easyMultiple Choice
AIF-C01 Practice Question: An e-commerce company uses an LLM to generate…
An e-commerce company uses an LLM to generate product descriptions. They observe that occasionally the model outputs factually incorrect information about products. What is the term for this phenomenon?
⚠ Common exam trap
The trap is conflating hallucination with bias or drift — candidates who see 'incorrect information' may pick bias amplification, but the AIF-C01 exam distinguishes factual fabrication (hallucination) from fairness issues (bias) and temporal degradation (drift).
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
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Hallucination
Hallucination is the term for an LLM generating fluent, confident output that is factually incorrect or unsupported by its training data — exactly what happens when the model invents product specifications, prices, or features. It occurs because the model predicts statistically plausible tokens rather than retrieving verified facts. This is a well-known limitation of generative AI and the reason techniques like RAG and grounding are used to reduce it.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
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Bias amplification
Why it's wrong here
Bias amplification describes a model reinforcing skewed patterns from biased training data, not inventing false product facts. It is tempting because biased outputs can look wrong, but the phenomenon here is hallucination — fluent, confident fabrication unsupported by the source data.
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Overfitting
Why it's wrong here
Overfitting is a training-time failure where a model memorises training data and generalises poorly to new inputs; it does not describe an LLM fabricating plausible falsehoods at inference. It is tempting because both produce wrong outputs, but the stem describes hallucination, not poor generalisation.
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Hallucination
Why this is correct
Hallucination describes an LLM generating fluent but factually incorrect output, such as false product details. The model produces plausible text without grounding in verified data, which is precisely the phenomenon observed in the generated descriptions.
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Concept drift
Why it's wrong here
Concept drift is the degradation of model accuracy when the statistical relationship between inputs and outputs changes over time in production. It is tempting because both involve wrong outputs, but the stem describes hallucination — fabricated content — not a shift in the underlying data distribution.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
This AIF-C01 practice question is part of Courseiva's free Amazon Web Services 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 AIF-C01 exam.