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WGU Practical Applications of Prompt QFO1 認定 Practical-Applications-of-Prompt 試験問題 (Q40-Q45):
質問 # 40
Which generative AI tool allows users to create engaging and dynamic content with templates and stock footage?
- A. Invideo
- B. DALL-E
- C. ChatGPT
- D. Midjourney
正解：A
解説：
Invideois a generative AI platform specifically designed for video creation. It distinguishes itself from text-to- image or text-to-text models by providing a comprehensive suite of tools that combine AI-generated scripts with a library of stock footage, music, and templates. Users can provide a single text prompt describing a video concept, and the AI will generate a script, select relevant video clips, and even provide a voiceover.
This tool is a prime example of an "application-specific" generative medium. While ChatGPT can write the script and Midjourney can create the thumbnails, Invideo integrates these capabilities into a single workflow for content creators and marketers. The "prompting" in Invideo is often more about "Art Direction" than linguistic structure; users must specify the target platform (e.g., "YouTube Shorts"), the target audience, and the desired aesthetic. Evaluating this medium involves understanding how AI interacts with pre-existing assets (stock footage) versus creating entirely new ones from scratch. It represents the shift from "Generative AI" as a novelty to "Generative AI" as a functional production tool.
質問 # 41
Which major challenge has been an issue for AI systems?
- A. Generating video content
- B. Analyzing vast amounts of data
- C. Lacking ethical reasoning
- D. Processing unstructured data
正解：C
解説：
One of the most significant and persistent challenges in the field of Artificial Intelligence is the lack of inherent ethical reasoning. AI models operate based on mathematical probabilities and patterns found within their training data; they do not possess a moral compass, a sense of justice, or an understanding of social nuances unless specifically programmed or constrained by human-defined rules. This often leads to issues where an AI might generate biased, harmful, or socially insensitive outputs because it is simply reflecting the biases present in its training set without any ethical filter.
While AI is actually quite proficient at analyzing vast amounts of data and is increasingly capable of processing unstructured data and generating video, the "black box" nature of its decision-making makes ethical alignment difficult. Ensuring that an AI respects privacy, avoids discrimination, and adheres to human values requires significant external intervention, such as Reinforcement Learning from Human Feedback (RLHF). The challenge lies in the fact that ethics are often subjective and context-dependent, making it nearly impossible to encode a universal moral code into a machine. This lack of ethical reasoning is why human oversight remains a critical component of AI deployment, especially in high-stakes fields like law, healthcare, and autonomous systems.
質問 # 42
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?
- A. Measurement bias
- B. Confirmation bias
- C. Selection bias
- D. Sampling bias
正解：A
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 # 43
A user wants to automatically identify and provide the name of the person speaking on a conference call.
Which advanced AI tool fits this goal?
- A. Speech recognition
- B. Text recognition
- C. Voice recognition
- D. Object recognition
正解：C
解説：
The specific task of identifyingwhois speaking is the primary function ofVoice recognition(also known as speaker recognition or speaker identification). It is important to distinguish this from "Speech recognition." While speech recognition focuses onwhatis being said (converting spoken words to text), voice recognition focuses on the unique biometric characteristics of an individual's voice-such as pitch, cadence, and tone-to identify the specific person talking.
In a conference call setting, the AI compares the incoming audio stream against a database of stored
"voiceprints." When a match is found, the system can display the name of the participant currently speaking.
This technology is a cornerstone of modern collaborative tools and security systems. In practical prompt engineering and AI integration, choosing the right "medium" or tool is vital; if a developer mistakenly uses a standard speech-to-text model, they would get a transcript of the meeting but would lose the metadata regarding speaker identity. Voice recognition adds a layer of "identity context" to the data, making it invaluable for automated meeting minutes, forensic analysis, and personalized user experiences in multi-user environments.
質問 # 44
A bank uses an AI model to help evaluate loan applications. The model makes suggestions, but the bank employees have no knowledge of which criteria the model uses to evaluate applicants. What is the associated ethical concern described in the scenario?
- A. Autonomy
- B. Transparency
- C. Misinformation
- D. Privacy
正解：B
解説：
The primary ethical concern in this scenario isTransparency, often referred to in the AI field as the "Black Box" problem. Transparency in AI means that the processes, logic, and data used by the system to reach a decision should be understandable and accessible to human stakeholders. When bank employees cannot explainwhya loan was denied, it violates the principle of "Explainability," which is a subset of transparency.
This lack of transparency is particularly problematic in high-stakes industries like finance, healthcare, and law. If a model is making biased or incorrect decisions, the lack of transparency makes it impossible to audit the system or correct the underlying error. Many modern regulations, such as the GDPR's "Right to Explanation," require that individuals affected by automated decisions have a right to know the logic behind them. Effective prompt engineering can help address this by using techniques like "Chain of Thought," where the AI is instructed to "show its work" or explain its reasoning process step-by-step, thereby transforming a black-box interaction into a more transparent, "white-box" process.
質問 # 45
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試験の準備方法-効果的なPractical-Applications-of-Prompt出題内容試験-実際的なPractical-Applications-of-Prompt最新受験攻略
参考のために許容できる価格に加えて、3つのバージョンのすべての資料は、10年以上にわたってこの分野の専門家によって編集されています、弊社のWGU Practical-Applications-of-Prompt問題集を通して復習してから、真実的に自分の能力の向上を感じ、Practical-Applications-of-Prompt資格認定を受け取ります。
Practical-Applications-of-Prompt学習教材があれば、あなたは自分の夢を叶えます、プロの支払い保護。
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