AI-102 Practice Test 40 — 15 Questions

Question 1

You are implementing a knowledge mining solution using Azure AI Search with a custom skillset. The custom skill is an Azure Function that enriches documents with additional metadata. You need to ensure that the custom skill receives the entire document content as input. How should you configure the skill's context and inputs?

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

Accepted Answer

Set context to '/document' and input source to '/document/content'.. To pass the entire document content to a custom skill, the skill's context must be set to '/document' (the root of each document in the enrichment tree) and the input source must be '/document/content'. Setting context to '/document' ensures the skill executes once per document, and mapping the input to '/document/content' delivers the full text content to the Azure Function. This is the correct configuration for document-level enrichment.

Answer

Set context to '/document/content' and input source to '/document/metadata'.

Answer

Set context to '/document/content' and input source to '/document/content'.

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Set context to '/document' and input source to '/document/normalized_images/*'.

Question 2

Which TWO actions should you take to reduce the cost of using Azure OpenAI for a chatbot that handles high traffic?

Accepted Answer

Use the batch API for non-real-time requests.. Option B is correct because the Azure OpenAI Batch API processes asynchronous, non-real-time workloads at a 50% discount compared to standard global pricing, so routing any chatbot requests that don't need immediate responses through batch jobs directly lowers per-token cost. Option C is correct because caching responses to frequently asked questions (for example with Azure Cache for Redis or a semantic cache) means repeated identical or similar prompts are served without calling the model at all, eliminating token charges for that traffic and reducing overall request volume. Option A is not correct because increasing max_tokens raises the maximum output length and therefore can increase, not decrease, token consumption and cost. Option D is not correct because temperature controls randomness in sampling, not the number of tokens generated, so setting it to 0 does not reduce token usage or cost. Option E is not correct because fine-tuning does not shorten the prompt by itself and adds training and hosting costs; prompt length is reduced through techniques like prompt compression or shorter system messages, not fine-tuning alone.

Answer

Increase max_tokens to reduce the number of requests.

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Lower the temperature to 0 to reduce token usage.

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Fine-tune the model to reduce prompt length.

Question 3

A news organization uses Azure Video Indexer to generate transcripts of live broadcasts. They notice that the speaker names are not appearing in the transcript. What is the most likely cause?

Accepted Answer

The speaker identification model has not been trained with voice samples.. Speaker names are missing because Azure Video Indexer's speaker identification feature requires pre-trained voice samples to match speakers to their identities. Without a custom voice model trained on known speakers' audio, the service can only label speakers as 'Speaker #1', 'Speaker #2', etc., but cannot assign actual names. This is a supervised learning process where the model must be trained with labeled voice samples before it can recognize and name speakers.

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The video resolution is too low for OCR.

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The video format is not supported.

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The language is not set correctly.

Question 4

A company is building an agent that uses Azure OpenAI to answer questions from a large document library. The agent must use a Retrieval Augmented Generation (RAG) pattern. Which TWO actions should the team take to implement RAG effectively?

Accepted Answer

Index the documents into a vector database like Azure Cognitive Search.. Option C is correct because RAG requires an external knowledge store that supports semantic similarity search, and indexing the documents into a vector database such as Azure Cognitive Search (or Azure AI Search) creates embeddings that let the agent retrieve the most relevant passages at query time. Option E is correct because the defining step of Retrieval Augmented Generation is retrieving the top-k relevant document chunks and injecting them into the prompt as grounding context before the model generates its answer, which keeps responses accurate and current without retraining. Option A is not appropriate because no model can memorize an entire large document library, and relying on memorization defeats the purpose of retrieval. Option B is not appropriate because fine-tuning teaches style and task behavior rather than reliably storing and retrieving factual document content, and it is not the RAG mechanism. Option D is not appropriate because training a custom language model from scratch is prohibitively expensive and unnecessary when Azure OpenAI plus retrieval already solves the problem.

Answer

Ensure the model is large enough to memorize the entire document library.

Answer

Fine-tune the Azure OpenAI model on the document library.

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Train a custom language model from scratch.

Question 5

Which THREE factors should you consider when selecting a pricing tier for Azure Computer Vision in a production environment?

Accepted Answer

Data residency requirements. Data residency requirements (Option C) are critical when selecting a pricing tier for Azure Computer Vision because the service processes images in specific regional data centers, and some tiers (e.g., Standard S0) support multi-region processing while others may be restricted. Compliance with regulations like GDPR or HIPAA may require that image data never leaves a particular geography, directly influencing which tier and region you can choose.

Answer

Availability of free tier

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Type of storage account for images

Question 6

You need to translate a large batch of documents from English to multiple languages. Which Azure service should you use?

Accepted Answer

Azure AI Translator. Azure AI Translator is the correct service because it is specifically designed for batch document translation across multiple languages, supporting both text and document translation with source language auto-detection. It provides a dedicated Document Translation feature via the Translator API, which can handle large volumes of files asynchronously while preserving document structure and formatting.

Answer

Conversational Language Understanding

Answer

Azure AI Language Detection

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Azure AI Speech Translation

Question 7

You are designing a generative AI solution that uses Azure OpenAI GPT-4 to answer customer support questions. The solution must comply with Microsoft's Responsible AI principles, particularly transparency and accountability. Which implementation approach best meets these requirements?

Accepted Answer

Enable content filtering, log all interactions, and include a disclaimer that responses are AI-generated.. It directly addresses Microsoft's Responsible AI principles of transparency and accountability. Enabling content filtering (via Azure AI Content Safety) ensures harmful outputs are blocked, logging all interactions provides an audit trail for accountability, and including a disclaimer that responses are AI-generated satisfies transparency by clearly informing users they are interacting with an AI system.

Answer

Use the model without any modifications, and have a human review all responses.

