AI-900 Describe features of Natural Language Processing workloads on Azure Practice Test 11 — 15 Questions

Question 1

What is 'coreference resolution' in natural language processing?

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Identifying which words or phrases in a text refer to the same real-world entity. Coreference resolution is the NLP task of identifying when two or more expressions in a text refer to the same real-world entity. For example, in 'Alice said she would come,' the pronoun 'she' corefers to 'Alice.' This is fundamental for tasks like document summarization and question answering, where maintaining entity consistency is critical.

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Checking whether a document's references (citations) are correctly formatted

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Resolving conflicts when multiple languages are mixed in the same document

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Matching database foreign keys to their referenced primary keys

Question 2

What is 'language detection' in Azure AI Language?

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Identifying the natural human language of input text with a confidence score. Language detection in Azure AI Language identifies the natural human language of input text and returns a confidence score indicating the likelihood of the detected language being correct. This is a core capability of the Language service, using pre-trained machine learning models to classify text into over 100 languages without requiring any training data from the user.

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Automatically identifying which programming language a code snippet is written in

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Checking whether text contains any language that violates community guidelines

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Translating text from any language into the user's preferred language

Question 3

What is 'conversational language understanding' (CLU) in Azure AI Language?

Accepted Answer

A model that extracts user intent and entities from conversational text to drive chatbot logic. Conversational language understanding (CLU) is a feature of Azure AI Language that enables you to build custom models to extract user intents (e.g., 'BookFlight') and entities (e.g., 'destination city') from natural language utterances. This extracted information drives the logic of a chatbot or virtual assistant, allowing it to determine what action to take. Option B correctly describes this core purpose.

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A chatbot that understands multiple languages and auto-translates responses

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A service that generates conversation transcripts from audio recordings

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A tool for analysing the sentiment of customer conversations in real time

Question 4

What is 'question answering' in Azure AI Language and what are its two main types?

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Custom QA (trained on your documents) and prebuilt QA (document provided at query time). Azure AI Language's 'question answering' feature provides two distinct capabilities: Custom QA, where you train a model on your own documents (e.g., PDFs, FAQs) to answer questions from that knowledge base, and Prebuilt QA, which uses a document provided at query time to extract answers without prior training. This distinction is fundamental to how the service is deployed—either as a persistent, trained knowledge base or as an on-the-fly extraction from a user-supplied document.

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Multiple-choice question generation and open-ended answer scoring

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Structured QA for databases and unstructured QA for text documents

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Real-time QA for chatbots and batch QA for scheduled document processing

Question 5

What is 'key phrase extraction' in Azure AI Language?

Accepted Answer

Identifying the most important words and phrases that best represent a text's main topics. Key phrase extraction in Azure AI Language uses natural language processing to identify the most salient words and phrases that summarize the main topics of a text. It analyzes the document's structure and semantics to return a ranked list of key phrases, enabling quick understanding of core content without reading the entire text.

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Encrypting sensitive phrases in a document for secure storage

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Finding and extracting password-like phrases from user messages for security monitoring

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Selecting the highest-scoring responses from a list of candidate answers

Question 6

What is 'orchestration workflow' in Azure AI Language for multi-skill scenarios?

Accepted Answer

A single entry point that routes queries to the appropriate language skill based on intent. In Azure AI Language, the orchestration workflow is a feature that allows you to connect multiple language skills (such as custom question answering, conversational language understanding, and LUIS) into a single endpoint. It acts as a router that analyzes the user's query intent and then directs the request to the most appropriate skill for processing, enabling multi-skill scenarios without requiring separate endpoints or manual routing logic.

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Scheduling language model inference jobs to run at off-peak hours

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Automating the data labelling workflow for custom NLP model training

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Managing the sequence of preprocessing steps applied to text before model inference

Question 7

What is 'word sense disambiguation' (WSD) and why is it challenging for NLP?

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Determining which meaning of an ambiguous word is intended based on surrounding context. Word sense disambiguation (WSD) is the NLP task of identifying which specific meaning of a polysemous word (a word with multiple meanings) is intended in a given context, using surrounding words, syntax, and semantic cues. This is challenging because many words have multiple, often unrelated meanings (e.g., 'bank' as a financial institution vs. river bank), and the correct sense depends on subtle contextual signals that are difficult for models to capture without deep understanding of the domain or world knowledge.

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Correcting spelling mistakes caused by homophones (words that sound the same)

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Translating words from one language to their exact equivalent in another language

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Measuring how many distinct meanings a word has across a dictionary

Question 8

What is 'text-to-speech' (TTS) in Azure AI Speech?

Accepted Answer

Converting written text into synthesised spoken audio. Text-to-speech (TTS) in Azure AI Speech converts written text into natural-sounding synthesized spoken audio. It uses deep neural networks to generate human-like speech from input text, enabling applications like voice assistants and audiobook narration.

