Generative AI Leader Practice Question: Business Strategies for Generative AI Solutions
A company wants to estimate the total cost of ownership (TCO) for a gen AI solution on Google Cloud. Which factors are most important?
⚠ Common exam trap
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.
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
✓
Compute, storage, and API call costs
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.
Answer analysis
Option-by-option breakdown
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
Training is a one-off cost; TCO also covers inference serving, storage, data preparation, monitoring, networking and personnel. Training-only understates ongoing spend. It tempts because training attracts the largest initial invoice, but inference typically dominates lifetime cost in production gen AI deployments.
- ✓
Compute, storage, and API call costs
Why this is correct
Compute, storage, and API call costs are the primary recurring drivers of gen AI TCO, since inference consumes GPU or TPU compute, datasets and embeddings consume storage, and model or API invocations are metered per call. These directly determine ongoing operational spend.
- ✗
Only inference cost
Why it's wrong here
Inference cost is only one component; TCO must also include training, data storage and egress, tuning, monitoring, and engineering labour. It is tempting because inference dominates runtime spend in steady-state serving, so it would be the right focus when comparing per-token serving options after the model is already built.
- ✗
Only compute cost
Why it's wrong here
TCO spans compute, storage, networking, data preparation, model tuning, inference serving, monitoring and staffing. Compute-only ignores those recurring drivers. It tempts because inference and training instances dominate early bills, but a full TCO estimate must include every cost category over the solution's lifetime.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.