AWS Certified Cloud Practitioner

Explanation

Option A is correct because this approach uses few-shot learning or in-context learning, which is an effective prompt engineering strategy for classification tasks like sentiment analysis. By providing examples of text passages with their corresponding labels (positive/negative), you're teaching the LLM the pattern it needs to follow for the new text passage.

Why other options are incorrect:

• Option B: While providing context about sentiment analysis might be helpful, it doesn't give the LLM concrete examples to learn from. LLMs benefit more from demonstration than explanation.
• Option C: Providing no context or examples is zero-shot learning, which is less effective for classification tasks where the model needs to understand the specific format and criteria for labeling.
• Option D: Examples of unrelated tasks (like text summarization or question answering) would confuse the model rather than help it learn the specific sentiment analysis task.

Key Concept: In prompt engineering for classification tasks, few-shot learning (providing examples with labels) is typically more effective than zero-shot or unrelated examples because it demonstrates the exact pattern the model should follow.