This Generative AI Leader practice question tests your understanding of google cloud's generative ai offerings. 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 media company uses Vertex AI to generate video captions. The generated captions sometimes contain factual errors about named entities (e.g., actor names). Which technique would most likely reduce these errors?
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.
Enable response caching
Why wrong: Caching reuses previous responses, not correcting errors.
Increase the temperature parameter
Why wrong: Higher temperature increases randomness, likely worsening errors.
Use Vertex AI grounding with a knowledge base of verified entities
Grounding supplies factual context to the model.
Decrease top_p to 0.3
Why wrong: Reduces token variety but doesn't introduce facts.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Use Vertex AI grounding with a knowledge base of verified entities
Option C is correct because Vertex AI grounding connects the model to a knowledge base of verified entities, allowing it to retrieve authoritative facts during generation. This reduces hallucinations about named entities by constraining outputs to validated data rather than relying solely on the model's parametric knowledge.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Enable response caching
Why it's wrong here
Caching reuses previous responses, not correcting errors.
Increase the temperature parameter
Why it's wrong here
Higher temperature increases randomness, likely worsening errors.
Use Vertex AI grounding with a knowledge base of verified entities
Why this is correct
Grounding supplies factual context to the model.
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.
Decrease top_p to 0.3
Why it's wrong here
Reduces token variety but doesn't introduce facts.
Common exam traps
The trap here is that candidates confuse techniques that control output randomness (temperature, top_p) with techniques that improve factual accuracy, overlooking the fundamental need for external knowledge retrieval via grounding.
Detailed technical explanation
Vertex AI grounding uses a retrieval-augmented generation (RAG) approach where the model queries a specified knowledge base (e.g., BigQuery, Vertex AI Search) at inference time to fetch relevant, verified facts. This is distinct from fine-tuning, as it does not alter model weights but instead provides context in the prompt, reducing the risk of outdated or hallucinated entity information. In practice, grounding is critical for applications like news captioning where actor names must match current, verified databases.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
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
Google Cloud's Generative AI Offerings — This question tests Google Cloud's Generative AI Offerings — Read the scenario before looking for a memorised answer..
The correct answer is: Use Vertex AI grounding with a knowledge base of verified entities — Option C is correct because Vertex AI grounding connects the model to a knowledge base of verified entities, allowing it to retrieve authoritative facts during generation. This reduces hallucinations about named entities by constraining outputs to validated data rather than relying solely on the model's parametric knowledge.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 25, 2026
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.
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