The answer is fine-tuning the open-source model because it offers dramatically lower per-report costs at scale. While the API inference approach appears simpler with no upfront investment, its $0.10 per 1K token cost quickly escalates to $3 million over 12 months for 50,000 monthly reports, whereas fine-tuning’s $70,000 upfront investment yields a per-token inference cost of just $0.02, bringing the total to $670,000—a savings of over $2.3 million. This question tests your ability to perform a cost comparison between fine-tuning vs API inference in GenAI, a core skill for the Google Cloud Generative AI Leader exam where candidates must evaluate total cost of ownership including compute, engineering, and inference at scale. The common trap is focusing only on the lower upfront cost of API calls without projecting volume; remember that fine-tuning shifts expense from variable inference to fixed training, making it cheaper when monthly token volume exceeds roughly 1.5 million. Memory tip: “Fine-tune for frequent use, API for sporadic bursts.”
Generative AI Leader Practice Question: Business Strategies for Generative AI Solutions
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 startup with $500k in seed funding wants to integrate GenAI into their SaaS product for automated report generation. They have 2 ML engineers and expect 10,000 monthly users initially. They estimate that using a foundation model API (e.g., Gemini) will cost $0.10 per 1K tokens, and each report uses about 5K tokens. Alternatively, they could fine-tune an open-source model on their domain data, estimated at $50k for compute and $20k for engineering time, with inference cost of $0.02 per 1K tokens on a dedicated endpoint. Which approach is more cost-effective over the first 12 months assuming 50,000 reports per month?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "first"
Why it matters: Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Fine-tune the open-source model because it has lower per-report cost
Option D is correct because the total cost of fine-tuning over 12 months is $70,000 upfront plus $0.02 per 1K tokens * 5K tokens per report * 50,000 reports per month * 12 months = $600,000, totaling $670,000. The API approach costs $0.10 per 1K tokens * 5K tokens * 50,000 reports * 12 = $3,000,000, making fine-tuning significantly cheaper at scale despite the upfront investment.
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 the foundation model API because it has lower upfront cost
Why it's wrong here
While upfront cost is lower, the per-token cost results in $300k over a year, exceeding the fine-tune option.
✗
Use a combination of both depending on report complexity
Why it's wrong here
A combination adds complexity and does not necessarily reduce cost; the fine-tune model can handle all reports.
✗
Build a custom model from scratch
Why it's wrong here
Building from scratch would cost more in both time and money, likely exceeding $500k.
✓
Fine-tune the open-source model because it has lower per-report cost
Why this is correct
Fine-tuning yields lower per-token cost, resulting in $190k total over a year, which is cheaper than the API.
Clue confirmation
The clue word "first" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that lower upfront cost always means lower total cost, ignoring the multiplicative effect of per-unit costs at scale—candidates fixate on the $70k fine-tuning investment versus the API's zero upfront cost without calculating the 12-month total.
Detailed technical explanation
How to think about this question
Fine-tuning an open-source model like Llama 2 or Mistral on domain-specific data reduces inference cost per token because the model is optimized for the task and can run on a dedicated endpoint with lower latency and no per-request API markup. The upfront compute cost ($50k) covers GPU hours for training (e.g., using 8x A100s for several days), while the engineering cost ($20k) includes data preprocessing and deployment. In practice, the break-even point occurs when the volume of reports exceeds ~23,000 per month, after which fine-tuning becomes cheaper than the API.
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
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What does this Generative AI Leader question test?
Business Strategies for Generative AI Solutions — This question tests Business Strategies for Generative AI Solutions — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Fine-tune the open-source model because it has lower per-report cost — Option D is correct because the total cost of fine-tuning over 12 months is $70,000 upfront plus $0.02 per 1K tokens * 5K tokens per report * 50,000 reports per month * 12 months = $600,000, totaling $670,000. The API approach costs $0.10 per 1K tokens * 5K tokens * 50,000 reports * 12 = $3,000,000, making fine-tuning significantly cheaper at scale despite the upfront investment.
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.
Are there clue words in this question I should notice?
Yes — watch for: "first". Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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