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 estimate the total cost of ownership (TCO) for a gen AI solution on Google Cloud. Which factors are most important?
Only model training cost
Why wrong: Inference costs often dominate over time.
Compute, storage, and API call costs
These three categories cover the primary cost drivers in a gen AI solution.
Only inference cost
Why wrong: Training and storage costs also contribute to TCO.
Only compute cost
Why wrong: Storage and API costs can be significant and must be included.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Compute, storage, and API call costs
Option B is correct because the total cost of ownership (TCO) for a generative AI solution on Google Cloud encompasses all operational expenses, including compute (e.g., TPU/GPU instances for training and inference), storage (e.g., Cloud Storage for datasets and model artifacts), and API call costs (e.g., Vertex AI prediction requests). Focusing on a single cost component, such as training or inference alone, ignores the recurring expenses of serving the model and storing data, which often dominate long-term TCO.
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.
Only model training cost
Why it's wrong here
Inference costs often dominate over time.
Compute, storage, and API call costs
Why this is correct
These three categories cover the primary cost drivers in a gen AI solution.
Related concept
Read the scenario before looking for a memorised answer.
Only inference cost
Why it's wrong here
Training and storage costs also contribute to TCO.
Only compute cost
Why it's wrong here
Storage and API costs can be significant and must be included.
Common exam traps
Google Cloud often tests the misconception that TCO is dominated by a single cost factor (e.g., training), when in reality, inference and API costs frequently surpass training expenses in production deployments.
Detailed technical explanation
Under the hood, Google Cloud bills for compute using custom machine types or accelerators (e.g., A100 GPUs at ~$3.50/hour) and for storage via Cloud Storage classes (e.g., Standard at $0.020/GB/month). API calls to Vertex AI incur per-token costs (e.g., $0.0025 per 1,000 input tokens for text-bison), which can accumulate rapidly in high-throughput scenarios. A real-world TCO analysis must also account for data egress fees and model fine-tuning cycles, which are often underestimated.
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
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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: Compute, storage, and API call costs — Option B is correct because the total cost of ownership (TCO) for a generative AI solution on Google Cloud encompasses all operational expenses, including compute (e.g., TPU/GPU instances for training and inference), storage (e.g., Cloud Storage for datasets and model artifacts), and API call costs (e.g., Vertex AI prediction requests). Focusing on a single cost component, such as training or inference alone, ignores the recurring expenses of serving the model and storing data, which often dominate long-term TCO.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 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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