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Doctor of Philosophy in Computer Science (PhD CS) Admission - Bahria University

PhD Computer Science

الأسئلة

المدة

درجة النجاح

50%

خصم الإجابة الخاطئة

—

الفئة

Bahria University

رقم القضية

—

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Exam Questions

O(1)

O(log n)

O(n)

O(n log n)

ExplanationIn a Fibonacci heap, the delete-min operation involves consolidating the root list, which takes amortized O(log n) time.

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Dijkstra's Algorithm

Bellman-Ford Algorithm

Prim's Algorithm

Kruskal's Algorithm

ExplanationDijkstra fails with negative edges, while Bellman-Ford can handle them correctly by relaxing edges V-1 times.

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m/2

ceil(m/2)

floor(m/2)

m-1

ExplanationTo maintain the balance of a B-tree, non-root internal nodes must be at least half full, defined as ceil(m/2) children.

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Binary Max-Heap

Pairing Heap

Van Emde Boas Tree

Sorted Linked List

ExplanationVan Emde Boas trees provide O(log log U) time complexity for priority queue operations, where U is the universe size, outperforming standard heaps for certain integer ranges.

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O(n log n)

O(n)

O(n^2)

O(log n)

ExplanationWhen the pivot is consistently extreme, the partition becomes highly unbalanced, resulting in a depth of n, leading to O(n^2) complexity.

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Dijkstra's Algorithm

Kruskal's Algorithm

Bellman-Ford Algorithm

Floyd-Warshall Algorithm

ExplanationKruskal's algorithm sorts all edges by weight and adds them sequentially if they do not form a cycle, which is a greedy strategy for MST.

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Guaranteed O(1) search time

Lower space complexity

Easier implementation for concurrent operations

Smaller constant factors

ExplanationSkip lists are highly preferred in concurrent programming because they allow localized updates, avoiding the global rebalancing locks required by trees like AVL or Red-Black trees.

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Greedy Algorithm

Dynamic Programming

Backtracking

Divide and Conquer

ExplanationDynamic programming is used to solve the Knapsack problem by breaking it down into overlapping subproblems and storing results to build the optimal solution.

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The recurrence must be in the form T(n) = aT(n/b) + f(n)

The subproblems must be solved sequentially

The function f(n) must be logarithmic

The problem must be non-recursive

ExplanationThe Master Theorem provides a cookbook solution for recurrences of the specific form T(n) = aT(n/b) + f(n).

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log2(n+1)

2 log2(n+1)

log2(n)

ExplanationDue to the balancing properties (red nodes cannot have red children, paths have equal black nodes), the height is guaranteed to be at most 2 log2(n+1).

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Doctor of Philosophy in Computer Science (PhD CS) Admission - Bahria University paper pattern infographic: subject-wise question distribution across 50 MCQs, 1h duration — Doctor of Philosophy in Computer Science (PhD CS) Admission - Bahria University — subject-wise question distribution, per the official syllabus

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Doctor of Philosophy in Computer Science (PhD CS) Admission - Bahria University — Frequently Asked Questions

How many questions are in the Doctor of Philosophy in Computer Science (PhD CS) Admission - Bahria University practice test?

This practice test currently has 50 MCQs, and every question includes the correct answer with an explanation.

How long is the Doctor of Philosophy in Computer Science (PhD CS) Admission - Bahria University mock test?

The full mock runs for 1h under real exam timing, just like the actual paper.

Is there negative marking in this test?

No negative marking is applied in this practice test. The rule for the real exam is announced in the official advertisement.

What score do I need to pass?

The passing score in this simulation is 50%, but for competitive merit you should aim well above it.

Is Doctor of Philosophy in Computer Science (PhD CS) Admission - Bahria University preparation free on PrepPro?

Yes — the questions, explanations and full timed mock test are all free. You can start practising right from this page.

How do I prepare for Doctor of Philosophy in Computer Science (PhD CS) Admission - Bahria University online?

Practise the syllabus-based MCQs, then sit the full timed mock test and use your result analytics to find and repeat weak topics — all of it starts free from this page.

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