Quick Answer
The answer is provisioned throughput, because it directly addresses the need for cost predictability in a GenAI chatbot deployment by allocating a fixed capacity of model processing units, ensuring a stable monthly bill regardless of actual usage spikes. This pricing model locks in a specific throughput rate, eliminating the variable costs that come with pay-as-you-go options, where each inference request adds incremental charges. On the Google Cloud Generative AI Leader exam, this question tests your understanding of how to balance performance guarantees against financial risk, often appearing as a scenario where a company prioritizes budget control over elasticity. A common trap is choosing committed use discounts, which still leave you exposed to overage costs if demand exceeds your commitment, or pay-as-you-go, which offers no ceiling. Remember the memory tip: “Provisioned throughput = predictable price; pay-as-you-go = pay-as-you-blow your budget.”
This Generative AI Leader practice question tests your understanding of business strategies for generative ai solutions. 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 company wants to deploy a generative AI chatbot for customer service but is concerned about cost unpredictability due to variable usage. Which pricing model should they choose to best manage costs?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "best"
Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
Committed use discounts
Why wrong: Requires upfront commitment but still charges variable beyond committed usage.
Free tier
Why wrong: Too limited for production use.
Pay-as-you-go
Why wrong: Uses variable pay-per-use billing, not predictable.
Provisioned throughput
Provides fixed capacity with predictable monthly cost.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Provisioned throughput
Option C is correct because provisioned throughput provides fixed capacity with predictable monthly cost, ideal for managing cost uncertainty. Option A (pay-as-you-go) is variable. Option B (committed use discounts) requires commitment but still variable if usage exceeds. Option D (free tier) is too limited.
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.
Committed use discounts
Why it's wrong here
Requires upfront commitment but still charges variable beyond committed usage.
Free tier
Why it's wrong here
Too limited for production use.
Pay-as-you-go
Why it's wrong here
Uses variable pay-per-use billing, not predictable.
Provisioned throughput
Why this is correct
Provides fixed capacity with predictable monthly cost.
Clue confirmation
The clue word "best" in the question point toward this answer.
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.
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
Business Strategies for Generative AI Solutions — This question tests Business Strategies for Generative AI Solutions — Read the scenario before looking for a memorised answer..
The correct answer is: Provisioned throughput — Option C is correct because provisioned throughput provides fixed capacity with predictable monthly cost, ideal for managing cost uncertainty. Option A (pay-as-you-go) is variable. Option B (committed use discounts) requires commitment but still variable if usage exceeds. Option D (free tier) is too limited.
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.
Yes — watch for: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
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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