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.
A gen AI application produces hallucinations (factually incorrect outputs). Which mitigation strategy is LEAST effective?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "least"
Why it matters: You want the option with minimum overhead, fewest steps, or lowest impact — not the most feature-rich or comprehensive answer.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Increasing model temperature
Increasing model temperature makes the model more random and creative, which directly increases the likelihood of hallucinations. It does not constrain or ground the output in factual data, making it the least effective mitigation strategy among the options.
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.
✗
Using prompt templates with constraints
Why it's wrong here
Carefully crafted prompts can guide the model away from hallucination by specifying required factuality.
✗
Using grounding with a knowledge base
Why it's wrong here
Grounding ties outputs to verifiable sources, directly reducing hallucinations.
✗
Implementing retrieval-augmented generation
Why it's wrong here
RAG retrieves factual context to inform generation, reducing hallucinations.
✓
Increasing model temperature
Why this is correct
Higher temperature leads to more diverse but less predictable outputs, exacerbating hallucinations.
Clue confirmation
The clue word "least" in the question point toward this answer.
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 misconception that increasing model temperature improves accuracy by making the model 'more confident,' when in reality it increases randomness and hallucination risk.
Trap categories for this question
Command / output trap
Grounding ties outputs to verifiable sources, directly reducing hallucinations.
Detailed technical explanation
How to think about this question
Temperature is a hyperparameter that scales the logits before applying softmax; higher values (e.g., >1.0) flatten the probability distribution, making low-probability tokens more likely, which increases creativity but also factual errors. In contrast, RAG uses a retriever (e.g., Dense Passage Retrieval) to fetch top-k chunks from a vector database, then the generator (e.g., a decoder-only LLM) conditions on those chunks, effectively reducing hallucination by anchoring the output to retrieved evidence.
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: Increasing model temperature — Increasing model temperature makes the model more random and creative, which directly increases the likelihood of hallucinations. It does not constrain or ground the output in factual data, making it the least effective mitigation strategy among the options.
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.
Are there clue words in this question I should notice?
Yes — watch for: "least". You want the option with minimum overhead, fewest steps, or lowest impact — not the most feature-rich or comprehensive answer.
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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