AI-102 Practice Test 8 — 10 Questions

Question 1

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?

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

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 2

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 3

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 4

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 5

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 6

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.

Answer

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 7

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 8

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 9

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 10

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