Career paths in GenAI - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Which role primarily focuses on designing and training large language models?

In the field of Generative AI, different career paths focus on various tasks. Which role is mainly responsible for designing the architecture and training of large language models?

A Machine Learning Engineer

B Data Scientist

C Prompt Engineer

D Research Scientist

💡 Hint

Think about who creates new model designs and experiments with architectures.

Research Scientists focus on creating and improving model architectures and training methods, while Machine Learning Engineers implement and deploy models.

🧠 Conceptual

intermediate

What is the main responsibility of a Prompt Engineer in GenAI?

Among the various GenAI career paths, what does a Prompt Engineer mainly do?

A Develop new neural network architectures

B Manage data pipelines for training

C Optimize input queries to get better AI outputs

D Deploy AI models to production servers

💡 Hint

Think about how users interact with AI models to get desired results.

Prompt Engineers craft and optimize input prompts to guide AI models to produce better and more accurate outputs.

❓ Model Choice

advanced

Which GenAI career path is best suited for deploying models at scale with reliability?

You want to ensure a GenAI model runs smoothly and reliably for millions of users. Which career path is most focused on this task?

A Machine Learning Engineer

B Data Scientist

C Prompt Engineer

D Research Scientist

💡 Hint

Consider who handles production and system reliability.

Machine Learning Engineers specialize in deploying, scaling, and maintaining AI models in production environments.

❓ Metrics

advanced

Which career path is most involved in analyzing model performance metrics to improve GenAI outputs?

Analyzing metrics like accuracy, loss, and perplexity is key to improving GenAI models. Which role focuses most on this analysis?

A Prompt Engineer

B Data Scientist

C Machine Learning Engineer

D Research Scientist

💡 Hint

Think about who works with data and model evaluation.

Data Scientists analyze model outputs and metrics to understand performance and suggest improvements.

🔧 Debug

expert

You notice a GenAI model deployed by the ML Engineer is producing inconsistent outputs. Which step should the ML Engineer take first to debug?

A deployed GenAI model sometimes gives wrong or inconsistent answers. What is the best first debugging step for the Machine Learning Engineer?

A Review the deployment environment and resource limits

B Check the model's training data for errors

C Rewrite the prompt inputs to the model

D Change the model architecture to a simpler one

💡 Hint

Think about what can cause inconsistent behavior after deployment.

Deployment issues like insufficient memory or CPU can cause inconsistent model outputs; checking environment is the first step.