using a language model as the grader, and Coherence (D) and Relevance (E) are two of the standard quality metrics they compute — Coherence measures how logically well-structured and fluent the response is, while Relevance measures how directly the response addresses the user's query or context. These metrics are part of the built-in evaluation metric set (alongside others such as Fluency, Groundedness, and Similarity) that can be run against a model deployment during custom model evaluation. Options A, B, and C are incorrect because CPU utilization percentage, DNS propagation delay, and disk IOPS are infrastructure and networking performance counters, not language-quality judgments, and they cannot be produced by an LLM-as-a-judge evaluator.

Answer

CPU utilization percentage

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DNS propagation delay

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Disk input/output operations per second (IOPS)

Question 7

You are developing a custom extraction model using Azure AI Document Intelligence Studio. Your training dataset consists of multi-page invoices with varying structures, and some fields appear only on the final page. How should you structure your tagging configuration?

Accepted Answer

Tag the fields on whatever page they appear across the sample documents in Document Intelligence Studio.. In Document Intelligence Studio, you tag fields on the pages where they actually appear across your sample documents — the labeling tool is page-aware and stores the page number alongside each field's bounding region. This lets the trained model learn that, for example, 'Total' appears on page 1 in some invoices and page 3 in others. You do not need to split documents or build orchestration models just because fields appear on different pages.

Answer

Create a separate custom model for the final page and chain them using an orchestration model.

Answer

Split every multi-page invoice into individual single-page documents before uploading.

Answer

Define all late-appearing fields as optional metadata parameters in the training configuration JSON file.

Question 8

Which Azure AI Foundry feature helps identify drift in model performance after deployment?

Accepted Answer

Model Monitoring. Model Monitoring in Azure AI Foundry continuously evaluates deployed models against production data, tracking data drift, prediction drift, and performance metrics like accuracy and latency. It is the built-in feature designed specifically to detect post-deployment degradation. The other options either describe manual or unrelated capabilities that do not provide ongoing drift detection for deployed models.

Answer

Data Drift Analysis via Compute

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Prompt Flow Tracing

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Model Catalog

Question 9

You are designing a customer service chatbot using Azure OpenAI. You need to ensure that the model refuses to answer questions outside the scope of your company's products. Where should you define this boundary instruction?

Accepted Answer

In the system message. The system message is correct because it is the designated channel for persistent, high-priority instructions that define the model's persona, scope, and refusal behavior across all turns of a conversation. Placing the boundary instruction there ensures it applies before and throughout the interaction, and it is the standard mechanism for scoping a chatbot to a domain. This is the architecturally correct location for guardrails that must not be overridden by user input.

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In the temperature setting

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In the user prompt prefix

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In the max_tokens parameter configuration

Question 10

Your organization is running an Azure AI Search service and needs to restrict management operations (such as index creation and deletion) to administrators while allowing standard client applications to perform search queries only. How should you configure access?

Accepted Answer

Use Admin API keys for management operations and Query API keys for client search queries.. Azure AI Search issues two key types: admin keys (primary and secondary) that grant full control including index creation, deletion, and configuration, and query keys that only permit read operations against indexes. Assigning admin keys to administrators and query keys to client applications enforces least privilege at the data-plane level without needing Azure RBAC.

Answer

Enable IP firewall rules to block index creation.

Answer

Configure CORS settings in the Azure portal.

Answer

Assign the Contributor role to client applications.

Question 11

Your team is implementing 'Human-in-the-loop' (HITL) for an agent that performs account updates. Which mechanism should you use to pause the agent and wait for approval?

Accepted Answer

Use a Prompt Flow 'Pause' node or custom state management logic.. Human-in-the-loop requires the agent to suspend execution, persist its state, and resume only after an external approval signal. In Azure Prompt Flow this is implemented with a Pause node (or equivalent custom state management) that halts the flow and waits for an external event or API call to continue. This preserves the agent's context and prevents the account update from executing until a human explicitly approves it.

Answer

Set a long timeout in the API call.

Answer

Increase the model's 'stop_sequences'.

Answer

Use an infinite loop in Python.

Question 12

Your development team is building a global customer support portal. Incoming tickets arrive in dozens of different languages. You need to automatically route tickets to the correct regional team based on the language of the ticket text. Which Azure AI Language feature should you call?

Accepted Answer

Language detection. Language detection is the Azure AI Language feature specifically designed to identify the language of a given text and return the detected language name and ISO 639-1 code. For routing tickets based on language, this is the exact capability needed. Sentiment analysis, NER, and key phrase extraction serve different purposes and do not identify language.

Answer

Sentiment analysis

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Named Entity Recognition (NER)

Answer

Key phrase extraction

Question 13

You are designing an architecture that processes incoming multilingual support emails using Azure AI Language. Which THREE processing steps should you include in your pipeline? (Choose three.)

Accepted Answer

Execute named entity recognition to extract relevant organizations, locations, and names.. Option C is correct because Azure AI Language's language detection endpoint must be called first to identify each email's primary language, which is essential for routing multilingual content to the appropriate language-specific models. Option A is correct because named entity recognition (NER) extracts entities such as organizations, locations, and person names from the email text, providing structured, actionable information for support workflows. Option E is correct because sentiment analysis and PII detection are supported Azure AI Language features that help gauge customer tone and identify/redact sensitive data in the email documents. Option B is not appropriate because speech-to-text acoustic models are unrelated to processing text emails and training a model per email is impractical. Option D is not appropriate because converting text to raw binary executables serves no analytical purpose and would destroy the text needed for language analysis.

