CCDV-F Claude API Mechanics Practice Question
A team is building a retrieval-augmented assistant that sends a large document set inside the system prompt on every turn. To reduce cost, they enable prompt caching. They notice cache_read_input_tokens is high on most turns but intermittently drops to zero even though the system prompt text has not changed. Which explanation best fits this behavior?
⚠ Common exam trap
The trap here is assuming cache misses must be caused by changing content or a broken marker, when the real cause is often the cache entry expiring during an idle gap.
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
✓
The cache has a short time-to-live, so if enough time passes between requests the cached prefix expires and the next request pays full input cost before re-populating the cache.
Prompt caching entries expire after a time-to-live if they are not referenced. Bursty or idle traffic patterns cause the prefix to be evicted between requests, so the next call misses the cache and reports zero cache reads before re-creating the entry. Keeping steady traffic, or reducing gaps between calls, stabilizes cache_read_input_tokens and preserves the intended cost savings.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The API randomly invalidates cache entries per account to distribute load, so intermittent cache misses are expected and cannot be controlled.
Why it's wrong here
There is no documented random per-account eviction that causes unpredictable cache misses. Cache behavior is deterministic given the prefix, the cache markers, and the time-to-live window. Attributing the drops to random load distribution would lead the team to abandon a fixable configuration issue, when adjusting traffic patterns or cache breakpoints would resolve the intermittent misses they observe.
- ✗
Prompt caching only applies to user messages, so any content placed in the system prompt bypasses the cache and is billed at full input rate.
Why it's wrong here
Prompt caching is not restricted to user messages; cacheable prefixes can include system content, tool definitions, and message content, provided the prefix is marked correctly and meets minimum length. The observation that cache_read_input_tokens is usually high in this scenario directly contradicts the claim that system content is always bypassed, so this explanation cannot account for the intermittent drops.
- ✓
The cache has a short time-to-live, so if enough time passes between requests the cached prefix expires and the next request pays full input cost before re-populating the cache.
Why this is correct
Prompt caching uses a time-to-live window; if no request references the cached prefix before it expires, the entry is evicted. The next request then misses the cache, shows zero cache reads, and re-creates the entry, which matches the intermittent pattern described. Steady traffic keeps the cache warm, while idle gaps cause the drop to zero.
- ✗
Cache reads only register when max_tokens is large enough to cover the entire cached prefix, so smaller responses skip the cache.
Why it's wrong here
max_tokens limits generated output and is unrelated to whether a cached input prefix is read. Cache read accounting reflects input tokens matched against the cache, not output length. This explanation cannot explain why cache_read_input_tokens is usually high but sometimes zero, since output size does not vary in a way that would toggle cache reads on and off.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Anthropic exam blueprint
This CCDV-F practice question is part of Courseiva's free Anthropic 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 CCDV-F exam.