AI-900 Describe features of Natural Language Processing workloads on Azure Practice Test 7 — 15 Questions

Question 1

A research team wants to automatically extract the most important phrases from a large collection of scientific articles to identify emerging trends. The articles are all in English. They do not want to train a custom model. Which built-in 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 returns the most important phrases in a document, which directly supports the goal of extracting key terms from scientific articles to spot emerging trends. This built-in capability requires no custom model training and works out-of-the-box for English text, making it ideal for the research team's use case.

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Sentiment analysis

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Named entity recognition (NER)

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Language detection

Question 2

A global company receives customer support tickets in over 60 languages. They need to automatically detect the language of each ticket so it can be routed to the appropriate language-specific team. The company has no labeled training data for language identification. Which Azure AI Language feature should they use?

Accepted Answer

Language Detection. Language Detection is the correct choice because it is a pre-built, zero-shot Azure AI Language feature that can automatically identify the language of text without requiring any labeled training data. The service uses a multilingual model trained on large datasets to detect over 100 languages, making it ideal for routing support tickets in over 60 languages with no prior customization.

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Custom Text Classification

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Key Phrase Extraction

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Entity Recognition

Question 3

A hotel chain receives thousands of online guest reviews each month. The management wants to automatically determine whether the overall feedback for each review is positive, negative, or neutral to identify areas for improvement. Which Azure AI Language feature should they use?

Accepted Answer

Sentiment analysis. Sentiment analysis is the correct Azure AI Language feature because it evaluates text to determine the overall sentiment—positive, negative, or neutral—at the document or sentence level. This directly matches the hotel chain's requirement to classify each review's feedback automatically, enabling them to identify areas for improvement based on sentiment trends.

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Key phrase extraction

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Named entity recognition

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Language detection

Question 4

A law firm wants to automatically categorize incoming legal documents into custom categories such as 'Motion', 'Contract', 'Discovery', and 'Memorandum'. The firm has a set of manually labeled documents that can be used to train the system. Which Azure AI Language feature should they use?

Accepted Answer

Custom text classification. The law firm needs to categorize documents into custom categories using their own labeled data. Custom text classification in Azure AI Language is specifically designed for this purpose, allowing you to train a model on your own labeled documents to classify text into user-defined categories. Prebuilt Text Analytics for sentiment only detects sentiment (positive/negative/neutral), not custom categories.

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Prebuilt Text Analytics for sentiment

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Conversational Language Understanding

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Key phrase extraction

Question 5

A news agency publishes hundreds of articles daily. They want to automatically determine the main topics discussed in each article, such as 'politics', 'economy', or 'sports', without manually tagging them. The agency has no labeled training data. Which built-in 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 or themes in a document without requiring any labeled training data. The news agency can use this built-in Azure AI Language feature to extract key phrases like 'politics', 'economy', or 'sports' from each article, enabling automatic topic categorization without manual tagging.

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Sentiment analysis

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Named entity recognition

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Language detection

Question 6

A customer service department wants to automatically extract the names of products mentioned in customer emails and the sentiment expressed about each product. For example, from the sentence 'The battery life of the X100 is excellent, but the screen is too dark,' they need to identify 'X100' and associate 'positive' sentiment with 'battery life' and 'negative' sentiment with 'screen'. Which Azure AI Language feature should they use?

Accepted Answer

Sentiment analysis with opinion mining. Option D is correct because sentiment analysis with opinion mining is specifically designed to extract both the sentiment (positive, negative, neutral) and the associated target (e.g., 'battery life', 'screen') from text. This feature goes beyond simple sentiment scoring by identifying the opinion target and the sentiment expressed toward it, which directly matches the requirement to extract product names and their associated sentiments from customer emails.

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Custom text classification

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Key phrase extraction

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Entity linking

Question 7

A company wants to build a customer service chatbot that can understand user intents (e.g., 'cancel order', 'track shipment') and extract relevant entities (e.g., order number, product name). Which Azure AI Language feature should they use?

Accepted Answer

Conversational Language Understanding (CLU). Conversational Language Understanding (CLU) is the correct Azure AI Language feature because it is specifically designed to understand user intents (e.g., 'cancel order') and extract relevant entities (e.g., order number) from natural language input. This makes it ideal for building a customer service chatbot that needs to interpret and act on user requests.

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Key phrase extraction

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Sentiment analysis

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Named entity recognition (NER)

Question 8

A hotel chain wants to automatically determine whether online guest reviews express a positive, negative, or neutral opinion about their stays. Which built-in Azure AI Language feature should they use?

