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

50
Questions
60 min
Time Limit
80.00%
Passing Score

📚 Algorithms Topics to Study (57)

✍️ Sample Algorithms Questions & Answers

1. The Dijkstra Algorithm has the following applications:
All of the above

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?
Bellman-Ford Algorithm

The Bellman-Ford algorithm detects negative-weight cycles by checking whether any edge can still be relaxed after n−1 iterations.

3. A risk has a 5% probability of occurring and would cause $200,000 in damages. What is the Annual Loss Expectancy (ALE)?
$10,000

ALE = Single Loss Expectancy × Annual Rate of Occurrence = $200,000 × 0.05 = $10,000.

4. Which algorithm is used in practice for string pattern matching with O(n+m) time complexity?
KMP (Knuth-Morris-Pratt)

KMP preprocesses the pattern to build a failure function, allowing O(n+m) worst-case string matching.

5. What is a 'regression test' in software quality assurance?
A test re-run after changes to confirm existing features still work

Regression tests re-execute previously passing tests after a code change to ensure new modifications haven't broken existing functionality.

6. What is the primary purpose of a 'model card' in the context of algorithmic compliance?
To document a model's intended uses, limitations, and fairness metrics

Model cards provide structured documentation of a machine learning model's performance, intended use cases, limitations, and ethical considerations.

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Your Algorithms Study Path
1. Learn with Flashcards → 2. Drill Practice Tests → 3. Take the Full Exam Simulation
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