Answer

Train a new speech-to-text acoustic model for every email received.

Answer

Convert all email text into raw binary executable files before analysis.

Question 14

You are automating document ingestion for a RAG pipeline. Which feature allows you to automatically split large documents into smaller chunks during indexing?

Accepted Answer

Indexers with Skillsets. In Azure AI Search, indexers with skillsets are the feature that automates document ingestion and chunking: the indexer pulls source documents, and a Text Split skill in the skillset breaks large documents into smaller chunks before they are embedded and written to the index. This is the standard mechanism for building a RAG pipeline where chunk size affects retrieval quality. The skillset can also enrich content (OCR, entity extraction, embeddings) in the same pipeline.

Answer

Prompt Flow Vector Store Lookup

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Model Fine-tuning

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Vector Embeddings API

Question 15

Which Azure AI Foundry feature allows you to visualize the execution path of an agentic workflow?

Accepted Answer

Prompt Flow visual designer.. Prompt Flow's visual designer (DAG view) in Azure AI Foundry renders each node (LLM call, Python tool, retrieval step) and the directed edges between them, letting you trace the exact execution path of an agentic workflow. It also surfaces per-node inputs/outputs and token usage, which is why it is the designated tool for visualizing agent orchestration. The other options are single-purpose surfaces that do not expose flow topology.

Answer

Azure OpenAI Playground.

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Azure AI Search dashboard.

Answer

Model catalog view.

Question 16

You need to programmatically delete an iteration of a Custom Vision model that performed poorly. Which API method should you invoke?

Accepted Answer

DELETE /customvision/v3.3/Training/{projectId}/iterations/{iterationId}. The Custom Vision Training API exposes iteration deletion via the HTTP DELETE method on the path /customvision/v3.3/Training/{projectId}/iterations/{iterationId}. Invoking DELETE on that resource removes the specified iteration from the project. This is the standard RESTful pattern for deleting a subordinate resource identified by its parent project and iteration IDs.

Answer

PUT /customvision/v3.3/Training/{projectId}/iterations/{iterationId}

Answer

GET /customvision/v3.3/Training/{projectId}/iterations/{iterationId}/purge

Answer

POST /customvision/v3.3/Training/{projectId}/iterations/{iterationId}/delete

Question 17

You need to implement token-based authentication using Azure AD (Microsoft Entra ID) for a Python application consuming an Azure OpenAI resource. Which authorization header format must your application use to authenticate successfully?

Accepted Answer

Authorization: Bearer . Azure OpenAI and most Microsoft Entra ID-protected REST APIs expect OAuth 2.0 bearer tokens in the `Authorization: Bearer ` header. When using Microsoft Entra ID (Azure AD) token-based authentication, the application acquires an access token via MSAL or the Azure Identity SDK and passes it in this standard header format. This is the RFC 6750-compliant way to present a bearer token.

Answer

Authorization: API-Key <api_key_value>

Answer

X-MS-Token: <access_token>

Answer

Ocp-Apim-Subscription-Key: <access_token>

Question 18

You are configuring content safety filters in Azure AI Foundry for a public-facing generative AI application. You need to ensure that the application detects and blocks potential jailbreak attempts and protected material text. Which component should you configure?

Accepted Answer

Azure AI Content Safety text moderation service with jailbreak and protected material detection enabled.. Azure AI Content Safety provides dedicated detectors for jailbreak attempts (prompt injection/manipulation) and protected material text (copyrighted content), which can be configured in Azure AI Foundry to block or flag such inputs and outputs. This is the correct component for enforcing content safety policy on a public-facing generative AI app.

Answer

Azure Key Vault access policies restricting model endpoint tokens.

Answer

Azure API Management rate-limiting policies configured to drop requests containing long prompts.

Answer

Azure Monitor activity log alerts for anomalous token usage.

Question 19

You are deploying a Custom Vision object detection model as a Docker container on an Azure Kubernetes Service (AKS) cluster. During load testing, you notice high memory consumption and container restarts. Which configuration setting in the deployment manifest should you review to ensure stability?

Accepted Answer

Kubernetes resource requests and limits for memory and CPU.. High memory consumption and container restarts in Kubernetes are classic symptoms of missing or misconfigured resource requests and limits. Setting appropriate memory and CPU requests/limits in the deployment manifest ensures the scheduler places the pod correctly and prevents OOMKills.

Answer

Billing endpoint URL syntax.

Answer

Azure Blob Storage connection string persistence.

Answer

Custom Vision iteration publishing name environment variable.

Question 20

You are implementing a multi-turn conversational agent in Azure AI Foundry. As the conversation grows longer, API latency and costs increase significantly. Which strategy should you implement to manage the conversation history effectively?

Accepted Answer

Implement a sliding window history buffer combined with periodic summarization of older dialogue turns.. For long multi-turn conversations, the standard pattern is a sliding window that keeps only the most recent N turns plus a running summary of older dialogue. This bounds the token count sent to the model, controlling latency and cost, while preserving essential context through summarization. It is the recommended approach in Azure AI Foundry for managing conversation history.

Answer

Increase max_tokens to accommodate infinite history growth.

Answer

Disable the chat history array entirely and treat every turn as a standalone prompt.

Answer

Truncate the entire conversation history after every single user turn.