AI-900 Describe features of Natural Language Processing workloads on Azure Practice Test 5 — 15 Questions

Question 1

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?

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

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 2

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 3

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 4

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. 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 5

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 6

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. 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 7

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 scenario requires two objectives: understanding the overall tone (positive/negative) and extracting commonly mentioned features. Sentiment analysis directly provides the tone. Key phrase extraction identifies important phrases like 'room cleanliness'. Therefore, the correct combination is sentiment analysis and key phrase extraction (Option A). Option C (key phrase extraction and entity linking) lacks sentiment analysis, so it does not fully address the requirement. Entity linking helps disambiguate entities but does not capture tone, making Option C incomplete.

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Entity recognition and language detection

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Key phrase extraction and entity linking

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Text summarization and sentiment analysis

Question 8

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. 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 9

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. 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

Question 10

A company wants to build an FAQ bot that can answer questions based on its internal knowledge base. The questions from users are often phrased in different ways. They want to match the user's intent to pre-defined answers without training a custom model. Which Azure AI Language feature should they use?

Accepted Answer

Custom Question Answering. Custom Question Answering (formerly QnA Maker) is the correct choice because it allows you to ingest a knowledge base (e.g., FAQs, manuals) and match user questions to pre-defined answers using a built-in ranking model, without training a custom ML model. It handles varied phrasing through semantic understanding and returns the best answer from the curated content, directly addressing the requirement to match intent to pre-defined answers without custom training.

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Language Understanding (LUIS)

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Translator

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Sentiment Analysis

Question 11

A law firm needs to automatically categorize documents (e.g., 'contract', 'pleading', 'memo') and extract specific clauses such as 'indemnity' and 'confidentiality'. They have a large set of labeled examples for both tasks. Which combination of Azure AI Language features should they use?

Accepted Answer

Custom text classification and custom named entity recognition. The law firm needs to categorize documents (a text classification task) and extract specific clauses (a named entity recognition task). Custom text classification allows training a model on labeled examples to classify documents into categories like 'contract' or 'pleading', while custom named entity recognition (NER) can be trained to extract domain-specific entities such as 'indemnity' and 'confidentiality' clauses from the text. Azure AI Language supports both custom features, enabling the firm to build tailored models using their labeled dataset.

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Prebuilt sentiment analysis and key phrase extraction

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Question answering and conversation summarization

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Language detection and translation

Question 12

A hospital collects patient feedback forms in text format. They want to automatically identify whether each feedback is positive, negative, or neutral, and also extract specific recurring phrases like 'waiting time' and 'staff attitude'. Which Azure AI Language feature should they use to determine the overall tone of the feedback?

Accepted Answer

C) Sentiment analysis. Sentiment analysis is the correct Azure AI Language feature because it is specifically designed to determine the overall tone (positive, negative, or neutral) of text. The question asks for identifying the tone of feedback, which is exactly what sentiment analysis provides by scoring each document and its sentences for sentiment polarity.

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A) Key phrase extraction

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B) Entity recognition

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D) Language detection

Question 13

A customer service team wants to automatically determine whether each customer feedback message is positive, negative, or neutral. Which 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 sentiments. This directly matches the customer service team's requirement to automatically determine the sentiment of each feedback message. Other features like key phrase extraction or entity recognition do not perform sentiment classification.

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Key phrase extraction

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Language detection

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Entity recognition

Question 14

A law firm needs to automatically process incoming legal documents. They have two specific requirements: (1) extract the names of all parties involved, the court name, and the filing date; (2) categorize each document as a 'complaint', 'motion', or 'subpoena'. Which two Azure AI Language features should they use? (Choose two.)

Accepted Answer

Custom text classification. Custom text classification (C) is correct because it allows the law firm to train a model to categorize legal documents into custom classes like 'complaint', 'motion', or 'subpoena' based on labeled examples. This feature is designed for domain-specific classification tasks where predefined categories are insufficient.

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Sentiment analysis

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Key phrase extraction

Question 15

A legal firm needs to automatically extract key information from contracts, including the names of parties involved, important dates, and monetary amounts. Which Azure AI Language feature should they use to identify and extract these specific pieces of information from the text?

Accepted Answer

Named Entity Recognition (NER). Named Entity Recognition (NER) is the correct Azure AI Language feature because it is specifically designed to identify and categorize entities such as people (parties involved), dates, and monetary amounts from unstructured text. This directly matches the legal firm's requirement to extract key information from contracts.

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Sentiment analysis

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Key phrase extraction

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Language detection