This 200-901 practice question tests your understanding of application deployment and security. 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 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?
Use a local in-memory counter
Why wrong: Local counters are not shared across instances.
Use a file-based lock
Why wrong: File locks are not designed for distributed systems.
Use environment variables
Why wrong: Environment variables are static and not suitable for dynamic rate limiting.
Use a distributed cache like Redis
Redis provides a shared counter accessible from all instances.
Answer choices
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
Option D is correct because 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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
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.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
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.
Detailed technical explanation
Under the hood, Redis uses a single-threaded event loop to guarantee atomicity of commands like INCR, which is critical for accurate rate limiting. A common implementation uses a sorted set to track timestamps per API key, enabling sliding window rate limiting with O(log N) complexity. In real-world scenarios, this approach handles millions of requests per second when combined with Redis Cluster for horizontal scaling.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A practitioner preparing for the 200-901 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
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FAQ
Application Deployment and Security — This question tests Application Deployment and Security — Read the scenario before looking for a memorised answer..
The correct answer is: Use a distributed cache like Redis — Option D is correct because 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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 2026
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