AI-102 Practice Test 36 — 15 Questions

Question 1

You are developing a solution that uses Azure AI Translator to translate documents from English to French. You need to ensure that the translated text maintains the original formatting, such as HTML tags. Which feature should you use?

Accepted Answer

Set the content type to 'text/html' in the translation request.. Option C is correct because the translator can preserve the original formatting when using the appropriate content type or using the 'textType' parameter set to 'html'. Option A is wrong because the glossary is for custom translations. Option B is wrong because the parallel corpus is for training custom models. Option D is wrong because transliteration converts between scripts, not formats.

Answer

Use the Translator glossary to preserve formatting.

Answer

Use the Translator parallel corpus to train a custom model.

Answer

Use the Transliterate method to preserve formatting.

Question 2

Refer to the exhibit. You are creating a Conversational Language Understanding (CLU) project using the Azure AI Language Service REST API. You want the project to support both English and Spanish utterances. Which parameter in the request body enables this?

Accepted Answer

multilingual. The correct answer is D because the 'multilingual' parameter set to true enables support for multiple languages. A (confidenceThreshold) is for prediction confidence. B (projectName) is just the name. C (language) sets the primary language but does not enable multilingual support.

Answer

projectName

Answer

language

Answer

confidenceThreshold

Question 3

You need to use Azure AI Language to analyze customer feedback. Which THREE analysis types are available in the Text Analytics API?

Accepted Answer

Sentiment analysis. Options A, B, and C are correct because these are standard features. Option D is wrong because image analysis is part of Azure AI Vision. Option E is wrong because speech-to-text is part of Azure AI Speech.

Answer

Image captioning

Answer

Speech-to-text

Question 4

Your NLP solution uses custom text classification in Azure AI Language. You need to improve the model's accuracy. Which action should you take?

Accepted Answer

Add more labeled training data with balanced classes.. Option D is correct because adding more labeled data improves model accuracy. Option A is incorrect because increasing training epochs may lead to overfitting. Option B is incorrect because using a different pretrained model is not applicable. Option C is incorrect because decreasing batch size may not help.

Answer

Increase the number of training epochs.

Answer

Decrease the batch size.

Answer

Use a different pretrained model.

Question 5

You are building a chatbot using Azure AI Language and need to handle user intents that are not covered by the predefined intents. What should you implement?

Accepted Answer

A 'None' intent in a conversational language understanding project. A 'None' intent in a conversational language understanding (CLU) project captures utterances that do not match any defined intent, allowing the bot to respond appropriately. Fallback is a general term, and custom entities are not intents.

Answer

Custom entities to capture unknown phrases

Answer

A fallback intent in the QnA Maker knowledge base

Answer

A prebuilt intent from the LUIS catalog

Question 6

Which TWO components are required to create a custom text classification model in Azure AI Language?

Accepted Answer

A set of labeled documents. A custom text classification project requires a project in Azure AI Language and a set of labeled documents for training. A LUIS app and a QnA maker knowledge base are different services. An Azure Functions app is not required.

Answer

A QnA Maker knowledge base

Answer

A Language Understanding (LUIS) app

Answer

An Azure Functions app

Question 7

You are building a multilingual chatbot using Azure AI Language. For a given user utterance, you need to first detect the language, then route to the appropriate language-specific intent model. Which combination of Azure AI Language features should you use?

Accepted Answer

Language Detection and Conversational Language Understanding. Language Detection identifies the language, then CLU (Conversational Language Understanding) processes intents. Custom Text Classification is for document-level classification, not conversational intents. Translator translates text but does not detect language. Key Phrase Extraction is not needed.

Answer

Key Phrase Extraction and Conversational Language Understanding

Answer

Translator and Conversational Language Understanding

Answer

Language Detection and Custom Text Classification

Question 8

You are designing a solution that must extract specific entities from customer emails, such as product names, order numbers, and dates. The solution must be able to learn from a small set of labeled examples and improve over time. Which Azure AI service should you use?

Accepted Answer

Custom Entity Extraction. Option C is correct because Custom Entity Extraction in Azure AI Language allows you to train a model with a small set of labeled data to extract custom entities. Option A is wrong because Pre-built Entity Extraction only works with predefined entity types. Option B is wrong because Text Analytics for health is specialized for medical data. Option D is wrong because the QnA Maker is for question answering, not entity extraction.

Answer

QnA Maker

Answer

Pre-built Entity Extraction

Answer

Text Analytics for health

Question 9

You are developing a custom text classification model using Azure AI Language. You have labeled 2000 documents across 10 categories. You need to evaluate the model's performance before deploying to production. Which THREE metrics should you examine?

