- A
Revert to PaLM 2 since it was more reliable for this task.
Why wrong: Reverting avoids addressing the issue; prompt tuning can likely fix it.
- B
Add negative prompts to discourage incorrect facts.
Why wrong: Negative prompts are for style, not for factual accuracy.
- C
Adjust the prompt to be more explicit about adhering to the input data, and reduce the temperature.
Different models may require slight prompt adjustments; lower temperature and clearer instructions improve factual precision.
- D
Increase the model's temperature to make outputs more deterministic.
Why wrong: Higher temperature increases randomness, not determinism.
Question 163 of 997
Fixing Gemini API Factual Inconsistency
This Generative AI Leader practice question tests your understanding of google cloud's generative ai offerings. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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.
An e-commerce company is using Vertex AI PaLM 2 for Text (via Model Garden) to generate product descriptions. They have an existing pipeline that calls the model with a prompt including product attributes. Recently, they migrated to the Gemini API. The team notices that the Gemini model sometimes outputs descriptions that are factually inconsistent with the input (e.g., wrong color or size). This was less frequent with PaLM 2. They have not changed the prompts. What is the most likely cause and solution?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"most likely"Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
Quick Answer
The correct answer is to adjust the prompt to be more explicit about adhering to the input data and reduce the temperature. This works because Gemini models can interpret instructions differently than PaLM 2, and a lower temperature reduces randomness, forcing the model to stick closer to the provided facts rather than generating creative but inconsistent details. On the Google Cloud Generative AI Leader exam, this scenario tests your understanding of how prompt engineering and model parameters directly control factual consistency, a key distinction between model families. A common trap is assuming the same prompt will yield identical behavior across models, or incorrectly increasing temperature to "fix" errors, which actually worsens the problem. Remember the memory tip: "Cold facts, hot errors"—lower temperature keeps the output grounded in the input data, while higher heat invites hallucinations.
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
✓
Adjust the prompt to be more explicit about adhering to the input data, and reduce the temperature.
Option C is correct because the core issue is that the prompt, originally optimized for PaLM 2, may not be sufficiently explicit for the Gemini model's different instruction-following behavior. By making the prompt more explicit about adhering strictly to the input data and reducing the temperature (e.g., to 0.2 or lower), the model's output becomes more deterministic and less prone to hallucinating incorrect attributes. This directly addresses the factual inconsistency without changing the model family, leveraging Gemini's ability to follow detailed instructions when properly guided.
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.
- ✗
Revert to PaLM 2 since it was more reliable for this task.
Why it's wrong here
Reverting avoids addressing the issue; prompt tuning can likely fix it.
- ✗
Add negative prompts to discourage incorrect facts.
Why it's wrong here
Negative prompts are for style, not for factual accuracy.
- ✓
Adjust the prompt to be more explicit about adhering to the input data, and reduce the temperature.
Why this is correct
Different models may require slight prompt adjustments; lower temperature and clearer instructions improve factual precision.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Increase the model's temperature to make outputs more deterministic.
Why it's wrong here
Higher temperature increases randomness, not determinism.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates assume model migration is the root cause and choose to revert (Option A), when in fact the real issue is prompt adaptation and hyperparameter tuning for the new model's behavior.
Detailed technical explanation
How to think about this question
Under the hood, Gemini models use a transformer architecture with a different tokenization and attention mechanism than PaLM 2, meaning they may interpret ambiguous or under-specified prompts differently. Reducing temperature (e.g., to 0.0–0.2) forces the model to sample from the highest-probability tokens, reducing variability and hallucination. In practice, for tasks requiring strict factual adherence, a temperature of 0.1 combined with explicit instructions like 'Only use the attributes provided: [list]' can eliminate most inconsistencies.
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.
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FAQ
Questions learners often ask
What does this Generative AI Leader question test?
Google Cloud's Generative AI Offerings — This question tests Google Cloud's Generative AI Offerings — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Adjust the prompt to be more explicit about adhering to the input data, and reduce the temperature. — Option C is correct because the core issue is that the prompt, originally optimized for PaLM 2, may not be sufficiently explicit for the Gemini model's different instruction-following behavior. By making the prompt more explicit about adhering strictly to the input data and reducing the temperature (e.g., to 0.2 or lower), the model's output becomes more deterministic and less prone to hallucinating incorrect attributes. This directly addresses the factual inconsistency without changing the model family, leveraging Gemini's ability to follow detailed instructions when properly guided.
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: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
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