D-PEN-F-A-00 Sample Questions & Answers
The basic write, refine, test and iterate prompt cycle makes up nearly half the weighting, alongside prompting with zero-shot and few-shot examples, professional practices like reusable templates, generative-AI and LLM fundamentals, and legal considerations.
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- Question 1Advanced
AI and NLP Fundamentals · Describe Generative AI, LLM and Neural Networks
When an LLM generates a highly plausible but completely fabricated legal citation, which foundational characteristic of its neural network architecture is the root cause of this behavior?
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Correct answer: A
At their core, Large Language Models (LLMs) are complex probabilistic engines based on neural network architectures (specifically Transformers). They generate text by predicting the next most likely token (word or sub-word) based on the preceding context. Because they optimize for statistical probability and linguistic coherence rather than querying a factual database, they can easily piece together a sequence of tokens that looks completely legitimate (like a legal citation) but is entirely fabricated—a phenomenon known as hallucination.
- Question 2Advanced
Legal and Compliance Requirements · Discuss legal frameworks in the context of prompts
A healthcare provider is deploying a prompt engineering initiative using a public instance of ChatGPT to summarize patient discharge notes. The prompt engineer designs a highly effective template that requires pasting the full discharge note, including patient names, SSNs, and medical histories, into the prompt.
Which legal and ethical framework is explicitly violated by this workflow, and what is the optimal mitigation strategy?
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Correct answer: B
Pasting Personally Identifiable Information (PII) or Protected Health Information (PHI) into a public LLM instance is a severe violation of data privacy frameworks like HIPAA or GDPR, as public models may use input data for future training. The optimal mitigation strategy, if a public model must be used, is to implement a sanitization layer that redacts or anonymizes all sensitive data before the prompt is sent to the LLM.
sequenceDiagram participant User participant Sanitizer participant Public LLM User->>Sanitizer: Prompt with PHI/PII Sanitizer->>Public LLM: Anonymized Prompt Public LLM-->>Sanitizer: Summarized Data Sanitizer-->>User: Re-identified Summary - Question 3IntermediateSelect 2
Legal and Compliance Requirements · Explain the ethical and legal implications that should be considered during prompt development
A software company uses Copilot to generate large portions of source code for a new commercial product. Which TWO legal and compliance risks must the prompt engineers and legal team consider regarding the generated code? (Select TWO)
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Correct answers: C, D
LLMs are trained on vast amounts of public data, including open-source repositories. A major legal risk is that the model might generate code that closely matches copyrighted open-source code, potentially violating licenses (like GPL) if used in a closed-source commercial product.
In many jurisdictions, copyright law requires human authorship. Code generated entirely or primarily by an AI model may not be eligible for copyright protection, meaning the company might struggle to legally protect their product from being copied by competitors.
- Question 4Intermediate
Legal and Compliance Requirements · Explain the ethical and legal implications that should be considered during prompt development
A prompt engineer is tasked with creating a prompt that evaluates resumes for a hiring manager. To align with ethical prompt development practices, what is the MOST effective technique to mitigate potential bias in the model's output?
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Correct answer: B
LLMs can inherit biases present in their training data. In sensitive applications like hiring, ethical prompt development requires explicit instructions (constraints) directing the model to ignore names, genders, or demographic markers, and to base evaluations strictly on a provided rubric of objective skills. This helps mitigate unconscious bias in the output.
- Question 5Beginner
Legal and Compliance Requirements · Discuss legal frameworks in the context of prompts
When developing a customer-facing chatbot powered by a Generative AI model, which practice best aligns with Responsible AI guidelines regarding transparency?
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Correct answer: B
Transparency is a core tenet of Responsible AI. Users have the right to know whether they are interacting with a human or an automated system. Prompt developers should include system instructions that mandate the AI to disclose its artificial nature, preventing deception.
- Question 6Beginner
Introduction to prompt structure and formatting · Discuss components of a basic prompt
Review the following prompt excerpt:
'Act as a senior network engineer at a Fortune 500 company. You are preparing a report for the CIO regarding the recent cloud migration.'
Which component of a basic prompt does this text represent?
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Correct answer: A
The 'Context' component provides the background, persona, or setting for the task. By instructing the model to 'Act as a senior network engineer' and defining the audience ('for the CIO'), the prompter is setting the context. This helps the LLM adjust its vocabulary, tone, and technical depth appropriately.
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