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200-901 Practice Question: A developer is designing an API that needs to…
A developer is designing an API that needs to support rate limiting per API key. The application is deployed on multiple instances. Which approach ensures consistent rate limiting across all instances?
⚠ Common exam trap
Cisco often tests the misconception that local counters or environment variables can be used for distributed state, when in fact they lack the shared, atomic, and persistent storage required for multi-instance rate limiting.
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 distributed cache like Redis
A distributed cache like Redis provides a shared, atomic counter that all application instances can read and increment, ensuring consistent rate limiting across a multi-instance deployment. Redis supports atomic operations like INCR and EXPIRE, which are essential for implementing sliding window or token bucket algorithms without race conditions.
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 local in-memory counter
Why it's wrong here
Local counters are not shared across instances.
- ✗
Use a file-based lock
Why it's wrong here
File locks are not designed for distributed systems.
- ✗
Use environment variables
Why it's wrong here
Environment variables are static and not suitable for dynamic rate limiting.
- ✓
Use a distributed cache like Redis
Why this is correct
Redis provides a shared counter accessible from all instances.
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