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1. What is the best approach to compete effectively in the GenAI space as a small or early-stage company?
A) Outsource all AI functionality to third-party services and prioritize fast monetization. B) Find a use case you understand well, build a thin layer over top foundational models, and prototype quickly. C) Focus on building a custom foundational model from scratch using massive datasets and deep tech talent. D) Launch with maximum i
2. What does the R.O.D.E.S. framework in prompt engineering stand for?
A) Reasoning, Output, Direction, Elaboration, Syntax B) Role, Output, Dataset, Evaluation, Summary C) Role, Objective, Details, Examples, Sense Check D) Role, Outcome, Description, Execution, Structure
3. What is a key difference between a model, a workflow, and an agent in the GenAI system architecture?
A) Workflows are static automations, agents are API gateways, and models are prompt wrappers. B) Agents combine models and workflows with memory and reasoning; workflows chain steps; models handle core generation tasks. C) Models are deterministic, workflows are unpredictable, and agents are static rule-based systems. D) Models are visual interfaces, workflows are databases, and agents are UIs for interaction.
4. According to the course, what is a core principle for making AI outputs useful and reliable?
A) Always fine-tune a model with a large proprietary dataset regardless of context. B) Add context through memory, vector stores, prompts, and external tools to improve relevance. C) Avoid adding too much context, as it introduces noise into model outputs. D) Skip prompt engineering and focus on improving model accuracy metrics instead.
5. What is the purpose of platforms like Make.com and n8n in the GenAI workflow?
A) To create, trigger, and orchestrate workflows integrating multiple AI agents and APIs. B) To replace AI agents with simpler spreadsheet macros. C) To visualize ML pipelines in real-time 3D renderings. D) To manage only foundational model training data pipelines.
6. What is a practical use of vector databases in GenAI applications?
A) To retrieve semantically similar data points that improve context in AI prompts B) To visualize LLM performance in real-time dashboards C) To serve as static memory for storing structured relational data like SQL D) To optimize the performance of prompt templates with temperature tuning
7. What does “tool use” enable in AI agents?
A) Monitoring latency across multiple prompts to detect bottlenecks B) Manually chaining prompts together to simulate multi-step workflows C) Calling external APIs, functions, or databases to perform tasks beyond pure text generation D) Building UI components from raw HTML embedded in LLM responses
8. What distinguishes agents from basic workflows in the GenAI stack?
A) Agents follow strict branching logic but lack real-time adaptation B) Agents are logic trees executed from manual input forms C) Agents combine workflows with memory, context, and reasoning to complete complex tasks D) Agents act only as wrappers around APIs with no internal autonomy
9. What is the main value of using a memory layer in AI agents?
A) Archiving logs of previous prompts in vector databases for backups B) Retaining user and task context across sessions to enable continuity C) Replacing prompts with hand-coded rules in agent systems D) Increasing LLM output randomness for more exploratory use cases
10. What is a good principle when embedding GenAI in a product?
A) Use AI as a backend enhancement only, without exposing it to the user B) Release AI features as fast as possible to gather hype, regardless of UX readiness C) Prioritize the latest model even if it adds latency or complexity D) Ensure the AI fits the user flow, is trustworthy, and solves a real job to be done
