AI-102 Implement natural language processing solutions Practice Test 9 — 15 Questions

Question 1

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.

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Conversational Language Understanding

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Azure AI Language Detection

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Azure AI Speech Translation

Question 2

Which TWO Azure AI Language features can you use to extract structured data from unstructured text?

Accepted Answer

Entity Linking. Entity Linking (C) and Named Entity Recognition (E) are both Azure AI Language features designed to extract structured data from unstructured text. Entity Linking disambiguates entities by linking them to a knowledge base (e.g., Wikipedia), providing a unique identifier and structured metadata. Named Entity Recognition identifies and categorizes entities (e.g., person, organization, location) directly from text, enabling extraction of structured information.

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Sentiment Analysis

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Language Detection

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Key Phrase Extraction

Question 3

Which THREE factors should you consider when choosing between a pre-built model and a custom model in Azure AI Language?

Accepted Answer

Domain-specific vocabulary coverage. Pre-built models in Azure AI Language are trained on general web-scale data and may lack domain-specific vocabulary (e.g., medical terminology, legal jargon). If your use case requires understanding specialized terms, a custom model trained on domain-specific labeled data will achieve higher accuracy. The choice hinges on whether the pre-built model's general vocabulary covers your domain's unique terms.

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Need for a trained endpoint

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Model size and memory footprint

Question 4

Which TWO Azure services can be used to implement a conversational AI solution that understands user intent and responds appropriately?

Accepted Answer

Azure Bot Service. Azure Bot Service provides the framework for building, deploying, and managing conversational bots that can interact with users across multiple channels. Conversational Language Understanding (CLU) is a cloud-based API that applies machine learning to extract user intents and entities from natural language utterances, enabling the bot to understand what the user wants and respond appropriately.

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Azure AI Speech-to-Text

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Azure AI Translator

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Azure AI Search

Question 5

Refer to the exhibit. You send this request to the Conversational Language Understanding API. The response includes the intent 'BookFlight' with entities 'FromCity: Seattle' and 'ToCity: Boston', but the 'Date' entity is missing. What is the most likely cause?

Accepted Answer

The model was not trained to recognize date entities. The Conversational Language Understanding (CLU) API returns only intents and entities that the deployed model was explicitly trained to recognize. If the training data did not include labeled 'Date' entities, the model will not extract them regardless of the input text. The API itself supports entity extraction, and the endpoint and string index type settings do not affect whether a specific entity type is recognized.

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The stringIndexType should be 'Utf16CodeUnit'

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The API version does not support entity extraction

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The endpoint is pointing to the wrong deployment

Question 6

Refer to the exhibit. You are calling the Azure AI Language NER API. The response returns no entities. What is the most likely reason?

Accepted Answer

The text does not contain any recognized entities. The NER API returns entities only if the input text contains recognized entity types (e.g., Person, Location, Organization, DateTime, etc.). If no entities are found, the API returns an empty entities array. This is the most straightforward and common reason for a zero-entity response, assuming the request is otherwise valid.

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The API version is incorrect

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The document language should be 'es' for Spanish

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The endpoint URL is for the wrong region

Question 7

You are building a chatbot that must handle customer inquiries about order status. The solution must use Azure AI Language and support multiple languages. You need to configure the project to detect language automatically from user input. Which setting should you enable?

Accepted Answer

Enable multi-lingual detection in the project language settings.. Azure AI Language's multi-lingual detection setting enables the project to automatically identify the language of user input without requiring explicit language selection. This is essential for a chatbot handling customer inquiries in multiple languages, as it allows the service to route the input to the appropriate language-specific model or processing pipeline, ensuring accurate order status responses across languages.

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Set the primary language to English only.

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Use the conversation summarization feature.

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Enable spelling correction in the project settings.

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. Custom Entity Extraction (D) is correct because it allows you to train a model with a small set of labeled examples to extract domain-specific entities like product names, order numbers, and dates from customer emails. This service supports iterative learning and improvement over time, making it ideal for scenarios where pre-built models lack the required specificity.

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QnA Maker

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Pre-built Entity Extraction

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Text Analytics for health

Question 9

Your organization needs to analyze customer feedback from social media posts to determine the sentiment (positive, negative, neutral). The solution must process up to 10,000 posts per day and provide a confidence score for each sentiment. Which Azure AI service should you use?

