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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Output Evaluation & Optimization | 25% | - Iterative refinement
|
| Topic 2: Prompt Design & Structure | 25% | - Core components of effective prompts
|
| Topic 3: Ethics & Best Practices | 20% | - Professional standards
|
| Topic 4: Real-World Application | 30% | - Industry use cases
|
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質問 # 34
A user uses an AI model to predict weather patterns. However, the model consistently predicts temperatures that are off by about five degrees. Which form of bias is associated with this phenomenon?
正解：C
解説：
The phenomenon where an AI consistently produces results that deviate from the truth by a specific margin (in this case, five degrees) is known asMeasurement bias. This typically occurs when the data used to train the model was collected using faulty, poorly calibrated, or inconsistent tools. If the thermometers used to gather the historical weather data were all consistently off by five degrees, the AI will learn and replicate that systemic error as if it were a factual pattern.
Unlike "Sampling bias" (which involves who or what is included in the data) or "Confirmation bias" (which involves the user seeking data that fits their beliefs), Measurement bias is a technical flaw in the data collection phase. It is particularly dangerous because the model may appear to be "consistent" and "reliable," but it is actually consistently wrong. In the field of AI ethics and data integrity, identifying measurement bias is crucial because it requires the user to go back to the source sensors or the data entry process to find the
"skew." Correcting this bias isn't a matter of changing the prompt, but rather of re-calibrating the training data to ensure it accurately reflects the real-world environment it is meant to predict.
質問 # 35
Which activity is facilitated by natural language processing?
正解：B
解説：
Checking for grammar errorsis a quintessential NLP task. Modern grammar checkers (like Grammarly or the built-in tools in Word and ChatGPT) do not just look for misspelled words; they utilize NLP to understand the syntactic structure of a sentence. This allows the AI to identify complex issues such as subject-verb disagreement, dangling modifiers, and improper tense usage.
NLP models are trained on the rules of linguistics and large corpora of well-written text, allowing them to predict what a "correct" sentence should look like. This facilitates more than just mechanical correction; it allows the AI to suggest improvements in tone, clarity, and conciseness. Because the AI "understands" the relationship between different parts of speech, it can offer context-aware suggestions. For example, it can distinguish between "there," "their," and "they're" based on the surrounding words-a task that a simple spell- checker cannot do. This application is foundational to prompt engineering because users often use AI as an editor. By facilitating high-quality grammar and style checking, NLP allows for more professional communication and ensures that the final output of any prompt is polished and ready for a human audience.
質問 # 36
A person wants to use an AI model to predict the winner of an athletic event. The person repeatedly prompts the model until it chooses the person's favorite athlete as the winner. What is the type of bias described in the scenario?
正解：B
解説：
This scenario is a textbook example ofConfirmation bias. Unlike other biases that reside within the data or the algorithm, confirmation bias is a cognitive bias on the part of theuser. It occurs when a person searches for, interprets, or prioritizes information in a way that confirms their pre-existing beliefs or desires. By repeatedly prompting the AI until it provides the "desired" answer, the user is disregarding all previous outputs that contradicted their preference.
In the context of prompt engineering, confirmation bias can lead to "leading prompts" where the user subconsciously (or consciously) steers the AI toward a specific conclusion (e.g., "Tell me why Athlete X is the best"). This undermines the AI's value as an objective tool for analysis. To mitigate this, prompt engineers should practice "neutral prompting" and seek to explore multiple perspectives (using techniques like Tree of Thought) rather than hunting for a specific output. Failing to recognize confirmation bias can lead to poor decision-making and the creation of "echo chambers" where AI is used to justify subjective opinions rather than uncover objective truths.
質問 # 37
Which programming software task is well-suited for artificial intelligence?
正解：B
解説：
Artificial Intelligence, particularly Large Language Models (LLMs) trained on vast repositories of public code, has become exceptionally proficient at suggesting code modifications. This task is well-suited for AI because code is inherently structured and follows strict logical and syntactical rules. AI can analyze a snippet of code, identify inefficiencies, detect potential bugs, and suggest more "pythonic" or optimized ways to achieve the same result. This is often referred to as "AI-assisted development" or "copiloting." While AI can certainly add comments to scripts, that is a relatively low-level task compared to the complex logic involved in code modification. Specifying project structure and performing user testing often require a high-level architectural understanding and human-centric feedback that AI currently lacks in a holistic sense.
Suggesting modifications involves the AI "understanding" the intent of the code and predicting the next logical sequence or identifying a better algorithm to solve a problem. This capability significantly accelerates the development lifecycle, allowing developers to focus on high-level logic while the AI handles boilerplate code and optimization suggestions. It bridges the gap between raw intent and functional implementation by leveraging the statistical likelihood of code patterns found in high-quality software libraries.
質問 # 38
Part of a person's prompt to an AI chatbot is: "You are a lawyer." Which effective prompt component does this demonstrate?
正解：A
解説：
The instruction "You are a lawyer" is a classic example of assigning aPersonato an AI model. In prompt engineering, a persona is a specified role or identity that the AI is asked to adopt. This technique is highly effective because it triggers the model to prioritize certain linguistic patterns, professional jargon, and specialized knowledge bases associated with that specific role. By telling the AI to act as a lawyer, the user is signaling that the tone should be formal, the reasoning should be analytical, and the output should reflect legal standards and structures.
Assigning a persona helps narrow the "probabilistic space" of the AI's responses. Instead of providing a generic answer, the model will attempt to provide an answer that a legal professional would likely give. This is different from "Instructions," which tell the AIwhat to do(e.g., "Write a contract"), or "Context," which provides thebackground facts(e.g., "This is for a small business in Ohio"). The persona provides thevoice and perspectivethrough which the information is filtered. Utilizing personas is a core strategy in prompt engineering to ensure that the output matches the professional or creative expectations of the user.
質問 # 39
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