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Extracting text from speech audio recordings

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Translating spoken text from one language to another in real time

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Detecting the emotional tone of speech audio to classify speaker sentiment

Question 9

What is 'abstractive summarisation' and how does it differ from 'extractive summarisation'?

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Extractive selects key sentences verbatim; abstractive generates new sentences capturing the meaning. Extractive summarisation works by selecting and concatenating the most important sentences directly from the source text, while abstractive summarisation uses natural language generation (NLG) models to produce entirely new sentences that paraphrase and condense the core meaning. This distinction is fundamental in Azure AI Language's summarisation capabilities, where extractive returns verbatim excerpts and abstractive generates novel, coherent summaries.

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Extractive writes shorter summaries; abstractive writes longer ones

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Abstractive summarisation is only available for non-English languages

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Extractive summarisation uses generative AI; abstractive uses keyword ranking

Question 10

What is 'sentiment analysis' at the opinion mining level vs. document level in Azure AI Language?

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Document-level assigns one overall sentiment; opinion mining extracts per-aspect sentiments. In Azure AI Language, document-level sentiment analysis assigns a single overall sentiment (positive, negative, neutral, or mixed) to the entire document, while opinion mining (a feature of aspect-based sentiment analysis) extracts sentiments for specific aspects or targets within the text, such as 'service' or 'food' in a restaurant review. This allows for granular, per-aspect sentiment detection rather than a single aggregate score.

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Document-level is more accurate; opinion mining is a faster but less precise approximation

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Opinion mining works only on social media posts; document-level works on all text types

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Document-level sentiment requires training data; opinion mining is pre-built

Question 11

What is 'speaker diarisation' in Azure AI Speech and when is it used?

Accepted Answer

Identifying and labelling which speaker said which portions of a multi-speaker audio recording. Speaker diarization is an Azure AI Speech feature that segments an audio recording by speaker identity, labeling each segment with a unique speaker tag (e.g., Speaker 1, Speaker 2). It is used in scenarios like meeting transcription, call center analytics, or any multi-speaker audio where distinguishing who spoke when is required. This directly matches option B's description of identifying and labeling which speaker said which portions of a multi-speaker recording.

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Translating spoken audio into the dialect of the speaker's home region

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Detecting when a speaker is lying based on vocal stress patterns

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Counting how many unique speakers have interacted with an AI voice assistant over time

Question 12

What is 'real-time speech translation' in Azure AI Speech?

Accepted Answer

Converting spoken words in one language to text or speech in another language instantly. Real-time speech translation in Azure AI Speech is designed to translate spoken language into another language with minimal latency, enabling live conversations. Option B correctly describes this capability, as it converts spoken words in one language to text or speech in another language instantly, leveraging the Speech Translation API with streaming audio input.

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Translating pre-recorded audio files overnight in a batch processing job

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Generating subtitles for pre-existing videos stored in Azure Media Services

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Converting text written in one language into spoken audio in the same language

Question 13

What is 'token budget' and 'context window' management in large language models?

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The maximum text the model can process in one call, requiring strategic management of what fits in context. 'token budget' and 'context window' refer to the maximum number of tokens (words, subwords, or characters) a large language model can process in a single inference call. The context window is a fixed limit (e.g., 4096 tokens for GPT-3.5, 8192 for GPT-4), and managing the token budget involves strategically truncating, summarizing, or prioritizing input text to fit within this window, ensuring the model can generate coherent and relevant responses without exceeding its capacity.

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The monthly spending limit set for an Azure OpenAI subscription

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The number of API calls allowed per minute before rate limiting kicks in

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A pre-purchase of tokens at a discounted rate for high-volume Azure OpenAI users

Question 14

What is 'custom speech' in Azure AI Speech and when would you use it?

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Fine-tuning speech recognition for domain-specific vocabulary, accents, or noisy environments. Custom speech in Azure AI Speech allows you to fine-tune the speech recognition model to better understand domain-specific vocabulary (e.g., medical or legal terms), unique accents, or noisy environments. By providing audio data and transcription text, you train the model to improve accuracy for your specific use case, which is not achievable with the base recognition model.

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Creating a custom voice persona that sounds different from the standard Azure voices

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Configuring speech recognition to only accept voice commands from authorised users

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Building a custom programming language for writing speech processing scripts

Question 15

What is 'intent recognition' in the context of Azure AI Language and conversational AI?

Accepted Answer

Determining the user's goal or purpose from their natural language input to route conversation logic. Intent recognition in Azure AI Language and conversational AI is the process of mapping a user's natural language input to a specific goal or purpose, such as 'book a flight' or 'check weather'. This allows the system to route the conversation logic to the appropriate handler or dialog flow. Option B correctly defines this core function, distinguishing it from simpler pattern matching or sentiment analysis.

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Recognising when a user intends to cancel their subscription during a chat session

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Detecting the emotional intention behind a user's message for sentiment classification

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Verifying that the user's stated intent matches their historical behaviour in the application