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SAP Exam C_AIG_2412 Topic 1 Question 12 Discussion

Actual exam question for SAP's C_AIG_2412 exam
Question #: 12
Topic #: 1
[All C_AIG_2412 Questions]

You want to assign urgency and sentiment categories to a large number of customer emails. You want to get a valid json string output for creating custom applications. You decide to develop a prompt for the same using generative Al hub.

What is the main purpose of the following code in this context?

prompt_test = """Your task is to extract and categorize messages. Here are some examples:

{{?technique_examples}}

Use the examples when extract and categorize the following message:

{{?input}}

Extract and return a json with the following keys and values:

- "urgency" as one of {{?urgency}}

- "sentiment" as one of {{?sentiment}}

"categories" list of the best matching support category tags from: {{?categories}}

Your complete message should be a valid json string that can be read directly and only contains the keys mentioned in t

import random random.seed(42) k = 3

examples random. sample (dev_set, k) example_template = """ {example_input} examples

'\n---\n'.join([example_template.format(example_input=example ["message"], example_output=json.dumps (example[

f_test = partial (send_request, prompt=prompt_test, technique_examples examples, **option_lists) response = f_test(input=mail["message"])

Show Suggested Answer Hide Answer
Suggested Answer: B

Contribute your Thoughts:

Rebbecca
2 months ago
Evaluating the model's performance with few-shot learning? Impressive! Bet the AI can learn a thing or two from those real-world examples. Maybe it'll even crack the customer's secret code language.
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Janine
28 days ago
Evaluating the model's performance with few-shot learning? Impressive! Bet the AI can learn a thing or two from those real-world examples. Maybe it'll even crack the customer's secret code language.
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Cecilia
1 months ago
A) Generate random examples for language model training
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Luz
2 months ago
Preprocessing the dataset, huh? Gotta make sure those emails are clean and ready for the model. I wonder if it can handle all the 'colorful' language that customers use.
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Charlette
2 months ago
I'm curious to see how the model handles edge cases. Like, what if the email is just a bunch of emojis? Hopefully, it doesn't get too 'emoji-tional' about it.
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Socorro
1 months ago
C) Train a language model from scratch
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Truman
1 months ago
B) Evaluate the performance of a language model using few-shot learning
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Amber
2 months ago
I'm curious to see how the model handles edge cases. Like, what if the email is just a bunch of emojis? Hopefully, it doesn't get too 'emoji-tional' about it.
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Buddy
2 months ago
A) Generate random examples for language model training
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Taryn
2 months ago
Ah, so it's generating random examples to fine-tune the language model. I bet that'll really improve the accuracy of the categorization. Gotta love that AI magic.
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Leila
1 months ago
C) Train a language model from scratch
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Fairy
2 months ago
B) Evaluate the performance of a language model using few-shot learning
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Barb
2 months ago
A) Generate random examples for language model training
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Rex
3 months ago
This code looks like it's using a generative AI model to extract and categorize the sentiment and urgency of customer emails. Seems like a great way to automate those tedious tasks!
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Tequila
2 months ago
C) Train a language model from scratch
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Freeman
2 months ago
B) Evaluate the performance of a language model using few-shot learning
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Sean
2 months ago
A) Generate random examples for language model training
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Laquita
3 months ago
I think the main purpose is to evaluate the performance of a language model using few-shot learning.
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