AI-900 Describe features of Natural Language Processing workloads on Azure Practice Test 4 — 15 Questions

Question 1

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?

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

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 2

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 3

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 4

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 5

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 6

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 7

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

Question 8

A customer support team receives hundreds of long product reviews every day. They want to automatically summarize each review into a few key sentences to quickly understand the main points. Which prebuilt Azure AI Language feature should they use?

Accepted Answer

Extractive summarization. Extractive summarization is the correct choice because it is specifically designed to condense long documents into a few key sentences by extracting the most important sentences directly from the original text. This aligns perfectly with the customer support team's goal of automatically summarizing hundreds of product reviews into concise, key points for quick understanding.

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Key phrase extraction

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Sentiment analysis

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Entity recognition

Question 9

A customer support team wants to automatically analyze customer emails to determine if the sentiment is positive, negative, or neutral. Which Azure service should they use?

Accepted Answer

Text Analytics. The Text Analytics service (part of Azure Cognitive Services) provides pre-built sentiment analysis, which can classify text as positive, negative, or neutral. This directly matches the requirement to automatically analyze customer emails for sentiment without needing to build custom machine learning models.

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Speech

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Translator

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QnA Maker

Question 10

A legal firm needs to process thousands of contracts to automatically identify important terms such as dates, monetary amounts, names of parties, and legal citations. Which built-in feature of the Azure AI Language service is best suited for this task?

Accepted Answer

C) Entity Recognition. Entity Recognition (also called Named Entity Recognition, NER) is the correct choice because it is specifically designed to identify and categorize predefined entities such as dates, monetary amounts, person names, organizations, and legal citations from unstructured text. The Azure AI Language service's NER capability can automatically extract these important terms from thousands of contracts, making it the ideal built-in feature for this task.

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A) Sentiment Analysis

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B) Key Phrase Extraction

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D) Language Detection

Question 11

A customer service organization has thousands of support tickets labeled with predefined categories such as 'Billing', 'Technical', and 'Account Management'. They want to build a solution that automatically assigns a category to new, incoming tickets. The categories are fixed and known in advance. Which Azure AI Language service feature should they use?

Accepted Answer

Custom Text Classification. Custom Text Classification (B) is correct because the organization has a fixed set of predefined categories and needs to classify new support tickets into those categories. This feature allows you to train a custom model using labeled examples of 'Billing', 'Technical', and 'Account Management' tickets, enabling automatic assignment of incoming tickets to the correct category.

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Prebuilt Text Analytics

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Language Understanding (LUIS)

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Translator

Question 12

A global social media platform wants to automatically detect the language of user posts to route them to appropriate content moderators. The posts are short and often contain mixed scripts. 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 primary language of text, including short and mixed-script content. This allows the platform to automatically route posts to moderators who speak the detected language, directly addressing the requirement.

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Sentiment analysis

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Key phrase extraction

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Entity recognition

Question 13

A market research company wants to analyze thousands of product reviews to identify the most frequently talked-about topics (such as 'battery life', 'screen quality', 'customer support') to guide product improvements. Which Azure AI Language feature is best suited for this task?

Accepted Answer

Key phrase extraction. Key phrase extraction is designed to identify the main points or topics in a body of text, making it the ideal choice for surfacing frequently mentioned themes like 'battery life' or 'screen quality' from thousands of product reviews. It returns a list of key phrases that represent the most salient concepts, directly supporting the goal of guiding product improvements based on customer feedback.

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Sentiment analysis

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Entity recognition

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Language detection

Question 14

A multinational company needs to automatically translate customer support emails from English to Spanish and French. The emails are plain text. Which Azure service should they use?

Accepted Answer

Azure AI Translator. Azure AI Translator is the correct service because it provides real-time text translation between multiple languages, including English to Spanish and French, via a REST API. It is specifically designed for plain text translation tasks, making it ideal for automatically translating customer support emails without requiring audio processing or conversational AI.

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Azure AI Language (Sentiment Analysis)

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Azure AI Speech

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Azure Bot Service

Question 15

A legal firm needs to analyze thousands of contracts to extract key information such as party names, dates, and monetary amounts. They also need to identify if certain clauses (e.g., non-compete, termination rights) are present. Which combination of Azure AI Language features should they use?

Accepted Answer

Named Entity Recognition and Key Phrase Extraction. Named Entity Recognition (NER) extracts predefined entities such as person names, dates, and monetary amounts from text, which directly addresses the need to identify party names, dates, and monetary amounts in contracts. Key Phrase Extraction identifies the main points or topics in a document, making it suitable for detecting the presence of specific clauses like non-compete or termination rights by surfacing the key phrases that represent those clauses.

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Entity Linking and Language Detection

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Sentiment Analysis and Key Phrase Extraction

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Text Analytics for Health and Entity Recognition