- A
Use batch prediction instead of online
Batch prediction is generally cheaper than online prediction.
- B
Increase the max output tokens
Why wrong: Increasing output tokens may increase cost.
- C
Use grounding with Google Search
Why wrong: Grounding with Google Search incurs additional costs.
- D
Use a larger model
Why wrong: Larger models cost more per token.
- E
Use context caching
Context caching reduces input token costs for repeated prompts.
Question 922 of 997
Reduce Costs When Using Vertex AI Gemini API
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.)
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.
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
✓
Use batch prediction instead of online
Batch prediction reduces cost because it processes multiple requests asynchronously in a single batch, allowing Vertex AI to optimize resource utilization and charge lower per-token rates compared to online (real-time) prediction, which requires dedicated infrastructure for low-latency responses.
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.
- ✓
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
Common exam trap: answer the scenario, not the keyword
Candidates often mistakenly believe that increasing max output tokens or using a larger model improves quality without cost impact, but both directly increase token consumption and per-token pricing.
Trap categories for this question
Command / output trap
Increasing output tokens may increase cost.
Detailed technical explanation
How to think about this question
Context caching reduces cost by storing frequently accessed context (e.g., large documents or system instructions) in a cache, so subsequent requests only pay for the new input tokens minus the cached portion, with cache storage charges being lower than full input token processing. Batch prediction leverages asynchronous processing to pool requests, achieving higher throughput and lower cost per token due to batching efficiency and reduced infrastructure overhead.
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 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
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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: Use batch prediction instead of online — Batch prediction reduces cost because it processes multiple requests asynchronously in a single batch, allowing Vertex AI to optimize resource utilization and charge lower per-token rates compared to online (real-time) prediction, which requires dedicated infrastructure for low-latency responses.
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.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
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Last reviewed: Jul 4, 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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