Accepted Answer

Sentiment analysis. Sentiment analysis is the correct Azure AI Language feature because it is specifically designed to classify text as positive, negative, or neutral, which directly matches the hotel chain's requirement to determine guest opinions from online reviews. This feature uses machine learning models to evaluate the overall sentiment expressed in a document or sentence, providing a confidence score for each sentiment category.

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Named Entity Recognition (NER)

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Key phrase extraction

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Language detection

Question 9

A legal firm needs to automatically extract custom entities such as 'Party Name', 'Effective Date', and 'Governing Law' from contracts. They have a set of manually labeled contracts to use as training data. Which Azure AI Language feature should they use?

Accepted Answer

Custom Named Entity Recognition (Custom NER). Custom Named Entity Recognition (Custom NER) is the correct choice because it allows the legal firm to train a model on their manually labeled contracts to extract domain-specific entities like 'Party Name', 'Effective Date', and 'Governing Law'. Unlike built-in NER, which only recognizes predefined entity types, Custom NER learns from the provided labeled data to identify custom categories tailored to the firm's needs.

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Key Phrase Extraction

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Entity Linking

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Built-in Named Entity Recognition

Question 10

A news agency receives thousands of articles daily from wire services. They want to automatically identify and tag mentions of people, organizations, and locations within each article to improve search and categorization. The agency has no labeled training data. Which built-in Azure AI Language feature should they use?

Accepted Answer

Named Entity Recognition (NER). Named Entity Recognition (NER) is the correct choice because it is specifically designed to identify and categorize mentions of people, organizations, locations, and other entity types in unstructured text. Since the agency has no labeled training data, NER's pre-trained model can be used out-of-the-box without any custom training, making it ideal for automatically tagging articles with these entity types to improve search and categorization.

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Sentiment Analysis

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Key Phrase Extraction

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Language Detection

Question 11

Which Azure AI service can detect the language of a text input and return the identified language name and confidence score?

Accepted Answer

Azure AI Language (language detection). Azure AI Language's language detection feature is specifically designed to identify the language of a text input, returning both the language name and a confidence score between 0 and 1. This capability is part of the Natural Language Processing (NLP) workloads within Azure AI Language, making option B the correct choice for this task.

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Azure AI Vision

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Azure AI Translator

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Azure AI Document Intelligence

Question 12

What does sentiment analysis do?

Accepted Answer

Determines the emotional tone (positive, negative, neutral) expressed in text. Sentiment analysis is a natural language processing (NLP) technique that evaluates text to determine the emotional tone expressed, typically classifying it as positive, negative, or neutral. In Azure Cognitive Services, this is performed by the Text Analytics API, which uses machine learning models to assign sentiment scores and labels based on the overall context of the input text. Option B is correct because it directly describes this core function of detecting emotional polarity.

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Translates text from one language to another

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Extracts key phrases and named entities from text

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Converts spoken words into written text

Question 13

Which Azure AI service can identify and extract named entities (people, organizations, locations, dates) from text?

Accepted Answer

Azure AI Language (Named Entity Recognition). Azure AI Language's Named Entity Recognition (NER) capability is specifically designed to identify and categorize named entities such as people, organizations, locations, and dates from unstructured text. This is a core feature of the Natural Language Processing (NLP) workload within Azure AI Language, making option B the correct choice.

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Azure AI Vision

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Azure AI Translator

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Azure AI Speech

Question 14

Which Azure AI service extracts key information (like invoice numbers, dates, and amounts) from structured documents like forms and invoices?

Accepted Answer

Azure AI Document Intelligence. Azure AI Document Intelligence (formerly Form Recognizer) is the correct service because it is specifically designed to extract structured data such as invoice numbers, dates, and amounts from forms and invoices. It uses prebuilt models for invoices and receipts, along with custom model training, to perform optical character recognition (OCR) and key-value pair extraction from structured documents.

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Azure AI Language

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Azure AI Vision

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Azure AI Translator

Question 15

What does Azure AI Speech service's text-to-speech (TTS) feature do?

Accepted Answer

Converts written text into natural-sounding spoken audio. Azure AI Speech service's text-to-speech (TTS) feature converts written text into natural-sounding spoken audio using neural voice models. It synthesizes speech from input text, enabling applications like voice assistants, audiobooks, and accessibility tools. This is the core function of TTS, distinct from speech-to-text or translation capabilities.

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Converts spoken audio into written text

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Identifies the language of spoken audio

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Translates spoken words from one language to another