AI-102 Practice Test 3 — 30 Questions

Question 1

Your application needs to extract key phrases from customer reviews to identify common topics. Which Azure AI Language feature should you use?

Accepted Answer

Key Phrase Extraction. Key Phrase Extraction is the correct Azure AI Language feature because it is specifically designed to identify and return a list of key phrases from unstructured text, such as customer reviews, that capture the main topics and themes. This allows you to aggregate common topics across multiple reviews without manual analysis.

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Sentiment Analysis

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Language Detection

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Named Entity Recognition

Question 2

You are building a mobile app that allows users to take a photo of a product and get detailed information. The app uses Azure AI Custom Vision to classify products. You need to ensure low latency for inference. What should you do?

Accepted Answer

Export the Custom Vision model as a TensorFlow model and run on-device. Exporting the Custom Vision model as a TensorFlow model and running it on-device eliminates network latency entirely. Inference happens locally on the mobile device, which provides the lowest possible latency for real-time classification, especially when network connectivity is poor or inconsistent.

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Increase the number of training iterations

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Use the Azure AI Vision API directly

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Use Azure Front Door to cache results

Question 3

You have a real-time video processing pipeline using Azure AI Video Indexer. You need to detect when a specific person appears in archived video footage. Which approach minimizes latency and cost?

Accepted Answer

Use Video Indexer's face detection and indexing, then search. Video Indexer's built-in face detection and indexing automatically identifies and tracks faces during the indexing process, storing the results in a searchable metadata index. To detect when a specific person appears, you can then search the indexed metadata for that person's face ID or name, which avoids re-processing the video and minimizes both latency and cost. This approach leverages the one-time indexing cost and optimized search capabilities rather than running additional AI services on every frame.

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Extract keyframes and use Custom Vision to detect the person

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Run face detection on every frame using Azure AI Face and store results

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Use Azure AI Vision to detect faces in video frames and compare against a database

Question 4

A company is building a chatbot using Azure OpenAI Service to answer customer queries. The chatbot must not generate harmful or offensive content. Which Azure AI service should be integrated to filter inappropriate content?

Accepted Answer

Azure AI Content Safety. Azure AI Content Safety is the correct service because it provides built-in content moderation APIs that detect and filter harmful or offensive text and images, including hate speech, violence, self-harm, and sexual content. Integrating this service with the Azure OpenAI chatbot ensures that user inputs and model outputs are screened in real time, preventing the generation of inappropriate responses.

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Azure Bot Service

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Azure Cognitive Search

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Azure Form Recognizer

Question 5

You are an Azure AI engineer at Fabrikam Inc. The company has developed a custom vision model using Azure Custom Vision to detect defects on a manufacturing assembly line. The model is deployed as a Docker container to an on-premises edge device using Azure IoT Edge. Recently, the model's inference accuracy has decreased. The operations team reports that the edge device is running low on memory and CPU. The model was trained with images from a specific camera angle, but the camera angle has been changed slightly due to maintenance. You need to improve the model's accuracy. What should you do?

Accepted Answer

Retrain the model with new images captured from the current camera angle.. The decrease in accuracy is most likely due to the change in camera angle, which introduces a domain shift between the training images and the new inference images. Retraining the model with images captured from the current camera angle will realign the training data distribution with the production environment, directly addressing the root cause of the accuracy drop. This is a standard practice in Custom Vision when deployment conditions change.

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Upgrade the edge device to have more memory and CPU.

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Reduce the image resolution to lower memory usage.

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Convert the model to use grayscale images.

Question 6

You are building a generative AI application using Azure OpenAI Service. The application must provide factual answers based on your company's internal knowledge base. You need to minimize the risk of the model generating incorrect information (hallucinations). Which approach should you take?

Accepted Answer

Implement Retrieval-Augmented Generation (RAG) with Azure AI Search.. Retrieval-Augmented Generation (RAG) with Azure AI Search grounds the model's responses in your company's internal knowledge base by retrieving relevant documents in real time and injecting them into the prompt. This reduces hallucinations by ensuring the model generates answers based on retrieved facts rather than relying solely on its parametric memory. Azure AI Search provides vector and hybrid search capabilities that efficiently index and query your documents, making RAG the most effective approach for factual accuracy.

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Fine-tune the model on your company's documents.

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Use few-shot prompting with examples of correct answers.

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Set the max_tokens parameter to a low value.

Question 7

You are a data scientist at a healthcare company. You have deployed a GPT-4 model using Azure OpenAI to answer patient inquiries about medical conditions. The model is configured with temperature=0.3 and max_tokens=200. Recently, the compliance team flagged that some responses contain contradictory information compared to the official medical guidelines. You need to ensure the model's answers align strictly with the provided medical documents (stored as PDFs in Azure Blob Storage). You have access to Azure Cognitive Search and Azure AI Document Intelligence. The solution must minimize hallucinations and not require retraining the model. What should you do?

