Queue-based task processing in Agentic AI - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

What is the main advantage of using a queue in task processing?

Imagine you have many tasks to complete one after another. Why would using a queue help in managing these tasks?

A It allows tasks to be processed in the order they arrive, ensuring fairness.

B It processes all tasks at the same time, speeding up completion.

C It randomly selects tasks to process, increasing unpredictability.

D It deletes tasks automatically after adding them to the queue.

💡 Hint

Think about how a line at a store works.

A queue processes tasks in the order they arrive (first-in, first-out), which keeps things fair and organized.

❓ Predict Output

intermediate

What is the output of this queue processing code?

Consider this Python code that simulates a simple queue processing tasks:

Agentic AI

from collections import deque
queue = deque()
queue.append('task1')
queue.append('task2')
processed = []
while queue:
    task = queue.popleft()
    processed.append(task + '_done')
print(processed)

A ['task2_done', 'task1_done']

B ['task1_done', 'task2_done']

C ['task1', 'task2']

D []

💡 Hint

Remember, popleft() removes items from the front of the queue.

The code processes tasks in the order they were added, appending '_done' to each.

❓ Model Choice

advanced

Which model architecture best suits queue-based task processing in AI agents?

You want an AI agent to handle tasks arriving in a queue and decide the next best task to process based on priority and dependencies. Which model architecture fits best?

A Recurrent Neural Network (RNN) to remember past tasks and predict next tasks.

B Convolutional Neural Network (CNN) to analyze image data of tasks.

C Transformer model with attention to weigh task priorities and dependencies.

D Simple linear regression to predict task completion time.

💡 Hint

Think about models that handle sequences and relationships well.

Transformers use attention mechanisms to focus on important parts of input, ideal for weighing task priorities and dependencies in a queue.

❓ Hyperparameter

advanced

Which hyperparameter adjustment improves queue task processing throughput?

You have an AI system processing tasks from a queue. You want to increase throughput without losing accuracy. Which hyperparameter change helps most?

A Reduce number of layers to simplify the model.

B Decrease learning rate to slow down training.

C Increase dropout rate to prevent overfitting.

D Increase batch size to process more tasks at once.

💡 Hint

Think about how processing multiple tasks together affects speed.

Increasing batch size allows processing more tasks simultaneously, improving throughput.

🔧 Debug

expert

Why does this queue processing code cause a runtime error?

Review this Python code snippet for processing tasks in a queue. It raises an error when run. What is the cause?

Agentic AI

tasks = ['task1', 'task2', 'task3']
queue = []
for task in tasks:
    queue.append(task)
while queue:
    current = queue.pop(0)
    if current == 'task1':
        queue.remove('task3')
    if current == 'task2':
        queue.remove('task3')
    print(f'Processed {current}')

A Removing 'task3' while iterating causes a ValueError if 'task3' is not in the queue.

B Using pop(0) on a list is not allowed and causes an AttributeError.

C The queue is empty before the loop starts, causing an IndexError.

D The print statement syntax is invalid and causes a SyntaxError.

💡 Hint

Consider what happens when you remove an item from a list that might not be there.

When 'task2' is processed, the code tries to remove 'task3'. If 'task3' was already removed, this causes a ValueError.