- A
Enable prompt caching
Prompt caching allows the API to reuse cached results for common prompt prefixes, reducing latency and cost for repeated queries.
- B
Use batching to send multiple requests in one API call
Batching allows processing multiple inputs together, improving throughput and reducing per-request overhead, which lowers latency and cost.
- C
Auto-scale the number of API endpoints
Why wrong: Auto-scaling increases capacity during high traffic, but it may not reduce per-request latency and can increase costs due to more resources.
- D
Quantize the model to FP16
Why wrong: Quantization is a model optimization performed by the provider, not something the client can implement for a cloud-hosted API.
- E
Implement rate limiting for API requests
Rate limiting controls the number of requests per user, preventing abuse and ensuring fair resource allocation while managing costs.
AI0-001 AI Infrastructure and Technologies Practice Question
This AI0-001 practice question tests your understanding of ai infrastructure 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 deploying a large language model via a REST API using a cloud AI service. They expect high traffic and need to minimize latency while controlling costs. Which THREE strategies should they implement?
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
Enable prompt caching
Option A is correct because prompt caching stores the intermediate key-value (KV) cache from previous inference runs for identical or similar prompts. When a cached prompt is reused, the model skips recomputing the attention keys and values for the cached portion, significantly reducing time-to-first-token (TTFT) latency and lowering compute cost per request. This is especially effective for high-traffic scenarios where many users submit the same or slightly varied prompts.
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.
- ✓
Enable prompt caching
Why this is correct
Prompt caching allows the API to reuse cached results for common prompt prefixes, reducing latency and cost for repeated queries.
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 batching to send multiple requests in one API call
Why this is correct
Batching allows processing multiple inputs together, improving throughput and reducing per-request overhead, which lowers latency and 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.
- ✗
Auto-scale the number of API endpoints
Why it's wrong here
Auto-scaling increases capacity during high traffic, but it may not reduce per-request latency and can increase costs due to more resources.
- ✗
Quantize the model to FP16
Why it's wrong here
Quantization is a model optimization performed by the provider, not something the client can implement for a cloud-hosted API.
- ✓
Implement rate limiting for API requests
Why this is correct
Rate limiting controls the number of requests per user, preventing abuse and ensuring fair resource allocation while managing costs.
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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the distinction between infrastructure-level optimizations (like auto-scaling) and model-level optimizations (like quantization), expecting candidates to recognize that only API-layer strategies (caching, batching, rate limiting) directly control latency and cost at the REST endpoint.
Detailed technical explanation
How to think about this question
Prompt caching works by storing the KV cache in GPU high-bandwidth memory (HBM) or a distributed cache (e.g., Redis) keyed by a hash of the prompt prefix. In practice, services like OpenAI’s prompt caching or Anthropic’s prompt caching reduce latency by up to 50% for long prompts. Batching (Option B) combines multiple independent requests into a single API call, allowing the model to process them as a single batch, which amortizes overhead and improves throughput, but it can increase latency for the first request in the batch if the batch is not filled immediately.
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 practitioner preparing for the AI0-001 exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
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.
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FAQ
Questions learners often ask
What does this AI0-001 question test?
AI Infrastructure and Technologies — This question tests AI Infrastructure and Technologies — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Enable prompt caching — Option A is correct because prompt caching stores the intermediate key-value (KV) cache from previous inference runs for identical or similar prompts. When a cached prompt is reused, the model skips recomputing the attention keys and values for the cached portion, significantly reducing time-to-first-token (TTFT) latency and lowering compute cost per request. This is especially effective for high-traffic scenarios where many users submit the same or slightly varied prompts.
What should I do if I get this AI0-001 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
This AI0-001 practice question is part of Courseiva's free CompTIA 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 AI0-001 exam.
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