Accepted Answer

Azure AI Language Service. Azure AI Language Service provides pre-built sentiment analysis capabilities that can process up to 10,000 posts per day and return a confidence score for each sentiment (positive, negative, neutral). This service is specifically designed for natural language processing tasks like sentiment analysis, making it the correct choice for analyzing customer feedback from social media posts.

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Azure AI Speech Service

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Azure AI Translator

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Azure AI Language Understanding (LUIS)

Question 10

You are developing a multilingual chatbot that must understand user intents in English, Spanish, and French. You are using the Azure AI Language service with a Conversational Language Understanding (CLU) project. What is the recommended approach to handle multiple languages?

Accepted Answer

Add utterances in all three languages to the CLU project and enable multi-lingual detection.. The Azure AI Language service's Conversational Language Understanding (CLU) supports multi-lingual projects natively. By adding utterances in English, Spanish, and French to a single CLU project and enabling multi-lingual detection, the model learns to recognize intents across languages without needing separate projects or translation steps. This approach leverages the underlying multilingual BERT-based model, which shares language-agnostic representations, making it the recommended and most efficient method.

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Use Azure AI Translator to translate all input to English before sending to CLU.

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Use the Translator service to detect language and route to language-specific CLU endpoints.

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Create separate CLU projects for each language.

Question 11

You have a custom Named Entity Recognition (NER) model trained using Azure AI Language. The model is performing poorly on new data. You need to improve its accuracy. Which action should you take first?

Accepted Answer

Review the test set results and add more labeled examples for entities with low precision/recall.. The first step to improve a custom NER model's accuracy is to analyze the test set results to identify which entity types have low precision or recall, then add more labeled examples for those specific entities. This targeted data augmentation addresses the root cause of poor performance—insufficient or imbalanced training data—rather than blindly adjusting hyperparameters or reducing complexity.

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Increase the training epochs.

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Retrain the model using the same training data.

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Reduce the number of entity types in the model.

Question 12

You need to build a solution that can answer questions based on a set of PDF documents, such as product manuals. The solution should allow users to ask questions in natural language and receive answers with citations. Which Azure AI service should you use?

Accepted Answer

Azure AI Custom Question Answering. Azure AI Custom Question Answering (part of Azure AI Language) is specifically designed to extract question-answer pairs from semi-structured content like PDFs and provide answers with citations. It uses a deep learning-based extractive QA model that can locate answer spans within documents and return the source text as a citation, directly meeting the requirement for natural language questions and cited answers from product manuals.

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Azure AI Text Analytics

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Azure Cognitive Search

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Azure AI Document Intelligence (formerly Form Recognizer)

Question 13

You are deploying a Conversational Language Understanding (CLU) model to production. You need to monitor the model's performance and detect when retraining is needed due to concept drift. Which metric should you monitor?

Accepted Answer

Average confidence scores of predictions. Average confidence scores of predictions is the correct metric because a sustained drop in confidence indicates that the model is encountering utterances that differ from its training distribution, which is a classic sign of concept drift. Monitoring confidence scores allows you to detect when the model's predictions become less certain, triggering the need for retraining with new data.

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Response time for each prediction

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Number of endpoint calls

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Number of utterances processed per day

Question 14

Your organization has a large corpus of legal documents that need to be analyzed for specific clauses. You need to extract key information such as party names, dates, and monetary amounts. The solution must be able to handle varying document formats (PDF, Word, scanned images). Which combination of Azure AI services should you use?

Accepted Answer

Azure AI Document Intelligence and Custom Entity Extraction. Azure AI Document Intelligence (formerly Form Recognizer) is designed to extract text, structure, and key-value pairs from PDFs, Word documents, and scanned images using OCR and layout analysis. Combining it with Custom Entity Extraction (via Azure AI Language's custom NER) allows you to identify domain-specific entities like party names, dates, and monetary amounts across varying formats. This pairing directly addresses the need for both document parsing and tailored entity recognition.

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Azure AI Computer Vision and Custom Entity Extraction

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Azure AI Translator and Custom Entity Extraction

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Azure AI Speech and Custom Entity Extraction

Question 15

You want to use the Azure AI Language service to summarize long customer support conversations into a short summary. Which feature should you use?

Accepted Answer

Conversational Summarization. Conversational Summarization is the correct feature because it is specifically designed to condense multi-turn dialogues, such as customer support conversations, into concise summaries. Unlike generic text summarization, it understands the conversational flow, speaker turns, and context to produce a coherent summary of the interaction.

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Sentiment Analysis

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Entity Extraction

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Key Phrase Extraction