- A
Use a smaller model variant (e.g., Claude Instant instead of Claude)
Smaller models have lower cost per token and often similar latency for simple tasks.
- B
Switch from text generation to image generation
Why wrong: Image generation does not serve the Q&A use case and may have different cost profile.
- C
Enable prompt caching for frequently used system prompts
Prompt caching reduces the need to reprocess identical prompt prefixes, saving compute and cost.
- D
Increase the context window to include more documents
Why wrong: Larger context increases tokens processed, raising cost and latency.
- E
Use a vector database to pre-filter documents before sending to the model
Pre-filtering reduces the number of tokens sent to the model, lowering cost and latency.
Question 41 of 1,000
AIF-C01 Generative AI and Foundation Models Practice Question
This AIF-C01 practice question tests your understanding of generative ai and foundation models. 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 uses Amazon Bedrock with Anthropic Claude for a question-answering system. They want to reduce costs while maintaining acceptable latency. Which TWO actions would help achieve this? (Choose two.)
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 model variant (e.g., Claude Instant instead of Claude)
Option A is correct because using a smaller model variant like Claude Instant (now Claude Haiku) reduces the computational cost per inference while still providing sufficient capability for many question-answering tasks. Smaller models have fewer parameters, requiring less GPU memory and compute time, which directly lowers API costs and can maintain acceptable latency for simpler queries.
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 model variant (e.g., Claude Instant instead of Claude)
Why this is correct
Smaller models have lower cost per token and often similar latency for simple tasks.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Switch from text generation to image generation
Why it's wrong here
Image generation does not serve the Q&A use case and may have different cost profile.
- ✓
Enable prompt caching for frequently used system prompts
Why this is correct
Prompt caching reduces the need to reprocess identical prompt prefixes, saving compute and cost.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Increase the context window to include more documents
Why it's wrong here
Larger context increases tokens processed, raising cost and latency.
- ✓
Use a vector database to pre-filter documents before sending to the model
Why this is correct
Pre-filtering reduces the number of tokens sent to the model, lowering cost and latency.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may think increasing the context window (Option D) improves accuracy without realizing it linearly increases token costs and latency on Amazon Bedrock, or they may confuse prompt caching with simple input repetition rather than understanding it as a server-side KV cache optimization specific to the Anthropic Claude model on Bedrock.
Detailed technical explanation
How to think about this question
Prompt caching (Option C) works by storing the key-value (KV) cache of frequently used system prompts on the server side, avoiding recomputation of attention states for repeated prefix tokens. This reduces both latency and cost because the model does not need to reprocess the cached portion, and AWS bills only for the unique tokens. A vector database (Option E) pre-filters documents by retrieving only the most relevant chunks via semantic similarity search (e.g., using cosine distance on embeddings), drastically reducing the number of input tokens sent to the LLM and thus lowering cost and latency.
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
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FAQ
Questions learners often ask
What does this AIF-C01 question test?
Generative AI and Foundation Models — This question tests Generative AI and Foundation Models — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use a smaller model variant (e.g., Claude Instant instead of Claude) — Option A is correct because using a smaller model variant like Claude Instant (now Claude Haiku) reduces the computational cost per inference while still providing sufficient capability for many question-answering tasks. Smaller models have fewer parameters, requiring less GPU memory and compute time, which directly lowers API costs and can maintain acceptable latency for simpler queries.
What should I do if I get this AIF-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
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