AI-900 Describe features of Natural Language Processing workloads on Azure Practice Test 6 — 15 Questions

Question 1

A hospital collects patient experience feedback in free-text form. They need to automatically (1) extract specific mentions of symptoms (e.g., 'headache', 'fever', 'fatigue') from the text, and (2) determine the overall emotional tone of each feedback (e.g., positive, negative, neutral). Which combination of Azure AI Language features should they use?

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

Accepted Answer

A. Entity recognition and sentiment analysis. The hospital needs two distinct NLP capabilities: extracting specific symptom mentions (entity recognition) and determining emotional tone (sentiment analysis). Azure AI Language's entity recognition identifies named entities like symptoms, while sentiment analysis evaluates text for positive, negative, or neutral sentiment. Together, they directly address both requirements without extraneous features.

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B. Key phrase extraction and question answering

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C. Language detection and text classification

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D. Summarization and translation

Question 2

A multinational corporation receives customer support emails in multiple languages. They need to automatically identify the language of each email so it can be routed to the appropriate support team. Which Azure AI Language feature should they use?

Accepted Answer

Language detection. Language detection is the correct Azure AI Language feature because it is specifically designed to identify the written language of text input. The multinational corporation's requirement to automatically determine the language of each email for routing directly matches the core functionality of this prebuilt capability, which returns a language name and ISO 639-1 code for each document.

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Sentiment analysis

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Key phrase extraction

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Entity recognition

Question 3

A company analyzes customer reviews to identify common themes. They need to automatically extract the most important concepts from each review, such as 'battery life', 'customer service', and 'price'. Which Azure AI Language feature should they use?

Accepted Answer

Key phrase extraction. Key phrase extraction is the correct Azure AI Language feature because it automatically identifies and extracts the most important concepts, such as 'battery life', 'customer service', and 'price', from unstructured text like customer reviews. It returns a list of key phrases that represent the main topics discussed, which directly matches the requirement to extract important concepts.

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Sentiment analysis

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Language detection

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Named entity recognition

Question 4

A marketing team needs to analyze thousands of product reviews to identify the most frequently mentioned topics, such as 'battery life', 'customer support', and 'price'. They want an automated method to extract these main concepts from each review. Which Azure AI Language feature should they use?

Accepted Answer

Key phrase extraction. Key phrase extraction is the correct choice because it automatically identifies the main concepts, such as 'battery life', 'customer support', and 'price', from unstructured text like product reviews. This feature is specifically designed to extract the most salient topics or points from a document, making it ideal for analyzing thousands of reviews to find frequently mentioned themes.

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Language detection

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Sentiment analysis

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Entity recognition

Question 5

A company wants to build a self-service FAQ bot that answers customer questions based on a collection of policy documents (PDFs and Word files). They want the bot to extract answers directly from the documents without manually creating question-answer pairs. Which Azure AI Language feature should they use?

Accepted Answer

Custom Question Answering. Custom Question Answering (C) is the correct choice because it is specifically designed to extract answers directly from source documents (PDFs, Word files) without requiring manual creation of question-answer pairs. It uses a deep learning-based extractive reader to locate answer spans within the text, making it ideal for building a self-service FAQ bot from policy documents.

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Key Phrase Extraction

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Conversational Language Understanding (CLU)

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Azure OpenAI on your data

Question 6

A news agency needs to automatically process thousands of news articles and extract specific structured information: the publication date, the author's name, and the headline. None of these fields are standard predefined entities in Azure AI Language. Which Azure AI Language feature should they use to train a model to extract these custom entities?

Accepted Answer

Custom Named Entity Recognition (Custom NER). Custom Named Entity Recognition (Custom NER) is the correct choice because it allows you to train a model to extract domain-specific entities—such as publication date, author name, and headline—that are not part of Azure's predefined entity catalog. By providing labeled examples, Custom NER learns to identify these custom fields from unstructured text, enabling automated extraction from thousands of news articles.

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Key phrase extraction

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Sentiment analysis

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Language detection

Question 7

A customer support team uses an AI chatbot to analyze incoming messages. They want to automatically identify the most frequently mentioned topics, such as 'shipping delay', 'refund policy', and 'product quality', without manually reading each message. Which Azure AI Language feature should they use?

Accepted Answer

Key Phrase Extraction. Key Phrase Extraction is the correct choice because it automatically identifies the main topics and concepts in text, such as 'shipping delay', 'refund policy', and 'product quality', without requiring manual reading. This feature returns a list of key phrases that represent the most salient points in the input, making it ideal for topic frequency analysis in customer support messages.

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Language Detection

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Sentiment Analysis

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Entity Recognition

Question 8

A multinational corporation receives customer feedback emails in several languages. The company wants to translate all emails into English for centralized analysis by its support team. Which Azure service should they use?

