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Cisco 810-110 Sample Question Answers
Question # 1
Which action demonstrates the principle of fairness in a generative AI deployment?
A. redacting PII from the training dataset B. hosting the model in a
carbon-neutral data center C. requiring users to sign Acceptable Use
Policies D. auditing model outputs for bias
Answer: D
Why this is correct:
Fairness in generative AI means ensuring that outputs do not systematically disadvantage or misrepresent groups of people.
By auditing model outputs for bias, organizations can detect and mitigate unfair treatment, stereotypes, or skewed recommendations.
This is a proactive action that directly addresses fairness in deployment, aligning with responsible AI principles.
Question # 2
What is a corporate data privacy risk when using a public AI chatbot?
A. The AI will provide biased technical advice. B. The input data
may be used for model training. C. The AI will provide hallucinated
technical specifications. D. The data will be encrypted at rest instead
of in transit.
Answer: B
Why this is correct:
When using a public AI chatbot, anything you type could be stored and potentially used to improve the model.
For a corporation, this creates a data privacy risk because sensitive or proprietary information (like architectural diagrams, client data, or internal strategies) might inadvertently be exposed outside the organization.
Cisco highlights this risk in the exam because enterprises must safeguard confidential data and avoid leaking intellectual property into public AI systems.
Question # 3
When using AI to draft a technical proposal, what is the benefit of a format-agnostic strategy?
A. It maintains contextual alignment. B. It minimizes token consumption. C. It allows for easy repurposing. D. It ensures the most creative
text.
Answer: C
Why this is correct:
A format?agnostic strategy means you design prompts and outputs without locking them into a single rigid format (like only Markdown, only tables, or only prose).
This flexibility makes the content reusable across multiple contexts — for example:
A technical proposal draft can be adapted into a presentation, a report, or a client?facing summary without rewriting from scratch.
The same structured information can be exported into different formats (HTML, PDF, slides) depending on the audience.
Cisco emphasizes this because in enterprise environments, proposals often need to be reshaped for executives, engineers, and customers — repurposing saves time and ensures consistency.
Question # 4
Which strategy should be used to mitigate hallucinations when asking an AI to summarize a specific technical document?
A. requesting citations for the summary B. using a short system
prompt C. increasing the model creativity D. removing negative
constraints
Answer: A
Why this is correct:
When asking an AI to summarize a specific technical document, hallucinations can occur if the model invents details not present in the source.
By requesting citations, you force the AI to anchor its summary to actual parts of the document. This reduces the chance of fabricated information and ensures traceability.
In practice, this means the AI must reference the original text, keeping the summary faithful to the source.
Question # 5
A practitioner is solving a complex architectural problem by breaking it into four sequential prompts. Each prompt's output is fed as input into the next prompt in the sequence. Which prompt engineering technique is being used?
A. few-shot prompting B. meta-prompting C. prompt chaining D. chain-of-thought prompting
Answer: C
Why this is correct:
Prompt chaining is the technique of breaking a complex problem into smaller, sequential prompts.
Each prompt’s output becomes the input for the next, creating a logical flow or “chain” of reasoning.
In your example, the practitioner uses four sequential prompts, each feeding into the next — that’s textbook prompt chaining.
Question # 6
Why is defining a persona considered a best practice for technical prompt engineering?
A. It provides instructions for desired action. B. It reduces the
total token usage. C. It provides a pattern to emulate. D. It guides the
tone and vocabulary.
Answer: D
Explanation:
Why this is correct:
Defining a persona in prompt engineering means specifying who the AI should act as (e.g., “You are a helpful technical consultant” or “You are a cybersecurity analyst”).
This doesn’t just tell the AI what to do; it shapes how it communicates — the tone, vocabulary, and style.
For example:
A “teacher persona” will explain concepts step?by?step in simple language.
A “lawyer persona” will use precise, formal terminology.
A “developer persona” will lean on technical jargon and code snippets.
Why not the other options:
A. Provides instructions for desired action ? That’s more about task definition, not persona.
B. Reduces total token usage ? Personas don’t inherently reduce tokens; in fact, they may add context.
C. Provides a pattern to emulate ? That’s closer, but still secondary. The main benefit is guiding communication style, not just mimicking a pattern.
So Cisco emphasizes tone and vocabulary alignment because in technical prompt engineering, clarity and consistency of communication are critical — especially when AI outputs are used in professional or customer?facing contexts.
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