AI-900 Practice Question: Describe features of generative AI workloads on Azure
What is a 'hallucination' in the context of large language models?
⚠ Common exam trap
Many exam-takers confuse a model's refusal to answer (safety guardrails) with a hallucination, or think that running out of context window is a type of hallucination, when in fact hallucination is specifically about generating confident but false content.
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
✓
When a model generates plausible-sounding but factually incorrect information
In the context of large language models (LLMs), a hallucination occurs when the model generates text that is fluent, coherent, and plausible-sounding but is factually incorrect or nonsensical. This happens because LLMs are trained to predict the next token based on statistical patterns in their training data, not to verify facts against a ground truth. Option B correctly identifies this behavior.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
When a model refuses to answer a question
Why it's wrong here
Refusing to answer is an intentional safety-guardrail behavior, typically implemented through instruction tuning, RLHF, or classifier-based filters, so the model declines requests that could cause harm. A refusal is an abstinence decision: the model says it cannot or should not comply. Hallucination is the opposite failure — the model complies eagerly but produces plausible misinformation, so it is a truthfulness problem rather than a compliance or safety mechanism.
- ✓
When a model generates plausible-sounding but factually incorrect information
Why this is correct
Hallucination arises because an autoregressive transformer samples the next token from a probability distribution learned over human text, optimizing for linguistic fluency and coherence rather than verifiable truth. Without an external knowledge base or retrieval verification, the model can confidently emit well-formed statements that are entirely fabricated. This is the key reliability limitation of purely statistical language models.
- ✗
When a model processes images instead of text
Why it's wrong here
Processing images instead of text refers to multimodal input handling, such as when a vision-language model accepts an image and produces a caption or answers visual questions. That is a separate capability and not an accuracy failure. Hallucination, by contrast, is a text-generation defect in which the model invents facts while producing natural-sounding language, regardless of whether the input was text or an image.
- ✗
When a model runs out of context window space
Why it's wrong here
Running out of context window space is a hard technical constraint: the model's attention mechanism can only attend to a fixed maximum number of tokens, so longer inputs are truncated or must be summarized. This causes loss of earlier information and degraded performance, but it is not the same failure mode as hallucination. Hallucination is not about forgetting due to an exhausted buffer; it is about generating false content that was never in the input at all.
Go deeper
Related to this question
Learn chapter
Azure AI Language Service
Key term
Token
A token is a digital or physical object that represents a set of permissions or access rights, used to verify identity or grant access in computer systems.
Key term
Training data
Training data is a set of examples used to teach a machine learning model how to make predictions or decisions.
About these practice questions
This AI-900 question is part of Courseiva's 985-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI-900 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI-900 exam.