Code generation agent design in Agentic AI - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Key components of a code generation agent

Which of the following is NOT typically a core component of a code generation agent?

A A hardware driver to control physical devices

B A code synthesis engine to generate code snippets

C A feedback loop to refine generated code based on testing

D A natural language understanding module to interpret user requests

💡 Hint

Think about what parts are needed to understand, generate, and improve code.

A hardware driver is unrelated to code generation agents, which focus on interpreting requests, generating code, and improving it through feedback.

❓ Model Choice

intermediate

Choosing a model architecture for code generation

Which model architecture is most suitable for generating code from natural language prompts?

A Convolutional Neural Network (CNN)

B Transformer-based language model

C Recurrent Neural Network (RNN) with attention

D K-Nearest Neighbors (KNN)

💡 Hint

Consider which architecture handles long-range dependencies and sequences best.

Transformer models excel at understanding and generating sequences like code and natural language due to their attention mechanisms.

❓ Hyperparameter

advanced

Impact of temperature in code generation sampling

In a code generation agent using a language model, what is the effect of increasing the temperature parameter during sampling?

A The model runs faster but with lower accuracy

B The generated code becomes more deterministic and repetitive

C The generated code becomes more random and diverse

D The model ignores the input prompt

💡 Hint

Temperature controls randomness in output choices.

Higher temperature increases randomness, leading to more diverse but less predictable code outputs.

❓ Metrics

advanced

Evaluating code generation quality

Which metric best measures how well a code generation agent produces syntactically correct and functional code?

A Code execution success rate on test cases

B BLEU score comparing generated code to reference code

C Perplexity of the language model on training data

D Training loss value after model convergence

💡 Hint

Think about what shows the code actually works as intended.

Execution success rate directly measures if generated code runs correctly and passes tests, which is crucial for quality.

🔧 Debug

expert

Identifying the cause of poor code generation output

A code generation agent frequently produces code with syntax errors despite training on clean data. Which is the MOST likely cause?

A The optimizer learning rate is too low

B The model uses a transformer architecture instead of an RNN

C The temperature parameter is set to zero during sampling

D The training dataset contains many incomplete or truncated code snippets

💡 Hint

Consider what data quality issues can cause syntax errors in output.

Incomplete or truncated code in training data teaches the model incorrect syntax patterns, causing errors in generated code.