AI-103 Practice Test 6 — 10 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'.

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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.

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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.

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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.

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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.

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

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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.

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Configure CORS settings in the Azure portal.

Answer

Assign the Contributor role to client applications.