Accepted Answer

Azure Translator. Azure Translator is the correct service because it is specifically designed for text-to-text translation across multiple languages, making it ideal for translating customer feedback emails into English. It provides a REST API that supports real-time or batch translation of text, which aligns with the requirement to process emails in various languages for centralized analysis.

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Azure AI Language

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Azure OpenAI Service

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Azure Speech Service

Question 9

A company wants to automatically categorize support tickets into categories such as 'Billing', 'Technical Issue', and 'Account Management'. They have a set of 1,000 labeled tickets that they can use to train a model. Which Azure AI Language feature should they use?

Accepted Answer

Custom text classification. Custom text classification is the correct choice because it allows you to train a model on your own labeled dataset (1,000 tickets) to categorize text into custom-defined classes like 'Billing', 'Technical Issue', and 'Account Management'. This feature is specifically designed for scenarios where you need to classify documents into user-defined categories, unlike pre-built classification options.

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Key phrase extraction

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Sentiment analysis

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Language detection

Question 10

A legal firm needs to automatically sort incoming legal documents into predefined categories such as 'Contract', 'Brief', 'Motion', and 'Discovery'. They have a set of 500 manually labeled documents to use as examples. Which Azure AI Language feature should they use to build this classification system?

Accepted Answer

Custom text classification. Custom text classification is the correct choice because it allows the legal firm to train a model using their 500 labeled documents to classify text into predefined categories like 'Contract', 'Brief', 'Motion', and 'Discovery'. This feature enables supervised learning where the model learns from labeled examples to automatically sort incoming documents, which is exactly the requirement described.

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Key phrase extraction

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Named entity recognition

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Sentiment analysis

Question 11

A global e-commerce company receives product reviews in multiple languages. They want to automatically identify the language of each review to route it to the appropriate translation queue. Which Azure AI Language feature should they use?

Accepted Answer

Language Detection. Language Detection is the correct Azure AI Language feature because it is specifically designed to identify the language of a given text input, returning a language name and a confidence score. This directly meets the requirement to automatically detect the language of product reviews so they can be routed to the appropriate translation queue.

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Sentiment Analysis

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Key Phrase Extraction

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Entity Recognition

Question 12

A restaurant chain wants to build a voice-powered ordering system for its drive-through. The system must understand when a user wants to place an order, modify an existing order, or cancel an order. It also needs to extract specific details like the menu item name and quantity from the user's speech. Which Azure AI Language feature should they use to handle both intent recognition and entity extraction?

Accepted Answer

Conversational Language Understanding (CLU). Conversational Language Understanding (CLU) is the correct choice because it is specifically designed to handle both intent recognition (e.g., 'place order', 'modify order', 'cancel order') and entity extraction (e.g., menu item name, quantity) from natural language utterances. CLU uses a pre-built or custom model to map user input to intents and extract detailed entities, making it ideal for a voice-powered ordering system that needs to understand complex commands.

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Custom text classification

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Key phrase extraction

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Question answering

Question 13

A hospital wants to automatically anonymize patient medical records by removing all personally identifiable information (PII) such as names, dates, and social security numbers from unstructured text notes. Which Azure AI Language feature should they use?

Accepted Answer

Named entity recognition (NER). Named entity recognition (NER) is the correct Azure AI Language feature because it is specifically designed to identify and categorize entities in unstructured text, including PII categories such as names, dates, and social security numbers. The hospital can use NER's pre-built PII detection model to automatically locate and redact these sensitive elements from patient notes, meeting their anonymization requirement.

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Key phrase extraction

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Sentiment analysis

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Language detection

Question 14

A university wants to build a chatbot that can answer questions about its admission procedures. The chatbot should retrieve answers directly from a set of official PDF documents containing policies and FAQs. Which Azure AI Language feature should they use to implement this?

Accepted Answer

Custom question answering. Custom question answering (C) is the correct choice because it allows the university to ingest official PDF documents and create a knowledge base of question-answer pairs. The chatbot can then retrieve answers directly from this curated content, making it ideal for domain-specific, document-based Q&A scenarios like admission procedures.

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Sentiment analysis

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Key phrase extraction

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Language detection

Question 15

A legal department needs to automatically extract specific entities from contracts, such as 'Effective Date', 'Governing Law', and 'Payment Terms'. They have 500 manually labeled contract clauses that specify which text spans correspond to each entity. Which Azure AI Language feature should they use to build this custom entity extraction solution?

Accepted Answer

Custom Named Entity Recognition (Custom NER). Custom Named Entity Recognition (Custom NER) is the correct choice because it allows you to train a model on your own labeled data (the 500 manually labeled contract clauses) to extract domain-specific entities like 'Effective Date', 'Governing Law', and 'Payment Terms' that are not covered by prebuilt models. This feature uses a custom trained model to identify and classify text spans according to your defined schema.

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Prebuilt Named Entity Recognition (NER)

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Key phrase extraction

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Custom text classification