WGU C949 DATA STRUCTURES AND ALGORITHMS I EXAM NEWLY UPDATED
COLLECTION OF EXAM QUESTIONS AND VERIFIED ANSWERS DESIGNED TO
GUARANTEE YOUR SUCCESS ON THE LATEST TEST VERSION
1. What is a data structure? A data structure is a way of organising and
storing data in a computer so that it can be accessed and modified efficiently.
2. What is an algorithm? An algorithm is a step-by-step procedure or formula
for solving a problem or completing a task.
3. What is Big O notation? Big O notation describes the upper bound of the
time or space complexity of an algorithm in terms of input size.
4. What does O(1) mean? O(1) means constant time complexity - the operation
takes the same amount of time regardless of input size.
5. What does O(n) mean? O(n) means linear time complexity - the time grows
proportionally with the input size.
6. What does O(n²) mean? O(n²) means quadratic time complexity - the time
grows proportionally to the square of the input size.
7. What is the difference between time complexity and space complexity?
Time complexity measures how runtime grows with input size, while space
complexity measures how memory usage grows with input size.
8. What is an abstract data type (ADT)? An ADT is a theoretical concept that
defines a data type by its behavior (operations) rather than its implementation.
9. What is the difference between an ADT and a data structure? An ADT is
a theoretical model defining operations, while a data structure is the concrete
implementation of that model.
10. What is a primitive data type? A primitive data type is a basic data type
provided by a programming language, such as int, float, char, or boolean.
,11. What is asymptotic analysis? Asymptotic analysis evaluates algorithm
performance for large input sizes, ignoring constants and lower-order terms.
12. Which is faster: O(log n) or O(n)? O(log n) is faster than O(n) for large
inputs.
13. What does O(n log n) typically represent? O(n log n) typically represents
efficient sorting algorithms like merge sort or heap sort.
14. What is the best case time complexity? The best case is the minimum time
an algorithm takes for the most favorable input.
15. What is the worst case time complexity? The worst case is the maximum
time an algorithm takes for the least favorable input.
16. What is average case time complexity? The average case is the expected
time an algorithm takes over all possible inputs.
17. Why do we typically focus on worst case analysis? Worst case analysis
provides a guaranteed upper bound on performance, which is important for
reliability.
18. What is a recursive algorithm? A recursive algorithm is one that solves a
problem by calling itself with smaller instances of the same problem.
19. What are the two main components of a recursive function? A base case
(stopping condition) and a recursive case (where the function calls itself).
20. What is iteration? Iteration is the repeated execution of a set of instructions
using loops.
21. What is the space complexity of a recursive algorithm? The space
complexity includes the call stack, which grows with the recursion depth.
22. What is memoization? Memoization is an optimization technique that
stores results of expensive function calls and returns cached results when the
same inputs occur again.
23. What is dynamic programming? Dynamic programming is an algorithmic
technique that solves complex problems by breaking them down into simpler
subproblems and storing their solutions.
24. What is a greedy algorithm? A greedy algorithm makes locally optimal
choices at each step hoping to find a global optimum.
, 25. What is divide and conquer? Divide and conquer breaks a problem into
smaller subproblems, solves them recursively, and combines their solutions.
26. What is the difference between stable and unstable sorting? Stable
sorting preserves the relative order of equal elements; unstable sorting does not.
27. What is an in-place algorithm? An in-place algorithm transforms data
using only a small, constant amount of extra storage space.
28. What is amortized analysis? Amortized analysis averages the time
required to perform a sequence of operations over all operations performed.
29. What is a heuristic? A heuristic is a practical approach to problem-solving
that may not be optimal but provides a good enough solution quickly.
30. What is the significance of log base in Big O notation? In Big O notation,
the base of logarithm doesn't matter because they differ by a constant factor,
which is ignored.
Section 2: Arrays (Questions 31-60)
31. What is an array? An array is a data structure that stores elements of the
same type in contiguous memory locations.
32. What is the time complexity of accessing an element in an array? O(1) -
constant time, because elements can be accessed directly using their index.
33. What is the time complexity of inserting an element at the end of an
array (with available space)? O(1) - constant time.
34. What is the time complexity of inserting an element at the beginning of
an array? O(n) - linear time, because all existing elements must be shifted.
35. What is the time complexity of deleting an element from the end of an
array? O(1) - constant time.
36. What is the time complexity of searching for an element in an unsorted
array? O(n) - linear time, requiring examination of each element.
37. What is the time complexity of searching in a sorted array using binary
search? O(log n) - logarithmic time.
38. What is a dynamic array? A dynamic array automatically resizes when it
runs out of space, typically doubling its capacity.