Accepted Answer

Index the medical PDFs into Azure Cognitive Search. Configure the Azure OpenAI deployment to use 'Add your data' pointing to this index. Set the system message to instruct the model to base answers only on the retrieved context.. It uses Azure Cognitive Search to index the medical PDFs and then configures the Azure OpenAI deployment with 'Add your data' to retrieve relevant context from that index at inference time. This retrieval-augmented generation (RAG) approach grounds the model's answers in the official documents without retraining, directly addressing the compliance team's requirement to align responses with the provided guidelines and minimize hallucinations.

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Use prompt engineering to add a system message that tells the model to only answer based on the uploaded PDFs. Keep the current deployment.

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Fine-tune GPT-4 on the medical documents using Azure OpenAI fine-tuning capabilities. Use the fine-tuned model for the chatbot.

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Deploy Azure AI Content Safety to filter responses that contradict guidelines. Set up a custom content filter using a list of approved phrases.

Question 8

A company needs to implement a chatbot that answers customer queries using a knowledge base. Which Azure AI service should be used to build the knowledge base?

Accepted Answer

Azure AI Language (Question Answering). Azure AI Language's Question Answering feature is specifically designed to create a knowledge base from structured or unstructured content (e.g., FAQs, product manuals, support documents). It uses a custom question-answering model that can be trained and published as a REST API endpoint, which a chatbot can then query to retrieve precise answers. This makes it the correct service for building the knowledge base itself.

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Azure Bot Service

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Azure AI Translator

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Azure AI Speech

Question 9

You have deployed a chatbot using Azure OpenAI with a system message as shown. The chatbot sometimes provides incorrect answers that are not supported by the sources. What is the most likely cause?

Accepted Answer

The system message does not guarantee grounding; the model may still hallucinate.. A system message in Azure OpenAI provides instructions and context but does not enforce factual grounding. The model can still generate responses that are not supported by the provided sources (hallucination), especially if the system message is not explicitly designed to restrict the model to only use the given data. Grounding requires additional techniques like retrieval-augmented generation (RAG) or explicit constraints in the prompt.

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Content filters are incorrectly configured, allowing harmful content.

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The data ingestion pipeline has errors, so the sources are not available.

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The temperature parameter is too low, causing repetitive answers.

Question 10

A company is building a chatbot using Azure OpenAI Service to handle customer inquiries. The bot sometimes responds with incorrect or fabricated information. The team wants to ground the model responses using their own product documentation stored in Azure Cognitive Search. Which configuration should they implement?

Accepted Answer

Configure Azure OpenAI on your data with Azure Cognitive Search as the data source.. It directly integrates Azure Cognitive Search as a data source for Azure OpenAI, enabling the model to retrieve and ground its responses in the indexed product documentation. This approach uses a 'retrieve-then-read' pattern where the search results are injected into the prompt, reducing hallucinations by constraining the model's output to verified content.

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Use Azure AI Document Intelligence to extract text and generate embeddings, then store them in a vector database for direct similarity search.

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Fine-tune the GPT model on the product documentation dataset.

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Enable the semantic ranker in Azure Cognitive Search to improve the relevance of search results.

Question 11

You are building a custom named entity recognition (NER) model using Azure AI Language. After labeling 200 documents, you train the model and achieve 85% precision but only 60% recall. Which action is most likely to improve recall?

Accepted Answer

Increase the number of labeled documents, especially those containing the target entities. Low recall in a custom NER model typically indicates that the model is failing to identify many instances of the target entities. Increasing the number of labeled documents, especially those containing the target entities, provides more positive examples for the model to learn from, directly improving its ability to recognize those entities and thus boosting recall.

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Lower the confidence threshold

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Increase the training hours

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Switch to a different Azure AI Language feature

Question 12

Refer to the exhibit. You called the Named Entity Recognition API on a document. Which entity type is "Seattle"?

Accepted Answer

Location. The Named Entity Recognition (NER) API in Azure AI Language identifies 'Seattle' as a Location entity because it is a recognized geographical place. The API uses a pre-trained model that categorizes entities into types such as Location, Person, Organization, etc., and 'Seattle' falls under the Location type based on its semantic context in the document.

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Organization

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Person

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City

Question 13

Refer to the exhibit. You are deploying a GPT-4 model using Azure OpenAI Service. The deployment uses the Standard scale type. Which statement is true about this deployment?

Accepted Answer

The deployment uses pay-as-you-go pricing and global rate limits.. The Standard scale type in Azure OpenAI Service uses pay-as-you-go pricing, where you are billed based on the number of tokens processed. It also enforces global rate limits (e.g., tokens per minute) that apply across all deployments in the region, rather than providing dedicated capacity. Option D correctly identifies these characteristics.

