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

Question 1

A language teacher uses Azure AI Language to automatically analyze hundreds of student essays. The teacher wants to identify the main topics discussed in each essay and also understand the overall sentiment (positive, negative, or neutral) expressed. Which two prebuilt Azure AI Language features should the teacher use together to accomplish this goal?

Accepted Answer

Key phrase extraction and Sentiment analysis. Key phrase extraction identifies the main topics discussed in each essay by extracting salient terms and phrases, while sentiment analysis determines the overall sentiment (positive, negative, or neutral) expressed in the text. Together, these two prebuilt Azure AI Language features directly address the teacher's goal of analyzing both topics and sentiment.

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Entity recognition and Language detection

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Text summarization and Key phrase extraction

Question 2

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 3

A customer service team wants to analyze thousands of support tickets to automatically categorize them into predefined topics like 'billing', 'technical issue', and 'account management'. They have a small set of labeled tickets for each category. Which Azure AI Language feature should they use?

Accepted Answer

C. Option C is correct because 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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A

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B

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D

Question 4

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 5

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 6

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. Option C is correct because 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 7

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 8

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

Question 9

A legal firm needs to automatically extract the names of organizations and monetary values from thousands of legal contracts. They want to use a prebuilt Azure AI Language feature without custom training. Which feature should they use?

Accepted Answer

Named entity recognition. Named entity recognition (NER) is the correct choice because it is specifically designed to identify and categorize entities such as organization names and monetary values from unstructured text. Azure AI Language's prebuilt NER model can extract these entity types without any custom training, making it ideal for processing legal contracts at scale.

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Key phrase extraction

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Sentiment analysis

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Text summarization

Question 10

A multinational company receives customer feedback in multiple languages. They need to automatically determine the language of each piece of feedback before routing it to the appropriate support team. Which prebuilt Azure AI Language feature should they use?

Accepted Answer

Language Detection. The Language Detection feature in Azure AI Language is specifically designed to identify the language of a given text input, returning the language name and a confidence score. This directly matches the requirement to automatically determine the language of customer feedback before routing it to the appropriate support team.

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Sentiment Analysis

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Key Phrase Extraction

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Named Entity Recognition (NER)

Question 11

A developer wants to build a virtual assistant that can understand user intents such as 'Book a flight' or 'Check weather' and extract relevant entities like destination and date. The developer has a small set of labeled example utterances. Which Azure AI Language feature should the developer use?

Accepted Answer

Conversational Language Understanding (CLU). Conversational Language Understanding (CLU) is the correct Azure AI Language feature because it is specifically designed to extract both intents (e.g., 'Book a flight') and entities (e.g., destination, date) from user utterances. The developer has a small set of labeled examples, which CLU can use to train a custom model for intent recognition and entity extraction, making it ideal for building a virtual assistant.

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Custom Question Answering

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Custom Text Classification

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Named Entity Recognition (NER)

Question 12

A customer support team receives thousands of unstructured chat transcripts every day. They want to automatically identify the most common recurring issues (e.g., 'long wait time', 'payment error', 'login problem') without training a custom model. 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 salient topics or 'key phrases' from unstructured text without requiring any custom training. In this scenario, the recurring issues like 'long wait time' or 'payment error' are exactly the type of multi-word, topic-level phrases that key phrase extraction surfaces, making it the ideal prebuilt feature for summarizing common support topics.

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Named entity recognition (NER)

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Sentiment analysis

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Language detection

Question 13

A hotel chain wants to automatically analyze guest reviews to identify the most frequently mentioned aspects of their stay, such as 'cleanliness', 'staff friendliness', or 'location'. They want to use a prebuilt Azure AI Language feature without custom training. Which 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 return the most salient words or phrases from unstructured text, such as 'cleanliness', 'staff friendliness', or 'location'. This feature requires no custom training and directly addresses the hotel chain's need to surface frequently mentioned aspects of guest reviews.

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Sentiment Analysis

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Entity Recognition

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Language Detection

Question 14

A business analyst wants to quickly summarize the main topics discussed in a large collection of customer feedback emails. The analyst needs to identify recurring concepts such as 'product quality', 'shipping delay', and 'customer service'. They want 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 a prebuilt Azure AI Language feature designed to automatically identify and return the main topics, concepts, and recurring themes from unstructured text, such as 'product quality' or 'shipping delay'. Unlike custom-trained models, this feature requires no training data and works out-of-the-box, making it ideal for quickly summarizing large collections of customer feedback emails.

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Named Entity Recognition (NER)

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Language Detection

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Sentiment Analysis

Question 15

A retail company wants to automatically determine whether customer reviews are positive, negative, or neutral. Which prebuilt 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 into positive, negative, or neutral sentiment categories. This prebuilt capability analyzes customer reviews at the document and sentence level, returning a sentiment label and confidence scores, which directly meets the requirement of automatically determining review polarity.

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Key phrase extraction

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Language detection

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Entity recognition