C949 Data Structures and Algorithms I Exam 2026/2027 – 70+ Questions & Answers | Big O, Sorting, Searching, Trees & Graphs | Western Governors University (WGU)

The C949 Data Structures and Algorithms I 2026/2027 exam study guide is a focused 12-page question-and-answer resource covering 70+ exam concepts, definitions, algorithms, complexity rules, and programming principles. The material begins with fundamental characteristics of algorithms—including finiteness, definiteness, modularity, maintainability, robustness, and extensibility—before progressing into searching, sorting, computational complexity, data structures, trees, graphs, object-oriented programming, memory management, and data-flow concepts. A major section concentrates on searching and sorting algorithms. Students review linear search, depth-first search (DFS), breadth-first search (BFS), bubble sort, selection sort, insertion sort, merge sort, quicksort, heap sort, radix sort, and shell sort. The material connects these algorithms with their stated time complexities, including O(V+E), O(N²), O(N log N), and O(nk), while also distinguishing sorting approaches suited to smaller and larger datasets. Quicksort concepts such as midpoint and pivot values are included alongside the midpoint formula. The guide provides extensive review of Big O notation and algorithm efficiency, covering constant O(1), logarithmic O(log n), linear O(n), log-linear O(n log n), quadratic O(n²), exponential O(2ⁿ), and factorial O(n!) complexity. It also includes simplification examples involving dominant terms and presents an ordered progression of common Big O complexities, making the material useful for students practicing runtime analysis and comparing algorithm performance. The data structures portion covers arrays, linked lists, stacks, queues, trees, graphs, hash tables, sets, heaps, abstract data types (ADTs), and enumeration. Python-related implementations are also addressed, including lists as an array equivalent, dictionaries for hash tables, and or lists for stacks. Tree concepts include binary trees, binary search trees, full, complete and perfect binary trees, plus preorder, postorder, and inorder traversal. Graph material includes vertices and edges, DFS, BFS, and Dijkstra's shortest-path algorithm. Later sections extend into programming-language and software-design concepts, including static and dynamic variables, strongly and weakly typed languages, classes, inheritance, polymorphism, garbage collection, memory allocation, and data-flow diagrams. Processes and data stores within data-flow diagrams are also defined, providing additional review of how information is represented and transformed within software systems. Academic and Exam Relevance This material is particularly relevant to Western Governors University students studying C949 Data Structures and Algorithms I, as well as computer science and software-development students who need concentrated practice with algorithm analysis and foundational data structures. Its combination of definitions, complexity notation, sorting and searching methods, tree traversal, graphs, ADTs, Python representations, and object-oriented concepts makes it suitable for exam revision and rapid concept recall. The underlying subject matter aligns with standard undergraduate computer science literature. A useful academic reference is Cormen, Leiserson, Rivest, and Stein, Introduction to Algorithms, which provides extensive treatment of algorithm analysis, asymptotic notation, sorting, data structures, graph algorithms, and shortest-path problems. Another relevant reference is Goodrich, Tamassia, and Goldwasser, Data Structures and Algorithms in Python, which connects algorithm analysis with practical implementations of arrays, linked structures, stacks, queues, trees, priority queues, maps, hash tables, sorting, searching, and graphs. Relevant students: WGU C949 students, WGU Computer Science students, software engineering students, computer programming students, data structures and algorithms students, Python programming students, students preparing for algorithm-analysis assessments, and learners reviewing Big O notation, sorting algorithms, searching algorithms, trees, graphs, hash tables, and object-oriented programming. Academic references: Cormen, T. H., Leiserson, C. E., Rivest, R. L., & Stein, C. Introduction to Algorithms. MIT Press. Goodrich, M. T., Tamassia, R., & Goldwasser, M. H. Data Structures and Algorithms in Python. Wiley. 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