WGU Scripting-and-Programming-Foundations Exam With Confidence Using Practice Dumps

The loop variable update statement is responsible for changing the loop variable’s value after each iteration of the loop, ensuring that the loop progresses and eventually terminates. In the options provided, J = j + 3 is the statement that updates the loop variable j by adding 3 to its current value. This is a typical update statement found in loops, particularly in ‘for’ or ‘while’ loops, where the loop variable needs to be changed systematically to avoid infinite loops.

References: This understanding of loop variable update statements is based on fundamental programming concepts that are taught in introductory programming courses and documented in programming language specifications1234. These principles are applied consistently across various programming languages.

In Agile methodology, the phase that typically results in a list of objects to be written is the Design phase. This phase involves creating the architecture and design of the system to be developed. It’s during this stage that developers and designers collaborate to identify the necessary objects and components that will make up the system, and how they will interact with each other. The output of this phase is often a set of design documents, diagrams, and a list of objects or components that need to be implemented in subsequent phases.

References: The Agile Development Lifecycle outlines various stages where different activities take place. The Design phase is crucial for setting a clear path for implementation and ensuring that the team has a shared understanding of the system’s architecture12. Additionally, Agile methodologies like Scrum and Extreme Programming (XP) emphasize the importance of design and architecture in their practices3.

 Dijkstra’s shortest path algorithm is designed to find the shortest path between nodes in a graph. This can be applied to various scenarios, such as routing problems, network optimization, and in this case, determining the most fuel-efficient flight plan. The algorithm works by iteratively selecting the unvisited vertex with the smallest tentative distance from the source, then visiting the neighbors of this vertex and updating their tentative distances if a shorter path is found. This process continues until the destination vertex is reached or all reachable vertices have been visited.

In the context of the given options, Dijkstra’s algorithm is best suited for option B, where the goal is to find the most fuel-efficient path (i.e., the shortest path) between multiple points (coordinates of five positions). The algorithm is not designed to solve problems like searching for an element in an array (option A), finding the longest common subsequence (option C), or searching for a name in a list (option D).

References:

• GeeksforGeeks article on Dijkstra’s Algorithm1
• Wikipedia page on Dijkstra’s Algorithm2
• Programiz explanation of Dijkstra’s Algorithm3