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| Section | Weight | Objectives |
|---|---|---|
| Output Evaluation & Optimization | 25% | - Assessing response quality
|
| Ethics & Best Practices | 20% | - Professional standards
|
| Real-World Application | 30% | - Contextual adaptation
|
| Prompt Design & Structure | 25% | - Core components of effective prompts
|
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NEW QUESTION # 12
Which strategy is effective for a company to promote the ethical use of AI?
Answer: A
Explanation:
The most effective strategy for promoting ethical AI is tofoster collaboration among diverse stakeholders.
Ethics in AI is not a purely technical problem that can be "solved" with code; it is a socio-technical challenge that requires input from various perspectives, including ethicists, legal experts, social scientists, engineers, and, most importantly, the communities affected by the AI.
Diverse collaboration helps identify "blind spots" that a homogenous technical team might miss. For example, a developer might not realize that a specific data feature is a proxy for race or gender, but a sociologist or a community advocate might recognize it immediately. By bringing these voices together, a company can develop "Ethics by Design" frameworks that proactively address bias, transparency, and safety issues before the AI is deployed. This approach aligns with the principle of "Multidisciplinary Oversight," ensuring that the AI's goals are aligned with human values. Relying purely on the AI to solve its own ethical dilemmas (Option A) is dangerous, as the AI lacks a true moral compass. Instead, human-led collaboration ensures that technology remains a servant to societal well-being.
NEW QUESTION # 13
What is an important component to include in an AI prompt used to generate an image?
Answer: B
Explanation:
In the context of text-to-image generative AI, theMain subjectis the most critical component of the prompt.
While technical parameters like resolution (Option A) or file size (Option D) can sometimes be adjusted via specific suffixes or settings, the AI cannot begin the diffusion process without a clear definition ofwhatit is supposed to visualize. The main subject acts as the "anchor" for the entire generation process, providing the primary semantic information that the model uses to map noise to a coherent image.
An effective image prompt typically starts with the subject (e.g., "a golden retriever"), followed by descriptive modifiers (e.g., "wearing a space suit"), and finally, stylistic or environmental details (e.g., "cinematic lighting, 8k, digital art style"). If the main subject is vague or missing, the AI may produce a generic landscape or a chaotic abstract image. In professional design workflows, identifying the subject clearly ensures that the AI's creative "energy" is focused on the correct focal point. This allows the user to later refine the "medium" or "mood" of the image without changing the core content. Without a well-defined subject, the rest of the prompt's descriptors have no context to adhere to, leading to unpredictable and often unusable results.
NEW QUESTION # 14
A person is preparing for an upcoming speech and wants to use generative AI to help prepare for the speech.
What should the person do before writing a prompt?
Answer: A
Explanation:
The most critical step in the "pre-prompting" phase is the clear identification of the objective. Before interacting with a generative AI, the user must identify the goal of the speech. This foundational step dictates every other element of the prompt, including the persona, tone, and specific constraints. For example, a speech intended to persuade a group of investors requires a radically different linguistic approach than a speech intended to toast a friend at a wedding.
By identifying the goal first, the user can construct a prompt that provides the AI with a clear "definition of success." In practical applications, this is often referred to as the "Intent" phase. If a user skips this and goes straight to writing a draft or providing samples, the AI may generate content that is stylistically correct but fundamentally misses the mark regarding the intended outcome. Clear goals allow the user to evaluate the AI's output critically-checking if the generated text actually serves the purpose of informing, persuading, entertaining, or inspiring. Without a defined goal, prompt engineering becomes a trial-and-error process rather than a strategic exercise.
NEW QUESTION # 15
The prompt, "Give me ideas for a birthday party," is created by a parent to help plan for an upcoming birthday party. Which change helps refine the prompt?
Answer: A
Explanation:
Refining a prompt involves adding constraints that narrow the range of possibilities to better fit the user's practical reality. Indicating thesize of the partyis a high-value refinement because it fundamentally changes the nature of the suggestions the AI will generate. Planning a party for five children at home is a radically different logistics task than planning a party for 50 people at a rented venue.
By adding the party size, the AI can filter out suggestions that are physically or financially impractical. For example, if the size is "small/intimate," the AI might suggest DIY crafts or board games. If the size is "large
/corporate," it might suggest catering options and venue rentals. While knowing "why" the party is thrown (Option A) provides some context, the "how many" (Option B) is a concrete constraint that dictates the feasibility of all subsequent ideas. Providing a child's full name (Option D) is a privacy risk and provides zero functional value to the AI's creative process. Effective refinement focusing on scale and constraints ensures that the AI's output is actionable rather than just imaginative.
NEW QUESTION # 16
What is an example of a prompt that has an appropriate level of specificity?
Answer: B
Explanation:
Specificity is the cornerstone of effective prompt engineering. A specific prompt provides clear boundaries and a narrow focus, which prevents the AI from generating generic or overwhelming amounts of irrelevant information. Option C, "Provide an overview of state representative election laws in Iowa," is the best example because it defines three critical parameters: theSubject(election laws), theScope(state representative level), and theLocation/Jurisdiction(Iowa).
In contrast, options A and D are far too broad; asking for an overview of four major sciences or all business regulations would result in a superficial summary that lacks depth. Option B is subjective and lacks context, as "best classes" depends entirely on the student's major and career goals. By specifying the state and the specific legislative body, the user in Option C allows the AI to access a targeted subset of its training data. In practical applications, this level of specificity significantly reduces the risk of "hallucinations" or factual errors, as the model is guided to a precise factual domain. This is essential in professional research where accuracy and relevance are prioritized over general knowledge.
NEW QUESTION # 17
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