AI-900 Practice Question: Describe Artificial Intelligence workloads and considerations
What is 'natural language processing' (NLP) as a category of AI workload?
⚠ Common exam trap
A common mix-up: candidates confuse a specific NLP application (like speech synthesis or translation) with the entire NLP workload category, leading them to select option D instead of the broader, correct definition in option A.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Using AI to process and understand text and speech in human languages
Natural language processing (NLP) is an AI workload that focuses on enabling computers to interpret, understand, and generate human language in both text and speech forms. It combines computational linguistics with statistical machine learning models to perform tasks like sentiment analysis, language translation, and speech recognition. This makes option A the correct definition.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Using AI to process and understand text and speech in human languages
Why this is correct
This is the core definition of natural language processing. NLP combines computational linguistics with machine learning to allow computers to analyze, interpret, and even generate human language, covering both written text and spoken audio. It powers real-world tools like chatbots, real-time translation, and sentiment analysis, making this the correct answer.
- ✗
Programming computers using natural spoken language instead of code
Why it's wrong here
This confuses NLP with natural language programming or instruction, where humans direct computers via speech. NLP is not about writing code with language; instead, it is the AI discipline that enables machines to parse, interpret, and generate human language for tasks such as question answering, summarization, and translation. Natural language programming is a potential application built on NLP, but it does not define the field.
- ✗
A network protocol for low-latency language model inference
Why it's wrong here
NLP is not a communication protocol or network infrastructure; it is a field of artificial intelligence focused on human language. While serving large language models may involve protocols to achieve low-latency inference, that is an engineering concern unrelated to the definition of NLP. Confusing NLP with network protocols mixes up the AI discipline with the underlying plumbing that might deliver its outputs.
- ✗
Automatically converting speech to a natural-sounding language
Why it's wrong here
This describes text-to-speech (speech synthesis), which is one specific NLP application. However, NLP is a much broader AI discipline that includes understanding, generating, and processing both text and speech in all their forms, from sentiment analysis to machine translation. Singling out speech synthesis mistakes a single use case for the entire field.
Go deeper
Related to this question
Learn chapter
Machine Learning Core Concepts
Key term
Natural language processing
Natural language processing (NLP) is a branch of artificial intelligence that helps computers understand, interpret, and respond to human language in a way that is both meaningful and useful.
Key term
Machine learning
Machine learning is a branch of artificial intelligence where computers learn patterns from data to make decisions or predictions without being explicitly programmed for every task.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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