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The model version is not specified and will default to the latest.

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Content filtering is disabled for this deployment.

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The deployment uses provisioned throughput with reserved capacity.

Question 14

You are deploying a generative AI application using Azure OpenAI Service. The application must generate responses in multiple languages while maintaining high accuracy. You need to minimize token usage. Which approach should you recommend?

Accepted Answer

Use a base model with a system message to output in the desired language. Azure OpenAI Service base models (e.g., GPT-4) natively support multilingual generation via a system message that sets the desired output language. This approach avoids the overhead of translation pipelines or fine-tuning, directly minimizing token usage while maintaining high accuracy through the model's inherent multilingual capabilities.

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Translate all input to English and then translate output back

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Fine-tune a model for each target language

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Use a separate deployment for each language

Question 15

You are creating a new Custom Vision project with the above JSON. The domainId corresponds to the 'Logo' domain. Which type of model will this project train?

Accepted Answer

A multilabel image classification model for logo detection. The 'Logo' domain in Custom Vision is specifically designed for image classification tasks, not object detection. When you create a project with the 'Logo' domain, it trains a multilabel image classification model, meaning each image can be assigned multiple labels (e.g., multiple logos in one image). This domain is optimized for identifying and classifying logos within images, making it distinct from object detection or general classification.

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An object detection model for logo detection

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An optical character recognition model

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A general image classification model

Question 16

A company is using Azure Form Recognizer to extract data from invoices. The prebuilt model does not correctly extract a custom field that is specific to the company's invoices. What is the most appropriate action to improve extraction accuracy for this field?

Accepted Answer

Train a custom model using labeled invoices that include the custom field.. The prebuilt Form Recognizer model is designed for common invoice layouts and may not recognize company-specific fields. Training a custom model with labeled invoices that include the custom field allows the model to learn the field's location and semantics, significantly improving extraction accuracy for that specific field.

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Use the prebuilt model with a custom field mapping.

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Adjust the confidence threshold for the prebuilt model.

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Retrain the prebuilt model with additional invoices.

Question 17

Refer to the exhibit. You are configuring a system message for an Azure OpenAI deployment. The assistant is still generating harmful code despite the instruction. Which additional measure should you implement?

Accepted Answer

Enable Azure AI Content Safety with a custom blocklist for harmful code.. Azure AI Content Safety provides a dedicated content filtering layer that can block harmful code generation at the inference level, regardless of the system message. A custom blocklist allows you to define specific patterns (e.g., code snippets for malware) that the model is prohibited from outputting, enforcing safety beyond prompt instructions.

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Fine-tune the model on safe code examples.

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Lower the temperature parameter to 0.

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Add more examples to the prompt.

Question 18

A company is building a chatbot using Azure Bot Service and Language Understanding (LUIS). The chatbot needs to handle user intents for booking flights and checking flight status. After testing, the chatbot frequently fails to distinguish between the two intents when users mention flight numbers. Which action should the engineer take to improve intent recognition?

Accepted Answer

Add more utterances that include flight numbers to the training data for both intents.. Adding more utterances that include flight numbers to both intents provides LUIS with more varied examples of how flight numbers appear in natural language, enabling the model to learn distinguishing patterns. Without sufficient training data containing flight numbers, LUIS cannot reliably differentiate between 'BookFlight' and 'CheckFlightStatus' when users mention flight numbers, as the entity alone does not determine intent.

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Increase the number of intents to split the flight-related queries further.

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Reduce the confidence score threshold for intent detection.

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Use the prebuilt domain for flight booking to improve accuracy.

Question 19

A company wants to extract key-value pairs from scanned invoices using Azure AI. Which service should they use?

Accepted Answer

Azure AI Document Intelligence. Azure AI Document Intelligence (formerly Form Recognizer) is the correct choice because it is specifically designed to extract key-value pairs, tables, and structured data from scanned documents like invoices. Unlike the Read API or OCR API, which only return raw text or OCR output, Document Intelligence uses prebuilt models (e.g., 'prebuilt-invoice') that understand the semantic layout of invoices, enabling direct extraction of fields such as invoice number, date, and total amount.

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Read API

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Custom Vision

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OCR API

Question 20

You are building a solution to detect if a person is wearing a hard hat in construction site images. You have a small dataset of labeled images. Which Azure service should you use?

Accepted Answer

Azure Custom Vision. Azure Custom Vision is the correct choice because it allows you to train a custom image classification model with your own small dataset of labeled construction site images to detect whether a person is wearing a hard hat. Unlike pre-built services, Custom Vision specializes in fine-tuning models for specific visual concepts that are not covered by general-purpose APIs, making it ideal for niche object detection tasks like hard hat detection.

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Azure AI Vision Image Analysis

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Azure Video Indexer

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Azure AI Document Intelligence