Accepted Answer

Recall. Options A, B, and D are correct because precision, recall, and F1 score are the standard metrics for classification models. Option C is wrong because BLEU is for machine translation. Option E is wrong because Word Error Rate is for speech recognition.

Answer

Word Error Rate

Answer

BLEU Score

Question 10

You are building a multilingual chatbot using Azure AI Language. The chatbot must handle English, Spanish, and French. You need to configure the LUIS (Language Understanding) model to support multiple languages efficiently. What is the best practice?

Accepted Answer

Create a separate LUIS app for each language and use the same intents and entities structure.. Option B is correct because LUIS supports a primary language and allows using the same app for multiple languages by training separate versions. Option A is wrong because you cannot have multiple languages in a single app without separate training. Option C is wrong because the prebuilt multilingual model does not exist. Option D is wrong because LUIS does not support per-language query prediction within the same app without separate models.

Answer

Use the prebuilt multilingual LUIS model and fine-tune it with your intents.

Answer

Create a single LUIS app and add utterances in all three languages.

Answer

Create one LUIS app with language set to 'Multilingual' and add utterances in all languages.

Question 11

Refer to the exhibit. You have an Azure AI Language resource named MyLangService. You need to call the conversational language understanding (CLU) API. Which URL should you use?

Accepted Answer

https://mylangservice.cognitiveservices.azure.com/language/:analyze-conversations?api-version=2022-10-01-preview. Option A is correct because the CLU endpoint is shown as the full URL with the path '/language/:analyze-conversations'. Option B is wrong because that URL is for Text Analytics. Option C is wrong because it uses the Text Analytics base URL incorrectly. Option D is wrong because it uses an invalid path.

Answer

https://mylangservice.cognitiveservices.azure.com/text/analytics/v3.0/sentiment

Answer

https://mylangservice.cognitiveservices.azure.com/language/analyze-conversations?api-version=2022-10-01-preview

Answer

https://mylangservice.cognitiveservices.azure.com/

Question 12

A company is building a chatbot using Azure AI Language. The chatbot must detect user intent from utterances and also extract key entities like dates and product names. The solution must minimize latency for real-time conversation. Which approach should the team use?

Accepted Answer

Use the Conversational Language Understanding (CLU) feature of Azure AI Language, which supports both intent and entity extraction in a single API call.. Option B is correct because using the Conversational Language Understanding (CLU) feature of Azure AI Language with a single API call to trigger both intent detection and entity extraction minimizes latency by combining both tasks in one request. Option A is wrong because using separate calls for intent and entity increases latency. Option C is wrong because LUIS is being replaced by CLU and does not support the latest features. Option D is wrong because QnA Maker is for FAQ-style Q&A, not for intent recognition.

Answer

Use QnA Maker with a custom question-answer pair for each intent and entity.

Answer

Use the Language Understanding (LUIS) service with a single call for both intent and entity extraction.

Answer

Use two separate calls to the Azure AI Language API: one for intent recognition and one for entity extraction.

Question 13

You are developing a chat application that uses Azure OpenAI GPT-4 to answer customer questions. You need to ensure the model does not generate harmful content. Which configuration should you set?

Accepted Answer

Enable the content filter in Azure OpenAI Service.. Option B is correct because the content filter in Azure OpenAI Service blocks harmful content. Option A is incorrect because temperature controls randomness, not content safety. Option C is incorrect because max_tokens limits response length, not safety. Option D is incorrect because system prompts guide behavior but are not a safety mechanism.

Answer

Use a system prompt that instructs the model to be safe.

Answer

Set the temperature parameter to 0.

Answer

Set max_tokens to a low value.

Question 14

Which THREE factors should you consider when selecting a region for an Azure AI Language resource?

Accepted Answer

Data residency requirements.. Options A, C, and E are correct. Data residency is important for compliance. Language support varies by region. Latency to end users affects performance. Option B is incorrect because pricing is generally consistent across regions. Option D is incorrect because SKU availability may vary but is not the primary factor.

Answer

Pricing differences.

Answer

Availability of the Free tier SKU.

Question 15

Your organization uses Azure AI Language to analyze customer support tickets. You need to ensure that personally identifiable information (PII) is detected and redacted before further processing. Which TWO features should you use?

Accepted Answer

Redaction. PII detection identifies personal data in text. Entity linking and key phrase extraction do not detect PII. Conversation summarization is not for PII.

Answer

Entity linking

Answer

Conversation summarization

Answer

Key phrase extraction