Submitting the final quiz unlocks your grade, Leaderboard points, and Certificate.
◆ Only one answer is 100%. Others may count 75% or 50% — your final grade reflects this. At the end you'll see the correct answers for all questions.
⚠️ Submit once and be patient — your grade can take up to 30 minutes.
After you submit, our pipeline processes your result automatically. This can take up to 30 minutes — that's normal. If you submit a second time thinking something went wrong, the pipeline will detect the duplicate and only your first submission counts . Your grade, Leaderboard points, and Certificate will not be processed for the repeat. Submit once, then wait.
After up to 30 minutes, find your grade at members.builderscamp.com and your Certificate at members.builderscamp.com/certificates .
Good luck. 🤞
1. What is the purpose of Chain-of-Thought prompting in AI?
A) It helps break down the reasoning process step-by-step, improving problem-solving and transparency B) It focuses on creating structured outputs by following a set of predefined rules C) It provides examples of the expected input-output format without requiring fine-tuning D) It simplifies prompts by removing intermediate steps from the model’s output.
2. What should be included in a well-structured prompt for an LLM (Large Language Model)?
A) It should include basic instructions without considering background or detailed structure B) It should organize the task into logical steps, separating the background, task, and output clearly C) It should always include several complex questions without context to challenge the model D) It should avoid structure so the model can respond more creatively.
3. What is one advantage of using role prompting in product management?
A) It helps the LLM predict the user’s next action without being trained on specific data B) It allows the LLM to assume specific perspectives, providing more specialized and relevant insights C) It forces the model to follow a rigid structure, which limits its creative outputs It limits the model’s responses to generic templates for safety.
4. Why is context important when prompting an LLM for product ideas?
A) Context is not important, as the model can generate useful ideas from minimal information B) Context helps but only marginally affects the quality of the model's outputs C) Without context, the model might generate irrelevant or inaccurate suggestions Context helps the model follow specific prompt formats but doesn’t change the content
5. How does few-shot learning improve LLM performance?
A) It allows the model to adapt to a specific task by demonstrating input-output patterns with a few examples B) It enables the model to perform well without any prior examples, relying only on its pre-training C) It offers a general understanding of tasks but often requires extensive fine-tuning afterward D) It teaches the model by iterating over the same input multiple times.
6. What is the role of clarity in prompt engineering?
A) It ensures instructions are direct and specific, reducing ambiguity in the model’s output. B) It helps the model explore open-ended tasks using poetic language. C) It enables the prompt to auto-adjust to the user’s tone and style. D) It removes the need for structure by allowing natural language flow.
7. What is a benefit of using structured XML-style tags in prompts?
A) They allow for visual formatting of the prompt on the user interface. B) They help organize content clearly, reducing misinterpretation by the model. C) They make the prompt easier to translate into multiple languages. D) They allow the model to execute JavaScript inside prompts.
8. In the context of product discovery, what’s the best use of role prompting?
A) To make the model behave like a generic assistant to maintain objectivity. B) To reduce model output length by avoiding unnecessary elaboration. C) To generate ideas based on multiple formats without a target persona. D) To simulate specific user types and elicit responses aligned with their needs.
9. Why is structure valuable when asking the model to create user stories?
A) It helps the model separate background, goals, and output format for more accurate responses. B) It helps the model guess user needs through abstract patterns. C) It allows the prompt to vary each time, improving creativity. D) It forces the model to generate lists only in JSON format.
10. How does Chain-of-Thought prompting support growth experimentation planning?
A) It helps the model guess the final outcome based on a hypothesis alone. B) It encourages step-by-step thinking through hypotheses, target users, and metrics. C) It makes the model generate random ideas faster. D) It suppresses the model’s reasoning to produce shorter outputs.
