Caching basics Quiz

Test your understanding of caching fundamentals for inference results, including cache keys, model versions, time-to-live (TTL), and differences between client-side and server-side caching. This easy quiz will help reinforce best practices and key concepts in caching strategies.

1. Basic definition of caching

   What is the main purpose of caching inference results in an application?

   1. To permanently delete obsolete data
   2. To encrypt all data before transmission
   3. To store previously computed outputs for faster future access
   4. To sequence data requests alphabetically

2. Cache key components

   Which of the following elements should typically be included in a cache key for model inference results?

   1. Just the server IP address
   2. Only the current date
   3. Randomly generated numbers
   4. Model name and version, input data, and user identifier

3. Effect of model versioning

   If a cache key does not include the model version, what might happen when the model is updated?

   1. The cache will automatically reset
   2. Old results may be falsely returned for new model versions
   3. Cache will stop storing any data
   4. All computation results will double in speed

4. Understanding TTL

   What does TTL (Time To Live) refer to in caching for inference results?

   1. The maximum duration a cached result is considered valid
   2. The timestamp of the last server reboot
   3. The size limit for cache entries
   4. Total transfer latency for input data

5. Cache refresh mechanism

   When the TTL for a cached result expires, what typically happens?

   1. It gets encrypted again
   2. The cached entry is invalidated and recomputed if needed
   3. The cache entry is silently ignored forever
   4. It turns into permanent storage

6. Client-side caching scenario

   If a web browser stores inference results locally, what type of caching is this?

   1. Database replication
   2. Server-based caching
   3. Client-side caching
   4. Global cache synchronization

7. Server-side caching definition

   What describes server-side caching in the context of inference results?

   1. Results are synced through USB drives
   2. Cache is kept only on the user's personal device
   3. Results are stored on the application server for all clients
   4. Each device stores its own results

8. Unique cache keys per request

   Why is it important to ensure cache keys are unique for different requests?

   1. To use more server memory
   2. To prevent returning incorrect results from unrelated inputs
   3. To increase TTL automatically
   4. To guarantee higher network latency

9. Impact of omitting input data from cache keys

   If the input data is not part of a cache key, what issue can occur?

   1. TTL will not function properly
   2. The cache will never be accessed
   3. Different inputs may incorrectly share the same cached result
   4. The server will crash instantly

10. Appropriate TTL setting

    Which TTL value would be most appropriate for frequently changing inference models?

    1. A TTL of 5 years
    2. An unlimited TTL
    3. A shorter TTL, such as 1-5 minutes
    4. No TTL at all

11. Cache hit versus miss

    What is a cache hit in the context of inference result caching?

    1. When the user refreshes their browser
    2. When a requested inference result is found in the cache and returned
    3. When the cache is too full to store results
    4. When two clients exchange cache entries

12. Best practices for cache invalidation

    Which action is a best practice for invalidating cached inference results when a model is updated?

    1. Shorten input data
    2. Disable caching for all users
    3. Change the model version included in the cache key
    4. Increase the cache storage size

13. Reducing redundant computation

    How does proper caching of inference results help reduce redundant computations?

    1. By always recalculating outputs every time
    2. By serving duplicate requests from cached data instead of re-computing
    3. By limiting the number of API requests
    4. By randomly dropping requests

14. Choosing between cache locations

    Which is an advantage of server-side caching over client-side caching for inference results?

    1. Server-side caching allows results to be shared among multiple users
    2. Server-side cache has no storage limitations
    3. Server caches can only be used on mobile devices
    4. Only the client can access cached data

15. Risks of stale cache

    What is a potential risk of having an excessively long TTL on cached inference results?

    1. Clients may receive outdated or incorrect results
    2. Input data will be randomly altered
    3. Network connections become unstable
    4. All inference models will crash