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Big O and Heuristics: 11th Grade Algorithmic Synthesis Quiz (Advanced) Hoja de trabajo • Descarga gratuita en PDF con clave de respuestas

Logic-driven students analyze 10 complex scenarios involving Dijkstra's algorithm, O(log n) optimization, and memoization to solve computational bottlenecks.

Panorama pedagógico

This algorithmic synthesis quiz assesses critical thinking in computer science, specifically focusing on Big O notation, heuristic problem-solving, and efficient data structures. The assessment utilizes a scenario-based approach to challenge students to apply abstract computational concepts to real-world engineering bottlenecks. It serves as an ideal summative assessment for high school computer science students preparing for AP Computer Science A or introductory college-level coursework.

Big O and Heuristics: 11th Grade Algorithmic Synthesis Quiz - arts-and-other 11 Quiz Worksheet - Page 1
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Big O and Heuristics: 11th Grade Algorithmic Synthesis Quiz - arts-and-other 11 Quiz Worksheet - Page 2
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Herramienta: Cuestionario de Opción Múltiple
Asunto: Artes & Otros
Categoría: Ciencia de la computación y tecnología
Calificación: 11th Calificación
Dificultad: Avanzado
Tema: Algoritmos y resolución de problemas
Idioma: 🇬🇧 English
Elementos: 10
Clave de respuestas:
Pistas: No
Creado: Feb 14, 2026

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Qué aprenderán los estudiantes

  • Analyze time and space complexity using Big O notation to evaluate algorithmic efficiency.
  • Compare and contrast deterministic and heuristic approaches for solving NP-hard computational problems.
  • Evaluate the appropriateness of specific data structures such as stacks and graphs for given software engineering scenarios.

All 10 Questions

  1. A logistics company needs to find the shortest path for deliveries in a weighted graph where some edges represent tolls. Which algorithmic approach is most appropriate for a single-source shortest path problem without negative weight edges?
    A) Kruskal's Algorithm
    B) Dijkstra's Algorithm
    C) Breadth-First Search (BFS)
    D) Depth-First Search (DFS)
  2. A programmer is using Dynamic Programming to solve the Fibonacci sequence efficiently. By storing the results of expensive function calls, they are utilizing a technique called _______.
    A) Recursion
    B) Backtracking
    C) Memoization
    D) Abstraction
  3. An algorithm with a time complexity of O(2^n) is considered more efficient for large datasets than an algorithm with O(n^2) complexity.
    A) True
    B) False
Show all 10 questions
  1. When designing a search feature for a massive, pre-sorted global database of Social Security numbers, which algorithm provides the best worst-case time complexity?
    A) Linear Search
    B) Jump Search
    C) Binary Search
    D) Selection Search
  2. The 'Divide and Conquer' paradigm involves breaking a problem into independent subproblems, solving them, and then combining their solutions.
    A) True
    B) False
  3. To solve the 'Traveling Salesperson Problem' for 500 cities timely, a developer must use a _______ algorithm, which provides a 'good enough' solution rather than the absolute optimum.
    A) Recursive
    B) Brute-force
    C) Heuristic
    D) Deterministic
  4. You are auditing a program that uses nested loops to compare every element in an array of size 'n' with every other element. What is the Big O complexity of this operation?
    A) O(1)
    B) O(n)
    C) O(log n)
    D) O(n^2)
  5. In the context of problem decomposition, creating a high-level overview of an algorithm using a mix of natural language and code structures is known as _______.
    A) Syntax
    B) Pseudocode
    C) Compilation
    D) Scripting
  6. Space complexity refers solely to the amount of permanent hard drive storage an algorithm requires to run.
    A) True
    B) False
  7. A developer is implementing a 'Undo' feature in a text editor. Which data structure is most efficient for managing the history of changes to allow for the 'last-in, first-out' (LIFO) retrieval of states?
    A) Queue
    B) Stack
    C) Binary Tree
    D) Linked List

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Grade 11 Computer ScienceAlgorithmic ThinkingBig O NotationData StructuresAdvanced Coding LogicFormative AssessmentHigh School Technology
This advanced 11th-grade computer science assessment evaluates student proficiency in algorithmic analysis and computational logic. The 10-question quiz covers diverse topics including Dijkstra's algorithm, O(log n) efficiency, memoization in dynamic programming, and heuristic solutions for NP-hard problems. Question formats include multiple-choice, fill-in-the-blank, and true-false items, each accompanied by detailed logical explanations. This resource is designed to bridge the gap between basic coding syntax and high-level algorithmic synthesis, emphasizing the LIFO principle of stacks and the divide and conquer paradigm.

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Preguntas Frecuentes

Yes, this Algorithmic Synthesis Quiz is an excellent no-prep computer science sub-plan because it provides clear explanations for each answer, allowing students to self-correct during independent study.

Most 11th-grade students will take approximately 20 to 30 minutes to complete this advanced computer science quiz, depending on their prior familiarity with graph theory and memoization.

This Computer Science Quiz can support differentiated instruction by using the detailed explanations as a teaching tool for students who need more scaffolding while the complex scenarios challenge high-achieving learners.

This Algorithmic Synthesis Quiz is specifically designed for 11th-grade students or advanced placement learners who have a strong foundation in logic and programming fundamentals.

You can use this Computer Science Quiz as a formative assessment after a unit on efficiency to identify which students struggle with the nuances of logarithmic versus polynomial growth.

Big O and Heuristics: 11th Grade Algorithmic Synthesis Quiz - Free Advanced Quiz Worksheet | Sheetworks