- A
Use a smaller base model like Gemini Flash
Smaller models require less compute for both training and inference.
- B
Use full fine‑tuning for better quality
Why wrong: Full fine‑tuning updates all parameters, which is computationally expensive.
- C
Increase the number of training epochs
Why wrong: More epochs increase compute time and cost.
- D
Use adapter‑based fine‑tuning (LoRA)
LoRA fine‑tunes only a small set of parameters, significantly reducing cost.
- E
Use a larger base model like Gemini Ultra
Why wrong: Larger models cost more to run and fine‑tune.
Question 19 of 997
Generative AI Leader Generative AI Concepts and Technologies Practice Question
This Generative AI Leader practice question tests your understanding of generative ai concepts and technologies. 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 is fine‑tuning a large language model for a domain‑specific task. They have a limited budget and want to minimize the cost of fine‑tuning. Which TWO approaches are most cost‑effective?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"minimum / minimize"Why it matters: Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
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 a smaller base model like Gemini Flash
Option A is correct because using a smaller base model like Gemini Flash reduces the computational resources (GPU memory, training time) required for fine-tuning, directly lowering cost while still achieving adequate performance for domain-specific tasks. Smaller models have fewer parameters, which means fewer floating-point operations (FLOPs) per training step, making them more budget-friendly.
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 a smaller base model like Gemini Flash
Why this is correct
Smaller models require less compute for both training and inference.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use full fine‑tuning for better quality
Why it's wrong here
Full fine‑tuning updates all parameters, which is computationally expensive.
- ✗
Increase the number of training epochs
Why it's wrong here
More epochs increase compute time and cost.
- ✓
Use adapter‑based fine‑tuning (LoRA)
Why this is correct
LoRA fine‑tunes only a small set of parameters, significantly reducing cost.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use a larger base model like Gemini Ultra
Why it's wrong here
Larger models cost more to run and fine‑tune.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Candidates often think that larger models or full fine-tuning always yield better results, but the trap here is that cost-effectiveness prioritizes resource efficiency over raw quality, and adapter methods like LoRA provide a practical trade-off that they overlook.
Detailed technical explanation
How to think about this question
Adapter-based fine-tuning (LoRA) works by freezing the original model weights and injecting trainable low-rank matrices into specific layers, reducing the number of trainable parameters by up to 10,000x. This allows fine-tuning on a single GPU with significantly less memory (e.g., 8GB vs 80GB for full fine-tuning of a 7B model), and the adapters can be swapped without retraining the base model. In real-world scenarios, teams often combine a small base model with LoRA to achieve 90% of full fine-tuning quality at 1% of the cost.
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.
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FAQ
Questions learners often ask
What does this Generative AI Leader question test?
Generative AI Concepts and Technologies — This question tests Generative AI Concepts and Technologies — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use a smaller base model like Gemini Flash — Option A is correct because using a smaller base model like Gemini Flash reduces the computational resources (GPU memory, training time) required for fine-tuning, directly lowering cost while still achieving adequate performance for domain-specific tasks. Smaller models have fewer parameters, which means fewer floating-point operations (FLOPs) per training step, making them more budget-friendly.
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: "minimum / minimize". Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
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
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