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Amazon AIF-C01 Exam - Topic 5 Question 9 Discussion

A company wants to use a large language model (LLM) on Amazon Bedrock for sentiment analysis. The company wants to classify the sentiment of text passages as positive or negative.Which prompt engineering strategy meets these requirements?
A) Provide examples of text passages with corresponding positive or negative labels in the prompt followed by the new text passage to be classified.
B) Provide a detailed explanation of sentiment analysis and how LLMs work in the prompt.
C) Provide the new text passage to be classified without any additional context or examples.
D) Provide the new text passage with a few examples of unrelated tasks, such as text summarization or question answering.

Amazon AIF-C01 Exam - Topic 5 Question 9 Discussion

Actual exam question for Amazon's AIF-C01 exam
Question #: 9
Topic #: 5
[All AIF-C01 Questions]

A company wants to use a large language model (LLM) on Amazon Bedrock for sentiment analysis. The company wants to classify the sentiment of text passages as positive or negative.

Which prompt engineering strategy meets these requirements?

Show Suggested Answer Hide Answer
Suggested Answer: A

by Elena at Sep 17, 2024, 10:45 PM

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Heidy
6 months ago
Wait, can LLMs really classify sentiment accurately?
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Ahmad
6 months ago
D just confuses the model, right?
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Bo
7 months ago
C seems too vague, how will it know?
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Maurine
7 months ago
I think B is overkill for this task.
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Carla
7 months ago
Option A is definitely the best choice!
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Johnetta
7 months ago
I recall that unrelated examples could confuse the model, so option D seems like a bad choice for this task.
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Celestina
7 months ago
I feel like option C might not give the model enough information to classify the sentiment accurately.
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Wynell
8 months ago
I'm not entirely sure, but I remember a practice question where giving context really helped improve the results.
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Fidelia
8 months ago
I think option A makes the most sense since providing examples could help the model understand what we're looking for.
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Laurel
8 months ago
I think the key here is to give the LLM some context and examples to work with. Providing the new text passage without any additional information seems like it would be too open-ended. I'm leaning towards the first option, but I'll double-check my reasoning before answering.
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Abraham
8 months ago
I'm a little confused by the options here. I'm not sure if providing a detailed explanation of sentiment analysis and LLMs would actually help the model, or if including unrelated tasks would be useful. I'll have to think this through carefully.
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Ahmad
8 months ago
Okay, let me think this through. I think providing examples of positive and negative text passages in the prompt, along with the new passage to be classified, would be the best way to guide the LLM to accurately determine the sentiment. That's my strategy for this question.
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Afton
8 months ago
This seems like a pretty straightforward prompt engineering question. I'm pretty confident I can figure this out.
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Anjelica
8 months ago
Hmm, I'm a bit unsure about this one. I know prompt engineering is important for getting good results from LLMs, but I'm not sure which approach would work best for sentiment analysis.
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Filiberto
8 months ago
Okay, I've got this. The key reasons to use Jest for Lightning web components are to verify basic user interactions, ensure events are firing correctly, and test how different components work together. Gotta cover those core functionality areas.
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Willie
2 years ago
Option F: Feed the LLM a diet of positive and negative emojis. It'll be the most efficient sentiment analysis tool ever!
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Arlean
2 years ago
Option D? Really? Mixing in other tasks is just going to confuse the poor thing. Keep it simple, people!
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Crista
2 years ago
C'mon, we're not writing a novel here. Just throw the new text at the LLM and let it figure it out! Option C all the way.
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Valentine
2 years ago
Let's trust the LLM to classify the sentiment accurately with just the text.
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Mila
2 years ago
Exactly, keeping it simple with option C is the way to go.
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Verdell
2 years ago
Yeah, no need to complicate things with extra examples or explanations.
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Celestina
2 years ago
I agree, just give the LLM the text and let it do its thing.
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Queen
2 years ago
I agree, Option A is the most straightforward approach. Who wants to read a lengthy explanation when you can just give some examples?
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Long
2 years ago
I think providing examples with corresponding labels is the most effective strategy for sentiment analysis.
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Jarod
2 years ago
I agree, examples are much more helpful than a long explanation.
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Gertude
2 years ago
Option A is definitely the way to go. Examples make it so much easier to understand.
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Royal
2 years ago
Option A seems like the way to go. Providing example text passages with labels should help the LLM understand the task better.
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Gerry
2 years ago
User 3: Definitely. It's important to give the model context to understand the task.
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Refugia
2 years ago
User 2: I agree. It will provide clear guidance for sentiment analysis.
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Markus
2 years ago
User 1: Option A seems like the best choice. Giving examples with labels will help the LLM learn.
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Wayne
2 years ago
I prefer option B. Understanding how LLMs work is crucial for accurate sentiment analysis.
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Celia
2 years ago
I agree with Filiberto. Providing examples will help the model learn better.
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Filiberto
2 years ago
I think option A is the best strategy.
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