How to Become an AI Engineer Quiz

Answers and explanations appear as you take the quiz.

1. How does an AI engineer's focus differ from a machine learning engineer's, according to the guide?
   • AI engineers design GPUs, while ML engineers write prompts
   • AI engineers build applications on top of existing foundation models, while ML engineers train and deploy models
   • AI engineers only do data analysis and statistics
   • There is no difference between the two roles
2. Why does the guide recommend learning raw LLM APIs before frameworks like LangChain?
   • Frameworks are illegal to use commercially
   • Raw APIs are always faster to write
   • Frameworks cannot call LLMs
   • It makes debugging any framework much easier later
3. What is the Model Context Protocol?
   • An open standard for connecting models to tools and data
   • A law regulating AI engineers
   • A method for compressing model weights
   • A benchmark for measuring model accuracy
4. In a RAG pipeline, what happens during ingestion?
   • The model is retrained on each user question
   • Users rate answers from one to five stars
   • Documents are chunked, embedded and stored with metadata
   • The system deletes old documents every day
5. Why might a system run a BM25 keyword search alongside vector search?
   • To catch exact keyword matches such as names and codes that embeddings may miss
   • Because vector search cannot return more than one result
   • To avoid storing any metadata
   • Because BM25 generates the final answer text
6. What does recall at k measure in RAG evaluation?
   • How quickly the model generates each token
   • Whether the correct chunk appears among the top k retrieved results
   • How many users return to the app after k days
   • The number of words in the final answer
7. According to the guide, where do many wrong RAG answers trace back to?
   • The user's internet connection
   • The model's temperature setting always being too low
   • The programming language used
   • Retrieval failures rather than the model itself
8. Which misconception does the guide correct about starting in AI engineering?
   • That solid software engineering matters
   • That evaluations help catch regressions
   • That you need a PhD or advanced mathematics to begin
   • That prompt injection is a security concern