The BFS (Breadth-First Search) traversal of a graph will result into a Queue. So, the answer is c) Queue.

Here's why:

1. BFS starts traversal from the root node (or any arbitrary node of a graph, also termed as the 'source node') and visits nodes in a level by level manner (i.e., visiting the ones closest to the root first).

2. It uses a Queue data structure to remember to get the next vertex to start the search when a dead end occurs in any iteration.

3. We insert the source node into the queue. Then we start a loop until the queue is empty. In each iteration of the loop, we remove a vertex from the queue and insert all its not yet visited neighbors into the queue.

4. As a result, we get a queue that represents the BFS traversal of the graph.

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The BFS (Breadth-First Search) traversal of a graph will result into a Queue. So, the answer is c) Queue.

Here's why:

1. BFS starts traversal from the root node (or any arbitrary node of a graph, also termed as the 'source node') and visits nodes in a level by level manner (i.e., visiting the ones closest to the root first).

2. It uses a Queue data structure to remember to get the next vertex to start the search when a dead end occurs in any iteration.

3. We insert the source node into the queue. Then we start a loop until the queue is empty. In each iteration of the loop, we remove a vertex from the queue and insert all its not yet visited neighbors into the queue.

4. As a result, we get a queue that represents the BFS traversal of the graph.

Knowee AI · Accepted Answer

The BFS (Breadth-First Search) traversal of a graph will result into a Queue. So, the answer is c) Queue.

Here's why:

1. BFS starts traversal from the root node (or any arbitrary node of a graph, also termed as the 'source node') and visits nodes in a level by level manner (i.e., visiting the ones closest to the root first).

2. It uses a Queue data structure to remember to get the next vertex to start the search when a dead end occurs in any iteration.

3. We insert the source node into the queue. Then we start a loop until the queue is empty. In each iteration of the loop, we remove a vertex from the queue and insert all its not yet visited neighbors into the queue.

4. As a result, we get a queue that represents the BFS traversal of the graph.

The BFS search traversal of a graph will result into?a)Stackb)Linked listc)Queued)Tree

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