CIE-1_DSA:Unit1&Unit2

What is a data structure?

• A way of organizing and storing data

• A programming language

• A type of algorithm

• A hardware component

Which of the following is not a characteristic of an algorithm?

• It must be ambiguous

• It must take zero or more inputs.

• It must terminate after a finite number of steps.

• It must produce one or more outputs.

What is the difference between primitive and non-primitive data structures?

• Primitive are basic types, non-primitive are derived

• Primitive are complex, non-primitive are simple

• Primitive use more memory, non-primitive use less

• There is no difference

A data structure that has a fixed size determined at compile-time is known as

• Transient data structure

• Dynamic data structure

• Static data structure

• Persistent data structure

What does 'Time Complexity' of an algorithm measure?

• The number of lines of code in the algorithm.

• The amount of time taken by the algorithm to run as a function of the input size.

• The amount of memory required to run the algorithm.

• The time it takes for a human to write the algorithm.

What is a 'Sparse Matrix'?

• A matrix that can be used only for matrix multiplication

• A matrix where most of the elements are zero.

• A matrix where all elements are non-zero.

• A matrix with a very large number of rows and columns.

A data structure is called 'dynamic' if:

• It is created at compile time.

• It can be accessed using a single index.

• Its elements are stored in contiguous memory locations.

• Its size can change during runtime.

What is the time complexity of adding two sparse matrices represented using a triplet representation?

• O(N2)

• O(N + M), where N and M are the number of non-zero elements in the two matrices.

• O(1)

• O(N*M)

What is a persistent data structure in Java?

• An immutable data structure that preserves previous versions

• A mutable data structure

• A type of array

• A structure that cannot be modified

Compute the time complexity for folloing code:

for(int i=0; i<n; i=i*2)

{

System.out.println( i );

}

• O(n)

• O(n2)

• O(log n)

• O(2n)