Free SAP C_AIG_2412 Exam Dumps Questions & Answers

What are some examples of generative Al technologies? Note: There are 2 correct answers to this question.

• A.Robotic process automation
• B.Foundation models
• C.Al models that generate new content based on training data
• D.Rule-based algorithms

Which of the following is a principle of effective prompt engineering?

• A.Write vague and open-ended instructions to encourage creativity.
• B.Use precise language and providing detailed context in prompts.
• C.Keep prompts as short as possible to avoid confusion.
• D.Combine multiple complex tasks into a single prompt.

Which technique is used to supply domain-specific knowledge to an LLM?

• A.Domain-adaptation training
• B.Prompt template expansion
• C.Retrieval-Augmented Generation
• D.Fine-tuning the model on general data

Which of the following sequence of steps does SAP recommend you use to solve a business problem using generative Al hub?

• A.Create a basic prompt in SAP AI Launchpad
  *Evaluate various models for the problem using generative-ai-hub-sdk
  *Scale the solution using generative-ai-hub-sdk
  *Create a baseline evaluation method for the simple prompt
  *Enhance the prompts.
• B.Create a basic prompt in SAP AI Launchpad
  *Scale the solution using generative-ai-hub-sdk
  *Create a baseline evaluation method for the simple prompt
  *Enhance the prompts
  *Evaluate various models for the problem using generative-ai-hub-sdk
• C.Create a basic prompt in SAP AI Launchpad
  *Enhance the prompts
  *Create a baseline evaluation method for the simple prompt
  *Evaluate various models for the problem using generative-ai-hub-sdk
  *Scale the solution using generative-ai-hub-sdk

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> {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"])

• A.Train a language model from scratch
• B.Evaluate the performance of a language model using few-shot learning
• C.Generate random examples for language model training
• D.Preprocess a dataset for machine learning