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Fine-tune the model on a curated dataset of support tickets and disable content filtering.

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Use the default model deployment and rely on the model's inherent safety.

Question 8

A company uses Azure Form Recognizer to extract data from invoices. The extracted data contains many errors for a specific vendor's invoices. What should they do?

Accepted Answer

Custom train a model with labeled examples of that vendor's invoices.. Azure Form Recognizer's prebuilt invoice model may not generalize well to vendor-specific layouts or data formats. By custom training a model with labeled examples of that vendor's invoices, you adapt the extraction to the unique fields, tables, and formatting, significantly reducing errors. This leverages the service's supervised learning capability to improve accuracy for domain-specific documents.

Answer

Use a different prebuilt model.

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Disable the OCR step.

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Increase the confidence threshold.

Question 9

Your organization is implementing a knowledge mining solution for a research institute that needs to extract chemical compound names and reactions from scientific articles in PDF format. The solution must use a custom model because the scientific terminology is not covered by built-in skills. You have trained a custom model using Azure AI Language's custom entity recognition (NER) and deployed it as a REST endpoint. You are using Azure AI Search with a skillset. How should you integrate the custom NER model into the enrichment pipeline?

Accepted Answer

Create a custom skill that calls the custom NER endpoint and map the output to the index fields.. To integrate a custom NER model into an Azure AI Search enrichment pipeline, you must create a custom skill that calls the custom NER endpoint. The custom skill is a web API that the skillset invokes, and it can map the JSON output to index fields. This allows the enrichment pipeline to use the custom model's predictions.

Answer

Use a Language Understanding (LUIS) app to extract entities and call it from a custom skill.

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Use the built-in Entity Recognition skill and configure it with your custom model's endpoint.

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Configure the indexer to call the custom NER endpoint directly during indexing.

Question 10

Which THREE components are required to build a custom skill for Azure AI Search enrichment?

Accepted Answer

A web API endpoint that accepts JSON input and returns JSON output.. To build a custom skill for Azure AI Search enrichment, you must expose your logic through a web API endpoint that accepts JSON input and returns JSON output (C), because the skillset invokes the skill via an HTTP POST with a JSON payload and expects a JSON response. The endpoint must be secured with HTTPS (D), since Azure AI Search requires custom skill connections to use HTTPS for secure transport. You also need a JSON schema defining inputs and outputs (E), which describes the expected input fields and output fields so the skillset can map enriched document content correctly. A database for intermediate results (A) is not required, as the skillset pipeline passes data between skills in memory, and a Power Automate flow (B) is not a supported orchestration mechanism for custom skills.

Answer

A database to store intermediate results.

Answer

A Power Automate flow to orchestrate the skill.

Question 11

You are planning to use Azure AI Vision to analyze images for a retail inventory management application. The solution must detect products on shelves and read expiration dates. Which two Azure AI Vision capabilities should you use?

Accepted Answer

Object Detection. Object Detection (B) is correct because it identifies and locates products on shelves by drawing bounding boxes around each detected item, which is essential for inventory tracking. Optical Character Recognition (OCR) (D) is correct because it extracts text from images, enabling the reading of expiration dates printed on product labels or packaging.

Answer

Image Captioning

Answer

Face Detection

Question 12

Your team is developing a chatbot using Azure AI Bot Service. You need to ensure that the bot can handle multiple languages and respond appropriately. Which Azure AI service should you integrate to perform language detection?

Accepted Answer

Azure AI Language. Azure AI Language provides pre-built language detection capabilities as part of its natural language processing (NLP) features. By integrating this service, the bot can analyze incoming text and identify the language, enabling it to route responses appropriately or trigger language-specific logic.

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Azure AI Speech

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Azure AI Content Safety

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Azure AI Translator

Question 13

You are testing a Conversational Language Understanding application. You send the JSON request shown in the exhibit. What is the purpose of this request?

Accepted Answer

Analyze the utterance for intent and entities.. The JSON request sends a user utterance to a Conversational Language Understanding (CLU) endpoint, which is designed to analyze natural language input. The response will include the predicted intent (e.g., 'GetWeather') and extracted entities (e.g., 'location: Seattle'), fulfilling the core function of CLU: intent and entity recognition. This is not a generative or translation task; it is a classification and extraction operation.

Answer

Translate the text to another language.

Answer

Generate a response to the user.

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Summarize the conversation.

Question 14

Which TWO Azure AI services can be used to detect objects in images?

Accepted Answer

Custom Vision. Custom Vision (C) is correct because it is an Azure AI service that lets you train and deploy custom image classification and object detection models, returning bounding boxes for detected objects in images. Computer Vision Object Detection API (E) is correct because the Azure AI Vision (Computer Vision) service provides a prebuilt object detection capability that identifies common objects and their bounding box coordinates in an image. Video Indexer (A) is not the right fit here since it focuses on analyzing and indexing video and audio content rather than being an image object detection service. Face API (B) only detects and analyzes human faces (and related attributes), not general objects. Azure AI Document Intelligence (D) extracts text, key-value pairs, and structured data from documents, so it does not perform general object detection in images.

Answer

Video Indexer

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Face API

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Azure AI Document Intelligence

Question 15

Your team develops a document translation solution using Azure AI Translator. The solution must translate documents while preserving formatting and layout. Which feature should you use?

Accepted Answer

Azure AI Translator Document Translation. Azure AI Translator Document Translation is specifically designed to translate entire documents while preserving the original formatting, structure, and layout. Unlike Text Translation, which handles only plain text strings, Document Translation processes files (e.g., PDF, Word, Excel) and returns a translated version with the same formatting, making it the correct choice for this requirement.

Answer

Azure AI Translator Custom Translator

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Azure AI Document Intelligence

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Azure AI Translator Text Translation