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| Section | Weight | Objectives |
|---|---|---|
| Ethics & Best Practices | 20% | - Professional standards
|
| Prompt Design & Structure | 25% | - Core components of effective prompts
|
| Real-World Application | 30% | - Contextual adaptation
|
| Output Evaluation & Optimization | 25% | - Assessing response quality
|
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NEW QUESTION # 40
Consider the following component of an AI search tool prompt: "Find bike paths near Minneapolis." Which effective prompt component does this demonstrate?
Answer: B
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 # 41
What is the principle of ethics that is ensured by explaining AI system decision-making to stakeholders and users?
Answer: C
Explanation:
Transparencyin AI ethics refers to the degree to which an AI system's internal logic, data sources, and decision-making processes are visible and understandable to humans. It is the direct antidote to the "Black Box" problem. When an AI system provides a recommendation, the principle of transparency ensures that stakeholders (such as regulators, developers, and end-users) can understand the "why" behind the output. This is often achieved through "Explainable AI" (XAI) techniques.
In practical prompt engineering, transparency is optimized by instructing the model to provide its reasoning.
For example, using "Chain of Thought" prompting forces the AI to list the steps it took to arrive at a conclusion. This makes the interaction transparent because the user can see if the AI relied on faulty logic or biased data. Transparency builds trust; if a user understands how an AI reached a conclusion, they are more likely to adopt the technology. Furthermore, transparency is a prerequisite for other ethical principles like Fairness and Accountability, as you cannot fix a bias or hold a system accountable if you cannot see how it functions internally.
NEW QUESTION # 42
A person wants to use AI to digitize receipts for expense tracking. Which advanced AI tool should be used?
Answer: C
Explanation:
To digitize physical documents like receipts, the necessary technology isOptical Character Recognition (OCR). OCR is a specialized AI field that involves the conversion of images of typed, handwritten, or printed text into machine-encoded text. When you take a photo of a receipt, the AI analyzes the pixels to identify the shapes of letters and numbers, then translates those shapes into digital characters that can be stored in a database or an Excel spreadsheet.
In the context of expense tracking, advanced OCR does more than just "read" the text; it uses "intelligent character recognition" to understand the layout. It can identify which number is the "Total," which is the
"Tax," and which is the "Date" by looking at their positions on the page and the keywords nearby. This makes OCR an essential bridge between the physical and digital worlds. While a "Virtual personal assistant" (Option B) mightusean OCR tool to help you, the specific technology doing the work of digitization is OCR. It saves hours of manual data entry and reduces the human error associated with typing in long strings of financial data, making it a powerful "practical application" of AI in business and personal finance.
NEW QUESTION # 43
Which task can be accomplished with the data cleaning capabilities of generative AI?
Answer: D
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 # 44
What is a capability that results from the raw data processing functionality of AI?
Answer: C
Explanation:
The fundamental strength of Artificial Intelligence lies in its ability to process vast amounts of raw data to identify patterns that are often imperceptible to humans. Among these capabilities, computer vision- specifically the recognition of objects or people in images-is a primary result of raw data processing. When an AI is fed millions of pixels from an image, it utilizes neural networks to identify edges, shapes, and textures, eventually aggregating these features to classify the subject matter. Unlike humans, who perceive an image through cognitive understanding and life experience, an AI "understands" an image as a complex matrix of numerical values.
Options such as experiencing emotions or applying moral reasoning remain outside the current capabilities of
"Narrow AI," as these require consciousness and subjective experience. Predicting human decision-making is also a separate, more complex behavioral modeling task that goes beyond simple raw data processing.
Recognizing objects serves as a foundational "perception" task, enabling practical applications such as facial recognition, autonomous driving, and medical imaging diagnostics. This capability is the direct result of training models on labeled datasets where the raw input (pixels) is mapped to specific outputs (labels), demonstrating the power of pattern recognition in modern AI architectures.
NEW QUESTION # 45
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