Generative AI Leader Fundamentals of Generative AI Practice Question
Which THREE of the following are potential risks when deploying generative AI?
⚠ Common exam trap
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.
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
✓
Hallucinations
Option A (Hallucinations) is correct because generative AI models can produce fluent but factually incorrect or fabricated outputs, which poses a risk when users trust them for decisions or information. Option B (Memorization of sensitive training data) is correct because models may reproduce verbatim or near-verbatim content from their training corpus, potentially leaking PII, proprietary data, or copyrighted material. Option C (Bias and fairness issues) is correct because models learn statistical patterns from training data and can amplify societal biases, leading to discriminatory or unfair outcomes in hiring, lending, or content moderation. Option E (Toxic or harmful content generation) is correct because generative models can be prompted or inadvertently produce hate speech, harassment, self-harm instructions, or other harmful material. Option D (Increased model accuracy) is not a risk but a potential benefit, so it does not belong among the deployment risks.
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
Hallucinations are a genuine generative AI risk: the model generates fluent but factually incorrect or fabricated output because it predicts probable tokens rather than verifying truth. This directly satisfies the stem's requirement to identify a deployment risk, since unverified content can mislead users and damage trust.
- ✓
Memorization of sensitive training data
Why this is correct
Generative models can reproduce verbatim fragments of their training corpus when prompted, so personally identifiable information, credentials or proprietary text ingested during training may be emitted to unrelated users. This verbatim regurgitation constitutes a direct data-leakage risk.
- ✓
Bias and fairness issues
Why this is correct
Training data reflects historical and societal imbalances, so generative models can produce discriminatory output against protected groups in hiring, lending or service contexts. This creates legal, ethical and reputational exposure that requires bias testing and mitigation.
- ✗
Increased model accuracy
Why it's wrong here
Accuracy gains are a benefit, not a risk, so this fails the question's risk framing. It is tempting because improved accuracy is a genuine outcome of well-tuned generative AI models, and would be the correct selection if the stem instead asked for advantages or benefits of deployment.
- ✓
Toxic or harmful content generation
Why this is correct
Models trained on unfiltered internet data can produce abusive, violent, harassing or otherwise harmful output when prompted, exposing the organisation to reputational damage, user harm and regulatory scrutiny. Output filtering and moderation layers are required to mitigate this generation risk.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This Generative AI Leader practice question is part of Courseiva's free Google Cloud 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 Generative AI Leader exam.