Why production readiness matters in Prompt Engineering / GenAI - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to print a message about production readiness.

Prompt Engineering / GenAI

print("Production readiness is important because it ensures [1].")

Drag options to blanks, or click blank then click option'

Acode runs fast locally

Btraining data is large

Cmodels work well in real use

Dalgorithms are complex

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing options about training data size instead of real-world use.

Focusing on code speed rather than reliability.

Production readiness means the model works well when used by real users, not just in tests.

2fill in blank

medium

Complete the code to check if a model is ready for production by testing its accuracy.

Prompt Engineering / GenAI

if model_accuracy >= [1]:
    print("Model is production ready")
else:
    print("Model needs improvement")

Drag options to blanks, or click blank then click option'

A0.8

B0.3

C0.1

D0.5

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing too low accuracy thresholds like 0.1 or 0.3.

Confusing accuracy with loss values.

A model accuracy of 0.8 or higher is often considered good enough for production use.

3fill in blank

hard

Fix the error in the code that checks if a model is production ready based on latency.

Prompt Engineering / GenAI

if model_latency [1] 100:
    print("Ready for production")
else:
    print("Too slow for production")

Drag options to blanks, or click blank then click option'

A<=

B>=

C==

D!=

Attempts:

3 left

💡 Hint

Common Mistakes

Using '>=' which means latency is too high.

Using '==' which is too strict.

Latency should be less than or equal to 100 milliseconds to be production ready.

4fill in blank

hard

Fill both blanks to create a dictionary of model metrics only if accuracy is above threshold and latency is below limit.

Prompt Engineering / GenAI

metrics = {"accuracy": [1], "latency": [2] if accuracy [3] 0.75 and latency [4] 150 else {}

Drag options to blanks, or click blank then click option'

Aaccuracy

Blatency

C>

D<

Attempts:

3 left

💡 Hint

Common Mistakes

Swapping comparison operators.

Using wrong variable names.

We include accuracy and latency in metrics if accuracy is greater than 0.75 and latency is less than 150.

5fill in blank

hard

Fill all three blanks to create a function that returns True if model is production ready based on accuracy, latency, and error rate.

Prompt Engineering / GenAI

def is_ready(accuracy, latency, error_rate):
    return accuracy [1] 0.85 and latency [2] 100 and error_rate [3] 0.05

Drag options to blanks, or click blank then click option'

A>=

B<=

C<

D>

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong comparison signs for latency or error rate.

Confusing greater than and less than.

Model is ready if accuracy >= 0.85, latency <= 100, and error_rate < 0.05.