Cost optimization strategies in Agentic Ai - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 conceptual

intermediate

Understanding Cost Drivers in Machine Learning

Which of the following is the most significant factor that increases the cost of training a machine learning model in a cloud environment?

A Using a simple linear regression model instead of a deep neural network

B Reducing the number of training epochs

C Increasing the size of the training dataset significantly

D Using pre-trained models without fine-tuning

❓ model choice

intermediate

Choosing Models for Cost Efficiency

You want to deploy a model for real-time predictions with minimal cost. Which model choice is best to reduce inference cost while maintaining reasonable accuracy?

A A support vector machine with a complex kernel

B A large deep neural network with many layers

C An ensemble of multiple complex models

D A small decision tree model

❓ hyperparameter

advanced

Hyperparameter Tuning and Cost Impact

Which hyperparameter adjustment is most likely to reduce training cost without severely impacting model performance?

A Increasing the number of training epochs

B Increasing batch size significantly

C Using a very low learning rate

D Adding more layers to the model

❓ metrics

advanced

Evaluating Cost vs. Accuracy Trade-offs

You have two models: Model A costs $100 to train and achieves 90% accuracy. Model B costs $150 to train and achieves 92% accuracy. Which metric best helps decide if the extra cost is justified?

A Cost per percentage point of accuracy improvement

B Accuracy alone

C Training time only

D Number of model parameters

🔧 debug

expert

Identifying Cost Inefficiency in Training Code

Given the code below for training a model, which line causes unnecessary cost increase by repeating data loading every epoch?

for epoch in range(10):
    data = load_data_from_disk()
    model.train(data)

A data = load_data_from_disk()

B None, the code is optimal

C model.train(data)

D for epoch in range(10):