AI-900 Describe features of Natural Language Processing workloads on Azure Practice Test 3 — 15 Questions

Question 1

A customer service team wants to analyze thousands of call transcripts to identify common complaints and understand whether customer sentiment is positive, negative, or neutral. They plan to use prebuilt Azure AI Language features without any custom training. Which combination of features should they use?

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

Accepted Answer

Key phrase extraction and sentiment analysis. The customer service team needs to identify common complaints (which requires extracting key phrases from the call transcripts) and understand sentiment polarity (positive, negative, or neutral). Azure AI Language's prebuilt key phrase extraction returns the main talking points and recurring terms, while sentiment analysis assigns a sentiment score and labels per sentence or document. Both features are available out-of-the-box without any custom training, directly meeting the stated requirements.

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Entity recognition and text translation

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Language detection and summarization

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PII detection and conversation analysis

Question 2

A law firm needs to automatically extract specific information from legal contracts, such as the names of the parties involved, effective dates, and governing law clauses. The firm has a small set of contracts that have been manually annotated with these specific fields. Which Azure AI Language feature should they use to build a custom extraction solution?

Accepted Answer

Custom Named Entity Recognition (NER). Custom Named Entity Recognition (NER) is the correct choice because the law firm needs to extract specific, custom fields (party names, effective dates, governing law clauses) from legal contracts, which are not covered by prebuilt entity categories. Custom NER allows you to train a model using a small set of manually annotated contracts to recognize these domain-specific entities, enabling tailored extraction for the firm's unique requirements.

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Prebuilt Named Entity Recognition (NER)

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Key Phrase Extraction

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Text Summarization

Question 3

A legal firm needs to automatically process thousands of court documents. The system must identify and redact sensitive personal information such as names, addresses, and social security numbers. Additionally, it must extract legal-specific entities like case numbers, judge names, and statute references. The firm has a small set of manually annotated documents with these legal entities. Which combination of Azure AI Language features should they use?

Accepted Answer

PII detection and custom named entity recognition (NER). The firm needs both PII detection to redact sensitive personal information and custom NER to extract legal-specific entities like case numbers and judge names from a small set of annotated documents. Azure AI Language provides a prebuilt PII detection feature for common sensitive data and a custom NER capability that can be trained on the firm's annotated documents to recognize domain-specific entities.

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Prebuilt entity recognition and key phrase extraction

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Sentiment analysis and language detection

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PII detection only

Question 4

A retail company wants to automatically analyze thousands of product reviews to identify the most frequently mentioned aspects, such as 'battery life', 'screen quality', and 'customer service'. They plan to use a prebuilt Azure AI Language feature without any custom training. Which feature should they use?

Accepted Answer

Key phrase extraction. Key phrase extraction is the correct choice because it is specifically designed to identify and extract the most important words or phrases from unstructured text, such as product reviews. This prebuilt Azure AI Language feature requires no custom training and directly surfaces frequently mentioned aspects like 'battery life' or 'screen quality' by analyzing term frequency and relevance.

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Text Analytics for Health

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Entity linking

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Sentiment analysis

Question 5

A customer support team wants to analyze chat logs to automatically identify the most common reasons for customer complaints and track how customer sentiment changes throughout a conversation. They plan to use prebuilt Azure AI Language features without any custom training. Which combination of features should they use?

Accepted Answer

Key phrase extraction and sentiment analysis. Key phrase extraction identifies the most common reasons for complaints by pulling out important terms from the chat logs, while sentiment analysis tracks how customer sentiment changes throughout a conversation by assigning positive, negative, or neutral scores per utterance. Both are prebuilt Azure AI Language features that require no custom training, making them the correct combination for this scenario.

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Entity recognition and language detection

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Text summarization and question answering

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Conversational language understanding and personal identification

Question 6

A customer insights team at a retail company has collected thousands of product reviews. They want to automatically extract the most frequently mentioned topics or aspects from these reviews, such as 'price', 'durability', and 'customer service', without any custom training. Which prebuilt Azure AI Language feature should they use?

Accepted Answer

Key Phrase Extraction. Key Phrase Extraction is the correct choice because it is a prebuilt Azure AI Language feature designed to automatically identify and extract the main points or topics from unstructured text, such as product reviews, without requiring any custom training or labeled data. It surfaces frequently mentioned aspects like 'price', 'durability', and 'customer service' by analyzing linguistic patterns and term frequency, making it ideal for this summarization task.

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Text Analytics for Health

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Conversational Language Understanding

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Entity Linking

Question 7

A global news organization receives articles in multiple languages. They need to first identify the language of each article, then translate it into English. Which combination of prebuilt Azure AI services should they use?

Accepted Answer

Language Detection (Azure AI Language) and Translator (Azure AI Translator). The scenario requires two distinct steps: first, identifying the language of each article, which is performed by the Language Detection API in Azure AI Language; second, translating the article into English, which is handled by the Azure AI Translator service. These two prebuilt services are designed to work together seamlessly for multilingual content processing.

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Key Phrase Extraction and Sentiment Analysis

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Entity Recognition and Translator

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Language Detection and Text Analytics for Health

Question 8

A marketing team wants to automatically analyze thousands of customer reviews to identify the most commonly discussed aspects, such as 'price', 'durability', or 'customer service'. They do not have any labeled data for custom training. Which prebuilt Azure AI Language feature should they use?

