The answer is toxic or harmful content generation, along with hallucinations and bias, as the three key risks of deploying generative AI. This is correct because large language models operate on probabilistic pattern matching rather than factual verification, meaning they can produce outputs that sound authoritative but are factually incorrect or nonsensical—a phenomenon known as hallucination. Additionally, models trained on internet-scale data can amplify societal biases present in their training sets, and without robust guardrails, they may generate offensive, dangerous, or misleading content. On the Google Cloud Generative AI Leader exam, this question tests your understanding of the operational risks that distinguish generative AI from traditional deterministic systems, often appearing as a multiple-select item where distractors include “increased model accuracy” or “reduced training costs.” A common trap is assuming that more training data automatically reduces risk, but in reality, it can increase the likelihood of surfacing harmful patterns. Memory tip: think of the three H’s—Hallucinations, Harmful outputs, and Hidden bias—as the core triad of generative AI deployment dangers.
Generative AI Leader Fundamentals of Generative AI Practice Question
This Generative AI Leader practice question tests your understanding of fundamentals of generative ai. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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.
Which THREE of the following are potential risks when deploying generative AI?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Hallucinations
Option A is correct because generative AI models, particularly large language models (LLMs), can produce plausible-sounding but factually incorrect or nonsensical outputs, known as hallucinations. This occurs due to the model's probabilistic nature and lack of true understanding, where it generates text based on learned patterns rather than verified facts.
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.
✓
Hallucinations
Why this is correct
Models can generate false or fabricated information.
Related concept
Read the scenario before looking for a memorised answer.
✗
Memorization of sensitive training data
Why it's wrong here
While a risk, it is less commonly cited than the others; only three correct answers are required.
✓
Bias and fairness issues
Why this is correct
Models may amplify biases present in training data.
Related concept
Read the scenario before looking for a memorised answer.
✗
Increased model accuracy
Why it's wrong here
Increased accuracy is a benefit, not a risk.
✓
Toxic or harmful content generation
Why this is correct
Models can inadvertently produce offensive or dangerous outputs.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the distinction between risks and benefits, so the trap here is that candidates may mistakenly identify 'increased model accuracy' as a risk, when it is actually a performance improvement and not a deployment risk.
Detailed technical explanation
How to think about this question
Hallucinations stem from the model's autoregressive generation process, where each token is sampled from a probability distribution over the vocabulary, leading to confident but incorrect outputs. In real-world scenarios, this can cause critical failures in domains like healthcare or legal advice, where a model might invent drug interactions or cite non-existent case law.
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.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What does this Generative AI Leader question test?
Fundamentals of Generative AI — This question tests Fundamentals of Generative AI — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Hallucinations — Option A is correct because generative AI models, particularly large language models (LLMs), can produce plausible-sounding but factually incorrect or nonsensical outputs, known as hallucinations. This occurs due to the model's probabilistic nature and lack of true understanding, where it generates text based on learned patterns rather than verified facts.
What should I do if I get this Generative AI Leader 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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