Algorithms Study Guide 2026
Everything you need to pass the Algorithms exam in one place: the exam format, every topic to study, real practice questions with explanations, flashcards, and full-length practice tests. Free, no sign-up needed.
📋 Algorithms Exam Format at a Glance
📚 Algorithms Topics to Study (21)
✍️ Sample Algorithms Questions & Answers
1. The Dijkstra Algorithm has the following applications:
Dijkstra's algorithm is a fundamental algorithm with wide-ranging applications due to its ability to find the shortest paths between nodes in a graph with non-negative edge weights. Its uses include general shortest path finding in various networks, optimizing traffic flow in transportation systems, and determining efficient routing protocols in computer networks. Therefore, it is applicable to all the listed scenarios.
2. Which algorithm can detect negative-weight cycles in a graph?
The Bellman-Ford algorithm detects negative-weight cycles by checking whether any edge can still be relaxed after n−1 iterations.
3. Which sorting algorithm is most suitable for sorting linked lists efficiently?
MergeSort is ideal for linked lists because merging two sorted lists requires only pointer updates, with no random access needed.
4. How should Algorithms professionals handle conflicts with stakeholders?
This is fundamental to Algorithms practice. Address issues professionally through active listening, finding common ground, and seeking resolution represents the professional standard for communication in the Algorithms certification framework.
5. What are the three different types of algorithm constructs?
The three fundamental control structures or constructs in algorithms are sequence, selection, and iteration. Sequence refers to the execution of instructions one after another in a linear fashion. Selection involves making decisions based on conditions (e.g., if-else statements), while iteration allows for repeating a block of instructions multiple times (e.g., loops).
6. The upper bound is defined by this algorithm analysis.
Big O notation (O) is used in algorithm analysis to describe the upper bound of an algorithm's running time or space complexity. It provides a worst-case scenario estimate, indicating the maximum amount of time or space an algorithm will take as the input size grows. This helps in understanding how an algorithm scales and performs under the most demanding conditions.