AIF-C01 Fundamentals of Generative AI Practice Question
A company uses Amazon Bedrock to generate marketing content. They want to reduce costs while maintaining response quality. Which action is most effective?
⚠ Common exam trap
Test-takers frequently confuse cost-reduction strategies with performance-enhancing strategies, assuming that fine-tuning or caching always saves money, when in fact the most direct lever is selecting the smallest capable model for the job.
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
✓
Select a smaller foundation model that still meets accuracy requirements.
The most effective cost-reduction strategy because smaller foundation models (FMs) have fewer parameters, resulting in lower compute and inference costs per request. If the smaller model still meets the required accuracy benchmarks for the marketing content task, it directly reduces operational expenditure without sacrificing quality. Amazon Bedrock offers a range of FMs (e.g., from large models like Claude 3 Opus to smaller ones like Claude 3 Haiku), allowing you to match model size to task complexity.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Fine-tune a larger model to improve accuracy and reduce retries.
Why it's wrong here
Fine-tuning a larger model raises training and inference costs rather than reducing them, and does not guarantee fewer retries. It is tempting because fine-tuning improves task accuracy on domain-specific data, which is the right choice when quality, not cost, is the binding constraint.
- ✗
Increase the temperature parameter to get shorter responses.
Why it's wrong here
Temperature controls randomness of token selection, not output length; raising it produces more varied text and often longer, less predictable responses, increasing token costs. It is tempting because temperature is a familiar generation parameter, and it is correctly used to tune creativity for brainstorming or diverse marketing variants.
- ✓
Select a smaller foundation model that still meets accuracy requirements.
Why this is correct
Selecting a smaller foundation model directly reduces inference cost per token, since Bedrock charges scale with model size and capability tier. This satisfies the stem's dual constraint: cutting spend while preserving response quality, provided the smaller model still meets the stated accuracy requirements for marketing content generation.
- ✗
Cache previous responses to reuse for similar prompts.
Why it's wrong here
Caching identical or near-identical prompts only helps when traffic repeats; marketing content generation typically produces varied prompts, so hit rates stay low and no cost reduction materialises. It is tempting because prompt caching genuinely cuts token costs for repetitive workloads such as fixed system prompts or FAQ chatbots.
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