Challenge - 5 Problems

🎖️

Human Approval Workflow Master

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Test your skills under time pressure!

🧠 conceptual

intermediate

1:30remaining

What is the main purpose of a human approval workflow in AI systems?

Human approval workflows are often integrated into AI systems. What is their primary role?

ATo automatically retrain AI models without human input

BTo replace AI models with human decision-making entirely

CTo allow humans to review and approve AI decisions before final action is taken

DTo speed up AI predictions by skipping human checks

Attempts:

2 left

❓ model choice

intermediate

1:30remaining

Which AI model type best supports human approval workflows for text classification?

You want an AI model that can provide confidence scores to help humans decide when to approve or review text classification results. Which model type is best?

ADeterministic rule-based model without confidence scores

BUnsupervised clustering model without labels

CGenerative model that creates new text samples

DProbabilistic model that outputs confidence probabilities

Attempts:

2 left

❓ metrics

advanced

2:00remaining

How does adding a human approval step affect the overall accuracy metric of an AI system?

An AI system has 85% accuracy alone. After adding a human approval step that corrects 10% of AI errors, what is the new effective accuracy?

A90% because human approval fixes some AI mistakes

B85% because human approval does not change AI accuracy

C75% because human approval slows down the system

D95% because human approval corrects all AI errors

Attempts:

2 left

🔧 debug

advanced

2:00remaining

Identify the error in this human approval workflow code snippet

Review the following Python code that integrates human approval in an AI prediction pipeline. What error will it cause?

Agentic_ai

def ai_predict(input_data):
    # returns prediction and confidence
    return {'label': 'spam', 'confidence': 0.7}

def human_approval(prediction):
    if prediction['confidence'] < 0.8:
        return input('Approve prediction? (yes/no): ')
    else:
        return 'yes'

result = ai_predict('email text')
approved = human_approval(result)
if approved == 'yes':
    print('Final label:', result['label'])
else:
    print('Prediction rejected')

AThe input() function will cause a runtime error in non-interactive environments

BThe ai_predict function returns a list instead of a dictionary

CThe confidence key is missing in the prediction dictionary

DThe human_approval function always returns None

Attempts:

2 left

❓ hyperparameter

expert

2:30remaining

Which hyperparameter adjustment best balances AI autonomy and human approval workload?

In a human approval workflow, the AI model outputs a confidence threshold to decide when to request human review. Which threshold setting best balances reducing human workload while maintaining safety?

ASet threshold very low (e.g., 0.1) to request human review on almost all predictions

BSet threshold very high (e.g., 0.95) to request human review only on very uncertain predictions

CSet threshold at 0.5 to request human review on half of predictions randomly

DDisable threshold and have humans review all predictions

Attempts:

2 left