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| Section | Weight | Objectives |
|---|---|---|
| Real-World Application | 30% | - Contextual adaptation
|
| Prompt Design & Structure | 25% | - Prompting techniques
|
| Ethics & Best Practices | 20% | - Professional standards
|
| Output Evaluation & Optimization | 25% | - Iterative refinement
|
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NEW QUESTION # 34
Which content creation tool specializes in versatile image creation through detailed text prompts?
Answer: B
Explanation:
Midjourneyis a generative AI tool that specifically specializes in high-quality, versatile image creation through sophisticated text prompts. While other tools like DALL-E are integrated into larger ecosystems (like OpenAI's ChatGPT), Midjourney has gained a reputation for its distinct artistic style, high resolution, and deep "parameter" controls that allow prompt engineers to fine-tune lighting, camera angles, and textures.
Midjourney operates primarily through a Discord interface, where users utilize "slash commands" (like
/imagine) to initiate generations. It is favored by designers and concept artists because of its ability to interpret complex, evocative language into visually stunning outputs. Unlike ChatGPT, which is primarily a text-based LLM, Midjourney is a "Diffusion Model" specifically trained on image-caption pairs. Evaluating Midjourney as a medium requires understanding that the "syntax" of the prompt differs from text models; it relies heavily on artistic descriptors, style references (e.g., "unreal engine," "octane render"), and aspect ratio constraints to achieve the desired outcome.
NEW QUESTION # 35
An AI system is used to aid in an applicant selection process. The users of the system, however, have no information about which criteria are used to evaluate applicants. Which ethical concern is associated with this issue?
Answer: D
Explanation:
This scenario highlights a critical failure inTransparency. When an AI system acts as a "gatekeeper" for life- changing opportunities-such as employment, university admissions, or bank loans-it is an ethical imperative that the criteria for selection be disclosed. If the users (the hiring managers or the applicants) do not know which variables the AI is prioritizing (e.g., years of experience, specific keywords, or even zip codes), the system is effectively a "Black Box." The lack of transparency here creates several downstream risks. First, it makes it impossible to verify if the system is actually being "Fair." If the criteria are hidden, the AI could be using proxy variables that result in illegal discrimination without anyone noticing. Second, it undermines "Accountability," as a rejected applicant has no way to challenge the decision or understand what they need to improve. In professional prompt engineering, this issue is addressed by designing prompts that require the AI to generate an
"Evaluation Report" alongside its selection, detailing which parts of the resume matched the job description.
This transforms the automated process from an opaque hurdle into a transparent, auditable tool.
NEW QUESTION # 36
Consider the following component of an AI search tool prompt: "Find bike paths near Minneapolis." Which effective prompt component does this demonstrate?
Answer: C
Explanation:
The phrase "Find bike paths near Minneapolis" functions as theInstructionscomponent of the prompt.
Instructions are the direct commands given to the AI, specifying the primary task that the user wants the system to perform. In any effective prompt, the instruction is the "verb" or the "action" that initiates the AI's processing. Without clear instructions, the AI may understand the subject (bike paths) and the location (Minneapolis) but may not know whether it should list them, map them, describe their history, or compare their difficulty levels.
In this specific case, the word "Find" is the directive. While "Minneapolis" provides a geographical constraint (Context), the core of the statement is the command to locate specific data. Effective prompt engineering relies on being explicit with these instructions to avoid ambiguity. For instance, a more refined instruction might be "Provide a list of..." or "Summarize the locations of..." to further clarify the desired action. However, at its most basic level, this component tells the AI exactly what operation to execute on the provided information, making it the functional heart of the prompt.
NEW QUESTION # 37
What is an example of a prompt that needs a greater level of detail?
Answer: D
Explanation:
Optimization often begins by identifying "under-specified" prompts. Option B, "What is the selection process for winning a national contest?", is a prime candidate for refinement because it lacks nearly all necessary context. To an AI, a "national contest" could refer to anything from a high school spelling bee in Canada to a professional bodybuilding competition in the U.S. or a lottery in the UK. Without knowing the country, the industry, or the specific type of contest, the AI's response will be purely theoretical and likely unhelpful.
Effective prompt engineering requires the user to fill in these "information gaps." To optimize this prompt, a user should include the specific field (e.g., "science fair"), the specific nation, and the specific audience or level. While options A and D are quite specific (specifying city, state, or year), and option C provides a clear target audience (college students), option B remains too vague for a generative model to provide a meaningful first draft. In professional environments, using such vague prompts leads to "prompt drift," where the AI provides a correct answer to a different question than the one the user intended to ask.
NEW QUESTION # 38
Which task can be accomplished with the data cleaning capabilities of generative AI?
Answer: A
Explanation:
Generative AI models, specifically Large Language Models (LLMs), are highly effective atIdentifying inaccuracieswithin a dataset during the data cleaning phase. When provided with a dataset and a prompt to
"check for consistency" or "identify anomalies," the AI can cross-reference the data points against its internal knowledge base or the logical rules established in the prompt. For example, if a list of "US States" includes
"London," the AI can flag this as an inaccuracy.
This capability extends to identifying spelling errors, formatting inconsistencies (e.g., dates written in multiple formats), and logical contradictions. While AI can help in identifying bias (Option D), that is usually considered a higher-level "auditing" task rather than a standard "cleaning" task. Identifying inaccuracies is a foundational step in the data pipeline; by cleaning the data first, the user ensures that any subsequent analysis or "conclusion drawing" (Option C) is based on high-quality, reliable information. In prompt engineering, this is often performed using the "Self-Correction" or "Reviewer" pattern, where one prompt generates data and a second prompt is used specifically to identify and fix any factual or structural inaccuracies within that output.
NEW QUESTION # 39
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