COLLECTION OF EXAM QUESTIONS AND VERIFIED ANSWERS DESIGNED TO
GUARANTEE YOUR SUCCESS ON THE LATEST TEST VERSION
1. What is a data structure? A data structure is a way of organising and
storing data in a computer so that it can be accessed and modified efficiently.
2. What is an algorithm? An algorithm is a step-by-step procedure or formula
for solving a problem or completing a task.
3. What is Big O notation? Big O notation describes the upper bound of the
time or space complexity of an algorithm in terms of input size.
4. What does O(1) mean? O(1) means constant time complexity - the operation
takes the same amount of time regardless of input size.
5. What does O(n) mean? O(n) means linear time complexity - the time grows
proportionally with the input size.
6. What does O(n²) mean? O(n²) means quadratic time complexity - the time
grows proportionally to the square of the input size.
7. What is the difference between time complexity and space complexity?
Time complexity measures how runtime grows with input size, while space
complexity measures how memory usage grows with input size.
8. What is an abstract data type (ADT)? An ADT is a theoretical concept that
defines a data type by its behavior (operations) rather than its implementation.
9. What is the difference between an ADT and a data structure? An ADT is
a theoretical model defining operations, while a data structure is the concrete
implementation of that model.
10. What is a primitive data type? A primitive data type is a basic data type
provided by a programming language, such as int, float, char, or boolean.
,11. What is asymptotic analysis? Asymptotic analysis evaluates algorithm
performance for large input sizes, ignoring constants and lower-order terms.
12. Which is faster: O(log n) or O(n)? O(log n) is faster than O(n) for large
inputs.
13. What does O(n log n) typically represent? O(n log n) typically represents
efficient sorting algorithms like merge sort or heap sort.
14. What is the best case time complexity? The best case is the minimum time
an algorithm takes for the most favorable input.
15. What is the worst case time complexity? The worst case is the maximum
time an algorithm takes for the least favorable input.
16. What is average case time complexity? The average case is the expected
time an algorithm takes over all possible inputs.
17. Why do we typically focus on worst case analysis? Worst case analysis
provides a guaranteed upper bound on performance, which is important for
reliability.
18. What is a recursive algorithm? A recursive algorithm is one that solves a
problem by calling itself with smaller instances of the same problem.
19. What are the two main components of a recursive function? A base case
(stopping condition) and a recursive case (where the function calls itself).
20. What is iteration? Iteration is the repeated execution of a set of instructions
using loops.
21. What is the space complexity of a recursive algorithm? The space
complexity includes the call stack, which grows with the recursion depth.
22. What is memoization? Memoization is an optimization technique that
stores results of expensive function calls and returns cached results when the
same inputs occur again.
23. What is dynamic programming? Dynamic programming is an algorithmic
technique that solves complex problems by breaking them down into simpler
subproblems and storing their solutions.
24. What is a greedy algorithm? A greedy algorithm makes locally optimal
choices at each step hoping to find a global optimum.
, 25. What is divide and conquer? Divide and conquer breaks a problem into
smaller subproblems, solves them recursively, and combines their solutions.
26. What is the difference between stable and unstable sorting? Stable
sorting preserves the relative order of equal elements; unstable sorting does not.
27. What is an in-place algorithm? An in-place algorithm transforms data
using only a small, constant amount of extra storage space.
28. What is amortized analysis? Amortized analysis averages the time
required to perform a sequence of operations over all operations performed.
29. What is a heuristic? A heuristic is a practical approach to problem-solving
that may not be optimal but provides a good enough solution quickly.
30. What is the significance of log base in Big O notation? In Big O notation,
the base of logarithm doesn't matter because they differ by a constant factor,
which is ignored.
Section 2: Arrays (Questions 31-60)
31. What is an array? An array is a data structure that stores elements of the
same type in contiguous memory locations.
32. What is the time complexity of accessing an element in an array? O(1) -
constant time, because elements can be accessed directly using their index.
33. What is the time complexity of inserting an element at the end of an
array (with available space)? O(1) - constant time.
34. What is the time complexity of inserting an element at the beginning of
an array? O(n) - linear time, because all existing elements must be shifted.
35. What is the time complexity of deleting an element from the end of an
array? O(1) - constant time.
36. What is the time complexity of searching for an element in an unsorted
array? O(n) - linear time, requiring examination of each element.
37. What is the time complexity of searching in a sorted array using binary
search? O(log n) - logarithmic time.
38. What is a dynamic array? A dynamic array automatically resizes when it
runs out of space, typically doubling its capacity.
