The correct answer is hallucination, which describes when a large language model generates content that sounds plausible but contains factual errors or fabricated information. This occurs because models like those in Azure OpenAI Service predict text based on probabilistic patterns from training data rather than verifying facts against the source input, leading to confident-sounding but incorrect outputs. On the Microsoft Azure AI Fundamentals AI-900 exam, this concept tests your understanding of fundamental AI limitations, often appearing in scenarios where a model produces summaries or answers that contradict provided documents. A common trap is confusing hallucination with bias or noise—hallucination specifically involves confident falsehoods, not random errors. To remember, think of a “confident liar”: the model doesn’t know it’s wrong, it just generates what seems statistically likely.
AI-900 Practice Question: Describe features of generative AI workloads on Azure
This AI-900 practice question tests your understanding of describe features of generative ai workloads on azure. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A company uses Azure OpenAI Service to generate summaries of long technical documents. They notice that the model sometimes produces summaries that sound plausible but contain factual errors contradicting the source document. Which concept describes this type of error in large language models?
Why wrong: Overfitting occurs when a model learns training data too well, including noise, and performs poorly on new data. This is not specific to generative AI producing confident falsehoods.
B
Hallucination
Hallucination is the term for a model generating factually incorrect but seemingly plausible content, a common risk in large language models like those used in Azure OpenAI.
C
Tokenization
Why wrong: Tokenization is the process of breaking text into tokens (words or subwords) for model input. It does not directly cause factual errors in output.
D
Bias
Why wrong: Bias refers to systematic unfairness or prejudice in model outputs. While bias can cause inaccuracies, it is not the term for generating false but plausible information.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Hallucination
Option B is correct because hallucination in large language models refers to the generation of content that is factually incorrect or nonsensical but presented with confidence. In this scenario, the model produces summaries that sound plausible yet contain factual errors contradicting the source document, which is the hallmark of hallucination. This occurs because the model generates text based on probabilistic patterns rather than verifying facts against the input.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✗
Overfitting
Why it's wrong here
Overfitting occurs when a model learns training data too well, including noise, and performs poorly on new data. This is not specific to generative AI producing confident falsehoods.
✓
Hallucination
Why this is correct
Hallucination is the term for a model generating factually incorrect but seemingly plausible content, a common risk in large language models like those used in Azure OpenAI.
Related concept
Read the scenario before looking for a memorised answer.
✗
Tokenization
Why it's wrong here
Tokenization is the process of breaking text into tokens (words or subwords) for model input. It does not directly cause factual errors in output.
✗
Bias
Why it's wrong here
Bias refers to systematic unfairness or prejudice in model outputs. While bias can cause inaccuracies, it is not the term for generating false but plausible information.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse hallucination with bias or overfitting, not realizing that hallucination specifically describes the generation of confident but false information, while bias relates to systematic prejudice and overfitting to memorization of training data.
Trap categories for this question
Command / output trap
Tokenization is the process of breaking text into tokens (words or subwords) for model input. It does not directly cause factual errors in output.
Detailed technical explanation
How to think about this question
Hallucination in large language models like GPT-4 arises from the autoregressive nature of text generation, where each token is predicted based on previous tokens without a grounding mechanism to verify against source material. This is exacerbated by the model's tendency to prioritize fluency and coherence over factual accuracy, especially when the input document contains ambiguous or contradictory information. In real-world applications, such as legal or medical document summarization, hallucinations can lead to serious compliance risks, prompting techniques like retrieval-augmented generation (RAG) to anchor outputs in verified data.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this AI-900 question in full detail.
Describe features of generative AI workloads on Azure — This question tests Describe features of generative AI workloads on Azure — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Hallucination — Option B is correct because hallucination in large language models refers to the generation of content that is factually incorrect or nonsensical but presented with confidence. In this scenario, the model produces summaries that sound plausible yet contain factual errors contradicting the source document, which is the hallmark of hallucination. This occurs because the model generates text based on probabilistic patterns rather than verifying facts against the input.
What should I do if I get this AI-900 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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