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| Section | Weight | Objectives |
|---|---|---|
| Prompt Design & Structure | 25% | - Core components of effective prompts
|
| Ethics & Best Practices | 20% | - Ethical considerations
|
| Real-World Application | 30% | - Industry use cases
|
| Output Evaluation & Optimization | 25% | - Iterative refinement
|
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NEW QUESTION # 20
A person asks a large language model to develop a product description for a laptop. The person refines the prompt several times, each time adding more details, context, and restrictions to improve the result. Which prompting technique is described?
Answer: B
Explanation:
The scenario describesLeast to mostprompting. This technique involves breaking down a complex task into smaller, manageable sub-problems and solving them sequentially. In this case, the user starts with a basic request and progressively adds layers of complexity-details, context, and restrictions-to guide the AI toward a sophisticated final output. It is essentially a strategy of "building up" the prompt complexity until the model has enough specific information to meet the high-level requirement.
Unlike "Chain of Thought" (COT), which focuses on the AI showing its internal reasoning steps for a single logic problem, "Least to most" is about the user-led structural decomposition of a task. It is highly effective for creative or technical writing where a "zero-shot" (single try) approach often yields generic results. By refining the prompt iteratively, the user ensures the AI understands each constraint before moving to the next level of detail. In practical applications, this technique is used to "warm up" the model's context window with specific domain data, ensuring that by the time the final description is generated, the AI is fully aligned with the technical specs and brand voice required for the laptop.
NEW QUESTION # 21
A user is crafting a prompt and includes both the goal and the context within the text of the prompt. What is a benefit of crafting the prompt in this way?
Answer: C
Explanation:
Combining a cleargoalwith richcontextis the gold standard for achievinggreater interaction effectiveness.
The goal tells the AIwhatto achieve (the destination), while the context explains thecircumstancessurrounding the task (the map). When these two elements are present, the AI can generate a response that is not only factually correct but also relevant to the user's specific situation. Effectiveness in AI interactions is measured by how closely the output meets the user's needs on the first try.
When a prompt lacks a goal, the AI might provide a great summary of a topic but fail to perform the required action. When it lacks context, it might perform the action in a way that is inappropriate for the audience. By merging them, the user minimizes "drift"-the tendency for AI to wander into irrelevant topics. This leads to a more professional, tailored, and high-quality interaction. In practical scenarios, such as drafting a corporate policy or creating a marketing strategy, the synergy between goal and context ensures that the AI understands the "big picture," resulting in a much more effective and usable first draft.
NEW QUESTION # 22
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 # 23
What is an advantage of using Personas in prompt engineering?
Answer: D
Explanation:
The primary advantage of using a persona (e.g., "Act as a senior data scientist" or "You are a friendly high school tutor") is the generation ofhighly relevant responses. A persona acts as a sophisticated filter for the AI's vast training data. When a persona is assigned, the model narrows its focus to the tone, vocabulary, and problem-solving frameworks that are most characteristic of that specific role. This ensures that the output is stylistically and substantively aligned with the user's expectations.
For instance, if you ask for financial advice without a persona, you may get a generic list of tips. If you use the persona of a "conservative financial planner for retirees," the response will prioritize low-risk investments and capital preservation. This relevance is key to professional applications where the "voice" of the output is just as important as the information itself. Personas essentially prime the model's "associative memory" to pull from the most appropriate clusters of data, making the interaction feel more like a consultation with an expert rather than a search query.
NEW QUESTION # 24
Which strategy is effective for a company to promote the ethical use of AI?
Answer: B
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 # 25
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