Quick Answer
The answer is to use context caching and batch prediction, as these two actions directly reduce cost when using the Vertex AI Gemini API. Context caching lowers expenses by storing frequently used input tokens, so you are not charged repeatedly for the same context in every request, while batch prediction processes multiple requests asynchronously at a significantly lower per-token rate than online (real-time) inference. On the Google Cloud Generative AI Leader exam, this question tests your understanding of cost optimization strategies within the Gemini API, often appearing as a multiple-select trap where candidates mistakenly choose options like increasing max output tokens or using larger models, which actually drive costs higher. A common memory tip is to think of “caching” as avoiding re-payment for the same data, and “batching” as buying in bulk for a discount—both are about reducing redundant or premium processing.
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.
Which TWO actions can reduce the cost of using Vertex AI Gemini API? (Choose two.)
Use batch prediction instead of online
Batch prediction is generally cheaper than online prediction.
Increase the max output tokens
Why wrong: Increasing output tokens may increase cost.
Use grounding with Google Search
Why wrong: Grounding with Google Search incurs additional costs.
Use a larger model
Why wrong: Larger models cost more per token.
Use context caching
Context caching reduces input token costs for repeated prompts.
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 batch prediction instead of online
Options A and B are correct. Context caching reduces repeated input costs, and batch prediction is cheaper than online. Options C, D, and E are incorrect because increasing max output tokens may increase cost, using larger models costs more, and grounding with Google Search incurs additional costs.
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.
Use batch prediction instead of online
Why this is correct
Batch prediction is generally cheaper than online prediction.
Related concept
Read the scenario before looking for a memorised answer.
Increase the max output tokens
Why it's wrong here
Increasing output tokens may increase cost.
Use grounding with Google Search
Why it's wrong here
Grounding with Google Search incurs additional costs.
Use a larger model
Why it's wrong here
Larger models cost more per token.
Use context caching
Why this is correct
Context caching reduces input token costs for repeated prompts.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Trap categories for this question
Command / output trap
Increasing output tokens may increase cost.
Detailed technical explanation
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
What to study next
Identify which Generative AI Leader exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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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 batch prediction instead of online — Options A and B are correct. Context caching reduces repeated input costs, and batch prediction is cheaper than online. Options C, D, and E are incorrect because increasing max output tokens may increase cost, using larger models costs more, and grounding with Google Search incurs additional costs.
Identify which Generative AI Leader exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 23, 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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