Choosing the right framework in Agentic AI - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Model Choice

intermediate

Choosing a framework for real-time image classification

You want to build a real-time image classification app on a mobile device. Which framework is best suited for this task?

A TensorFlow Lite, because it is optimized for mobile and edge devices

B PyTorch Lightning, because it simplifies training on large clusters

C Scikit-learn, because it has many classical machine learning algorithms

D Keras with TensorFlow backend, because it is easy to prototype on desktop

💡 Hint

Think about frameworks optimized for mobile and low-latency inference.

TensorFlow Lite is designed specifically for running machine learning models efficiently on mobile and embedded devices, making it ideal for real-time image classification on mobile.

❓ Hyperparameter

intermediate

Selecting a framework for hyperparameter tuning

You want to perform automated hyperparameter tuning with distributed trials on a cluster. Which framework provides built-in support for this?

A OpenCV, because it processes images efficiently

B FastAI, because it has many pretrained models

C NLTK, because it is good for text preprocessing

D Ray Tune, because it supports distributed hyperparameter search

💡 Hint

Look for frameworks designed for scalable hyperparameter optimization.

Ray Tune is a framework specialized for scalable and distributed hyperparameter tuning, making it suitable for cluster environments.

❓ Metrics

advanced

Framework support for custom evaluation metrics

You need to implement a custom evaluation metric for a regression problem. Which framework allows easy integration of custom metrics during training?

A Matplotlib, because it can plot graphs

B Pandas, because it handles data manipulation well

C XGBoost, because it supports custom objective and evaluation functions

D Seaborn, because it provides statistical visualizations

💡 Hint

Consider frameworks that allow you to define your own loss or metric functions.

XGBoost allows users to define custom objective and evaluation functions, enabling flexible metric integration during training.

🔧 Debug

advanced

Debugging model deployment issues

You deployed a model using ONNX runtime but get errors about unsupported operators. What is the most likely cause?

A The model uses operators not supported by the ONNX runtime version

B The model was trained with too few epochs

C The model file is too large for the runtime

D The input data is missing normalization

💡 Hint

Think about compatibility between model operators and runtime support.

ONNX runtime supports a subset of operators; if the model uses newer or custom operators, errors occur during inference.

🧠 Conceptual

expert

Choosing a framework for agentic AI with multi-modal inputs

You want to build an agentic AI system that processes text, images, and audio simultaneously and can learn from interactions. Which framework best supports this complex multi-modal, interactive learning?

A OpenCV, because it specializes in image processing

B LangChain, because it supports building agentic AI with multi-modal capabilities and interaction loops

C TensorFlow 1.x, because it is stable and widely used

D Scikit-learn, because it has many classical algorithms for tabular data

💡 Hint

Look for frameworks designed for building AI agents with multi-modal and interactive features.

LangChain is designed to build agentic AI systems that can handle multiple data types and support interaction loops, making it ideal for complex multi-modal AI.