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

Question 1

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 2

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 3

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 4

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 5

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 6

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 7

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 8

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

Question 9

A consumer electronics company collects online reviews about their latest smartphone. They want to identify specific aspects that customers praise or criticize, such as battery life, camera quality, and screen brightness. Which Azure AI Language feature should they use to extract these aspect-based opinions?

Accepted Answer

Sentiment analysis with opinion mining. Option C is correct because sentiment analysis with opinion mining is specifically designed to extract aspect-based opinions from text. In this scenario, the company needs to identify which aspects (e.g., battery life, camera quality) are praised or criticized, which requires both aspect detection and sentiment polarity assignment. Opinion mining extends standard sentiment analysis by linking sentiments to specific targets or aspects within the text.

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Key phrase extraction

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Named entity recognition

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Language detection

Question 10

A law firm needs to process thousands of legal contracts. They want to extract specific clauses (e.g., 'confidentiality', 'indemnity') and also identify the names of parties and monetary amounts mentioned. Which Azure AI Language feature(s) should they use together to achieve both tasks?

Accepted Answer

Custom text classification and named entity recognition. Custom text classification allows the law firm to train a model to identify specific clauses like 'confidentiality' and 'indemnity' by providing labeled examples. Named entity recognition (NER) can then extract predefined entities such as person names (parties) and monetary amounts from the text. Together, these two features address both custom clause detection and standard entity extraction.

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Key phrase extraction and sentiment analysis

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Entity linking and language detection

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Summarization and conversational language understanding

Question 11

A customer support team receives thousands of emails daily. They want to automatically route each email to the appropriate department (Billing, Technical Support, or General Inquiry). They also want to extract the customer's account number and order ID from each email. Which combination of Azure AI Language features should they use?

Accepted Answer

Text classification and custom entity recognition. Option C is correct because the scenario requires two distinct NLP tasks: categorizing emails into predefined departments (Billing, Technical Support, General Inquiry) which is a text classification task, and extracting specific structured data (account number and order ID) which requires custom entity recognition to identify domain-specific entities not covered by prebuilt NER. Azure AI Language provides both custom text classification and custom entity recognition features to handle these requirements.

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Sentiment analysis and key phrase extraction

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Language detection and translation

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Named entity recognition (NER) and summarization

Question 12

A hotel chain wants to analyze thousands of guest reviews to understand the overall tone of feedback (positive or negative) and to extract the most commonly mentioned features (e.g., 'room cleanliness', 'staff friendliness', 'breakfast'). Which two Azure AI Language features should they combine?

Accepted Answer

Sentiment analysis and key phrase extraction. The hotel chain needs to determine the overall tone (positive/negative) of guest reviews and extract commonly mentioned features. Sentiment analysis provides the overall tone by assigning a sentiment score to each review, while key phrase extraction identifies the most salient phrases such as 'room cleanliness' and 'staff friendliness'. Combining these two Azure AI Language features directly addresses both requirements.

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Entity recognition and language detection

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Text summarization and sentiment analysis

Question 13

A global e-commerce company receives customer support emails in over 30 languages. They want to automatically route each email to the correct department (Billing, Technical Support, or Returns) and also extract the order number and the product name mentioned in the email. The solution must be able to identify the language of each email before further processing. Which combination of Azure AI Language features should they use?

Accepted Answer

Language Detection, Custom Text Classification, and Entity Recognition. Option A is correct because the scenario requires three distinct capabilities: Language Detection to identify the email's language (a prerequisite for further processing), Custom Text Classification to route emails to the correct department (Billing, Technical Support, or Returns), and Entity Recognition to extract the order number and product name. Azure AI Language provides these as separate, combinable features that directly map to the stated requirements.

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Language Detection, Key Phrase Extraction, and Sentiment Analysis

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Entity Recognition, Sentiment Analysis, and Key Phrase Extraction

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Custom Text Classification, Key Phrase Extraction, and Sentiment Analysis

Question 14

A customer support team receives emails in multiple languages. They want to automatically determine the language of each email and then extract key phrases to summarize the issue. Which two Azure AI Language features should they use in sequence?

Accepted Answer

Language detection and entity extraction. Option C is correct because the scenario requires first identifying the language of each email (using Language Detection) and then extracting key phrases from the text to summarize the issue (using Key Phrase Extraction). These two features are designed to work sequentially in Azure AI Language, where language detection provides the language code needed for key phrase extraction to operate accurately.

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Sentiment analysis and key phrase extraction

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Entity extraction and sentiment analysis

Question 15

A law firm receives hundreds of legal documents daily. They need to automatically extract key entities like names of parties, dates, jurisdictions, and also classify each document as 'contract', 'pleading', or 'memo'. Which combination of Azure AI Language features should they use?

Accepted Answer

Entity recognition and custom text classification. Option B is correct because the law firm needs both entity extraction (to identify parties, dates, jurisdictions) and document classification (contract, pleading, memo). Azure AI Language's prebuilt entity recognition handles the entity extraction, while custom text classification allows the firm to train a model to classify documents into their specific categories. This combination directly addresses both requirements without unnecessary features.

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Entity recognition and key phrase extraction

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Sentiment analysis and language detection

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Summarization and conversation analysis