What is the best that you can say about the average runtime of an insert operation in a hash table using chaining as a collision resolution scheme when the table is less than 90% full? Select one: а. O( (1 + 1/(1-n/m)2)/2) b. O( log n ) с. О(1) d. O(n) Your answer is incorrect.

Consider a hash table with m buckets and n entries. Give a tight upper bound O() for a method that returns all the keys in the maps. Do not assume any constraint on the load factor. a) O( n) b) O( m) c) O( m + n) Hint: O(n + m) is the same as O( max(m, n) ) d) O( m n)

Let us consider a hash table with an underlying array of length 5. It is initially empty, and we then add consecutively the integers: 44, 7, 45, 21, 8, 23, 0, 33 Draw the resulting hashtable after all additions are made. Assume that the hash function used is the remainder of dividing by 5, that is: def hash(d): return d%5 You do not need to consider resizing the underlying array.

Is it consistent to say that a hash table of size m always contains the same number of linked lists? I've been trying to understand the purpose of a hash function for ages. Give an illustration of your point with an example.

Can m-size linked lists be stored in a hash table? Why do we need to use a hash function, exactly? Give us a sample.

Use the hash function h(x) =x mod 11 to load the following values 25, 14, 36, 47 using separate chaining.

Give the contents of a linear-probing hash table that results when you insert thekeys E A S Y Q U T I O N in that order into an initially empty table of initial size M= 4 that is expanded with doubling whenever half full. Use the hash function 11 k % Mto transform the kth letter of the alphabet into a table index.

What is the time complexity of delete function in the hash table using a doubly linked list?a) O(1)b) O(n)c) O(log n)d) O(n log n)

how would the hash table look like ? linear probing and quadratic probing ?

What is the worst-case performance of a lookup operation in a hashmap and why? Group of answer choices A- O(1), hashmap always has a constant time lookup, and that is why we like using this associative data structure.   B- O(lg(n)) hashmap has a log(n) lookup because we are able to perform a binary search on the keys because our hashmap always maintains a sorted order of entries added.   C- O(n) because we can have a bad hash function that puts all of our items in the same bucket, thus we would have to iterate through all n items.