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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Ethics & Best Practices | 20% | - Professional standards
|
| Topic 2: Output Evaluation & Optimization | 25% | - Assessing response quality
|
| Topic 3: Prompt Design & Structure | 25% | - Prompting techniques
|
| Topic 4: Real-World Application | 30% | - Industry use cases
|
>> Practical-Applications-of-Prompt受験内容 <<
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質問 # 34
Which prompting technique involves using information from an initial prompt to guide the AI to a second prompt?
正解：C
解説：
TheGenerated Knowledgetechnique is a two-step optimization process. In the first step, the user asks the AI to generate a set of relevant facts, rules, or background information about a topic. In the second step, this newly "generated knowledge" is incorporated into a follow-up prompt to improve the accuracy of the final answer. This is particularly useful when the AI needs to perform a task that requires specific domain expertise that might not be immediately "top-of-mind" for the model.
For example, if you want the AI to write a medical summary, you might first ask it to "List the current guidelines for treating hypertension" (Generated Knowledge). Then, you use that list in a second prompt:
"Based on these guidelines, evaluate this patient's case." This technique prevents the AI from relying purely on its general training data and instead forces it to use a "grounded" set of facts as a reference point. It is a powerful way to reduce hallucinations because the model is essentially building its own "contextual library" before attempting the main task. This sequential approach ensures that the final output is backed by explicit logic rather than just probabilistic word prediction.
質問 # 35
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?
正解：B
解説：
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.
質問 # 36
A company released a new sports watch, and an advertiser wants to use generative AI to help produce a text- based advertisement for the watch that explains the features of the watch. Which prompt engineering solution is most likely to achieve this goal?
正解：D
解説：
To achieve a high-quality, accurate advertisement, the most effective solution is togive a list of features that should be highlighted. In prompt engineering, this is known as providing "input data" or "grounding." Without a specific list of features, the AI will likely "hallucinate" capabilities for the sports watch-such as a
100-day battery life or a built-in laser-that the product does not actually possess.
By providing a concrete list (e.g., "GPS tracking, heart rate monitor, 50m water resistance, and sapphire glass"), the user provides the AI with the raw materials needed to construct the ad. This shifts the AI's role from "fictional writer" to "creative editor." The model can then focus on persuasive language and structural formatting rather than inventing technical specifications. This is the standard professional approach for marketing teams: use the prompt to establish the "facts" and let the AI handle the "flair." It ensures the resulting text is both creative and factually grounded, which is the primary requirement for any commercial advertisement.
質問 # 37
Which statement explains why generative AI is valuable for data classification?
正解：C
解説：
Generative AI is exceptionally valuable for data classification becauseit can detect complex patternsthat traditional, rule-based systems might miss. Classification is the process of assigning a category to a piece of data (e.g., labeling an email as "Spam" or "Priority"). While older systems might look for specific keywords, generative AI understands the semantic relationship between words and the overall intent of the text.
This ability to detect nuance allows the AI to classify unstructured data-like customer feedback or social media posts-based on sentiment, urgency, or topic, even if the user hasn't provided a specific "rule" for every possible scenario. For instance, an AI can recognize that "The wait time was unacceptable" and "I've been standing here for an hour" both belong in the "Negative Experience" category, despite having no words in common. This pattern recognition is the result of training on billions of parameters, allowing the model to
"understand" the underlying context. In prompt engineering, leveraging this capability involves providing the AI with a few examples (few-shot prompting) to "prime" it on the specific patterns you want it to identify, resulting in highly accurate and flexible data categorization.
質問 # 38
What is the principle of ethics that is ensured by creating mechanisms to assign responsibility for AI actions and decisions?
正解：B
解説：
The principle ofAccountabilityis centered on the requirement that there must be an identifiable person or entity responsible for the outcomes of an AI system's actions. As AI systems become more autonomous, the
"responsibility gap" becomes a significant ethical risk. Establishing accountability means creating clear frameworks-legal, organizational, and technical-to ensure that when an AI makes a mistake (such as an incorrect medical diagnosis or a biased financial decision), there is a mechanism for recourse, explanation, and correction.
In the context of prompt engineering, accountability is often managed through "human-in-the-loop" systems.
This ensures that while the AI may generate the initial draft or decision-making logic, a human remains the ultimate authority who "signs off" on the result. Accountability also involves "Auditability"-the ability for third parties to review the AI's logs and decision-making history. Without accountability, AI deployment can lead to "organized irresponsibility," where no one takes ownership of systemic failures. By embedding accountability into the lifecycle of an AI project, organizations protect themselves and their users, ensuring that the technology serves as a tool for human progress rather than an unchecked black box.
質問 # 39
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