Accepted Answer

Key phrase extraction. Key phrase extraction is the correct choice because it automatically identifies the most important points or topics (like 'price', 'durability', 'customer service') from unstructured text without requiring any labeled training data. This prebuilt Azure AI Language feature is designed specifically to surface commonly discussed aspects from large volumes of text, making it ideal for analyzing thousands of customer reviews.

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Sentiment analysis

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Entity recognition

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Language detection

Question 9

A customer support team wants to analyze chat transcripts to automatically extract the most frequently mentioned product issues and also determine whether each chat represents a positive, neutral, or negative customer experience. Which prebuilt Azure AI Language feature should they use?

Accepted Answer

A. Text Analytics (prebuilt). The Text Analytics (prebuilt) feature in Azure AI Language provides pre-built capabilities for key phrase extraction (to identify frequently mentioned product issues) and sentiment analysis (to classify chats as positive, neutral, or negative). This matches the customer support team's requirements exactly without needing custom training or complex configuration.

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B. Custom Text Classification

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C. Conversational Language Understanding

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D. Question Answering

Question 10

A customer service team wants to analyze thousands of call transcripts to identify common complaints and understand whether customer sentiment is positive, negative, or neutral. They plan to use prebuilt Azure AI Language features without any custom training. Which combination of features should they use?

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

Accepted Answer

Custom Text Classification. The Custom Text Classification feature of Azure AI Language allows you to train a model using a small set of labeled tickets to automatically categorize text into predefined topics like 'billing', 'technical issue', and 'account management'. This feature is specifically designed for scenarios where you have labeled data and need to classify documents into custom categories, making it ideal for analyzing support tickets.

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Pre-built Text Classification

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Sentiment Analysis

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Key Phrase Extraction

Question 11

A customer support team wants to use Azure AI Language to automatically analyze incoming support emails. They need to extract the product name mentioned in each email and determine whether the customer's sentiment is positive, negative, or neutral. They have no labeled data for custom training. Which two prebuilt Azure AI Language features should they use together?

Accepted Answer

Named entity recognition (NER) and sentiment analysis. Named entity recognition (NER) extracts specific entities like product names from text, while sentiment analysis determines the emotional tone (positive, negative, neutral). Both are prebuilt, no-code features in Azure AI Language that require no labeled data for custom training, making them the correct pair for this use case.

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Key phrase extraction and language detection

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Conversational language understanding (CLU) and translation

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Text summarization and personal identifying information (PII) detection

Question 12

A company wants to build a chatbot that can answer customer questions about their product return policy, shipping times, and warranty information. They have a structured document with these questions and answers. Which Azure AI Language feature should they use to create this chatbot without writing custom code?

Accepted Answer

Custom Question Answering. Custom Question Answering (C) is the correct choice because it is specifically designed to create a chatbot that answers questions based on a structured document (e.g., FAQ, product manual) without writing custom code. It uses a predefined knowledge base of question-answer pairs and provides a built-in orchestration for bot integration, making it ideal for this scenario.

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Conversational Language Understanding (CLU)

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Custom Text Classification

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Language Detection

Question 13

A law firm needs to automatically redact personal identifiable information (PII) such as names, addresses, and social security numbers from thousands of legal documents before making them public. They want to use a prebuilt Azure AI Language feature without custom training. Which feature should they use?

Accepted Answer

Personally Identifiable Information (PII) Detection. PII Detection is a prebuilt Azure AI Language feature specifically designed to identify and redact personal identifiable information such as names, addresses, and social security numbers from text. It requires no custom training, making it ideal for the law firm's use case of automatically redacting PII from legal documents before public release.

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Key Phrase Extraction

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Sentiment Analysis

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Language Detection

Question 14

A legal firm needs to automatically extract specific terms such as contract dates, party names, and monetary amounts from thousands of legal documents. The firm does not have a labeled dataset for custom training but needs to identify only these predefined types of information. Which prebuilt Azure AI Language feature should they use?

Accepted Answer

Named Entity Recognition (NER). Named Entity Recognition (NER) is the correct choice because it is a prebuilt Azure AI Language feature designed to automatically identify and categorize predefined entities such as dates, person names, and monetary amounts from text. Since the firm needs to extract specific types of information (contract dates, party names, monetary amounts) without a labeled dataset, NER's out-of-the-box models can directly recognize these common entity categories without any custom training.

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Key phrase extraction

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Sentiment analysis

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Language detection

Question 15

A research team wants to automatically analyze thousands of online news articles to identify the main topics discussed in each article (e.g., 'technology', 'politics', 'sports'). They need a prebuilt Azure AI Language feature that returns a list of key concepts or topics without any custom training. Which 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 a list of the main topics, concepts, or themes discussed in a document without requiring any custom training or labeled data. This prebuilt capability is designed specifically for extracting high-level topics from unstructured text, making it ideal for analyzing thousands of news articles to determine subjects like 'technology', 'politics', or 'sports'.

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Named Entity Recognition (NER)

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Sentiment analysis

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Language detection