How does an increasing load factor generally impact the performance of a hashmap?
It depends on the specific hash function being used.
It has no significant impact on performance.
It degrades performance due to a higher probability of collisions.
It improves performance by reducing memory usage.
In the context of hashmaps, what does 'probing' refer to?
Searching for a specific key in the hashmap.
Resizing the underlying array to accommodate more keys.
Determining the load factor of the hashmap.
Finding an alternative slot for a key when a collision occurs.
Which collision resolution technique involves using a second, independent hash function to compute the probe sequence?
Double Hashing
Separate Chaining
Linear Probing
Quadratic Probing
In the context of hash functions, what does the avalanche effect refer to?
A small change in input causing a significant change in output
Increased likelihood of hash collisions with larger datasets
Uneven distribution of keys within the hash table
Gradual degradation of hash performance over time
Which collision resolution strategy is generally preferred for hash tables with open addressing when the load factor is low?
How does universal hashing enhance the robustness of hash tables?
By minimizing the impact of hash collisions on retrieval time
By dynamically adjusting the hash function to the input data
By ensuring a uniform distribution of keys across the hash table
By eliminating the possibility of hash collisions entirely
What is the primary motivation behind designing hash functions with a uniform distribution property?
To minimize the occurrence of hash collisions and improve efficiency
To reduce the memory footprint of the hash table
To maximize the amount of data that can be stored in the hash table
To simplify the implementation of the hash function itself
In a system where memory usage is a major concern, what trade-off should be considered when using a hashmap?
A larger hash table size generally results in faster lookups but consumes more memory.
Collision resolution strategies have no impact on memory consumption.
Using a complex hash function always reduces collisions and memory usage.
Hashmaps always use less memory than arrays for storing the same data.
How does the choice of a hash function impact the performance of a hashmap?
A simple hash function is always preferred as it reduces computational overhead.
The hash function has a negligible impact on performance compared to the data structure itself.
A well-chosen hash function minimizes collisions, leading to faster lookups and insertions.
A complex hash function guarantees a lower collision rate, improving performance.
You are implementing an LRU (Least Recently Used) cache. Which data structure, in conjunction with a hashmap, is most suitable for tracking the usage order of cached items?
Stack
Queue
Doubly Linked List
Binary Tree