Caching Assessment | Distributed Caching

1

Your e-commerce site caches product prices. A flash sale starts but cached prices show the old (higher) price. Customers are angry. What went wrong?

The cache was too small Cache invalidation wasn't triggered when prices changed Redis was too slow The database crashed

2

A popular influencer tweets about your product. Suddenly 100,000 requests/second hit your API. Your cache key for that product expires at the same moment. What happens?

The cache automatically scales Thundering herd - all 100K requests hit your database simultaneously Redis handles it gracefully The requests are load balanced

3

You're designing a real-time multiplayer game leaderboard showing top 100 players. Which caching approach is best?

Cache the entire leaderboard as a JSON string Use Redis Sorted Sets with player scores Store each player's rank in Memcached Don't cache - query the database each time

4

Your application has a 60% cache hit ratio. What does this indicate?

Excellent performance - most requests are cached Poor performance - 40% of requests hit the database The cache is 60% full 60% of data is cacheable

5

You're caching user sessions. Which pattern ensures a user's session is always current after they update their profile?

Cache-aside with long TTL Write-through caching Write-behind caching No caching for sessions

6

Your cache uses LRU eviction. A nightly batch job scans all 1 million products for a report. The next morning, users complain about slow response times. Why?

The batch job corrupted the cache LRU evicted all hot user data because batch job touched every product The database is overloaded Redis ran out of memory

7

You need to cache API responses from a third-party service that rate-limits you to 100 requests/minute. What's the most important caching consideration?

Use the fastest cache possible Set a TTL that matches the data freshness requirements while staying under rate limits Cache everything forever Don't cache third-party data

8

In a microservices architecture, Service A caches data from Service B. Service B's data changes. How should Service A's cache be invalidated?

Service A polls Service B every second Service B publishes an event when data changes, Service A subscribes and invalidates Use a shared database Never cache data from other services

9

Your Redis cache is at 100% memory. New writes are failing. What's the safest immediate fix?

Restart Redis Delete random keys Set maxmemory-policy to allkeys-lru so Redis auto-evicts Add more RAM to the server

10

You're implementing rate limiting: max 100 requests per user per minute. Which Redis data structure is most efficient?

String with INCR List of timestamps Sorted Set with timestamps as scores Hash of request counts

11

Your cache warmup loads 10GB of data on startup, taking 5 minutes. During this time, all requests hit the database. How can you improve this?

Use a bigger cache Lazy loading - only cache on first request for each key Use faster disks Don't use caching

12

Final question: You're designing caching for a banking application showing account balances. What's the most important consideration?

Maximum cache hit ratio Lowest possible latency Consistency - users must never see stale balances Cost optimization

Launch Offer