Multi-level caching in HLD - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Primary benefit of multi-level caching

Which of the following best describes the main advantage of using multi-level caching in a system?

A It increases storage capacity by duplicating data across all cache levels.

B It eliminates the need for a database by storing all data in caches.

C It reduces latency by storing frequently accessed data closer to the user at multiple cache layers.

D It guarantees data consistency without any synchronization mechanisms.

💡 Hint

Think about how caches at different levels help speed up data access.

Multi-level caching reduces latency by having caches at different layers (e.g., CPU cache, application cache, distributed cache) so data can be accessed faster depending on where it is stored.

❓ Architecture

intermediate

Typical multi-level cache architecture components

In a multi-level caching system, which component is usually responsible for the slowest but largest storage?

A Persistent Storage (e.g., SSD or HDD)

B L1 Cache

C Main Memory (RAM)

D L2 Cache

💡 Hint

Consider the storage hierarchy from fastest to slowest.

Persistent storage like SSD or HDD is the slowest but has the largest capacity compared to caches and RAM.

❓ scaling

advanced

Scaling multi-level caching for high traffic

When scaling a multi-level caching system to handle high traffic, which approach helps reduce cache stampede (many requests missing cache simultaneously)?

A Increase cache size without changing eviction policies.

B Disable caching for popular keys to avoid stale data.

C Use cache expiration times that are all the same for every key.

D Implement request coalescing or locking to allow only one request to fetch data on a cache miss.

💡 Hint

Think about how to prevent many requests from hitting the database at once.

Request coalescing or locking ensures only one request fetches data on a cache miss, preventing many simultaneous database hits (cache stampede).

❓ tradeoff

advanced

Tradeoff in cache consistency vs. performance

In multi-level caching, what is the main tradeoff when choosing between strong consistency and high performance?

A Strong consistency always improves performance by reducing cache misses.

B High performance can be achieved by allowing some stale data, sacrificing strong consistency.

C Strong consistency requires no synchronization between cache levels.

D High performance means disabling cache invalidation mechanisms.

💡 Hint

Consider what happens if caches are not updated immediately after data changes.

Allowing some stale data improves performance by reducing synchronization overhead but sacrifices strong consistency.

❓ estimation

expert

Estimating cache hit ratio impact

A system has a two-level cache: L1 cache with 90% hit rate and L2 cache with 80% hit rate for requests missed by L1. What is the overall cache hit rate?

A 98%

B 90%

C 72%

D 82%

💡 Hint

Calculate combined hit rate: hits in L1 plus hits in L2 after L1 misses.

Overall hit rate = L1 hit rate + (L1 miss rate × L2 hit rate) = 0.9 + (0.1 × 0.8) = 0.9 + 0.08 = 0.98 (98%)

Check carefully: The question states L2 hit rate is for requests missed by L1, so combined hit rate is 98%.