LATEST C-AIG-2412 DUMPS QUESTIONS, VALID C-AIG-2412 EXAM QUESTION

Latest C-AIG-2412 Dumps Questions, Valid C-AIG-2412 Exam Question

Latest C-AIG-2412 Dumps Questions, Valid C-AIG-2412 Exam Question

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Tags: Latest C-AIG-2412 Dumps Questions, Valid C-AIG-2412 Exam Question, C-AIG-2412 Valid Test Vce, Pass C-AIG-2412 Test Guide, Exam C-AIG-2412 Topics

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SAP C-AIG-2412 Exam Syllabus Topics:

TopicDetails
Topic 1
  • Large Language Models (LLMs): This section of the exam measures the skills of AI Developers and covers the evolution of large language models, distinguishing them from traditional IT operations analytics. It also explores the current stages of AIOps systems and their implications for organizations. A key skill assessed is understanding the foundational concepts behind LLMs and their applications in various contexts.
Topic 2
  • SAP's Generative AI Hub: This section of the exam measures the skills of technology strategists and covers the functionalities provided by SAP's Generative AI Hub. It emphasizes how organizations can use generative AI to create new content and automate complex tasks. A vital skill evaluated is applying generative AI techniques to enhance business processes and customer experiences.
Topic 3
  • SAP AI Core: This section of the exam measures the skills of SAP developers and covers the core components of SAP's AI framework. It emphasizes how these components integrate with existing systems to enhance functionality and performance. Leveraging SAP AI Core to develop intelligent applications that meet business needs is a critical skill evaluated.
Topic 4
  • SAP Business AI: This section of the exam measures the skills of business analysts and covers the features and capabilities of SAP Business AI. It includes exploring how AI can automate processes, provide real-time insights, and enhance decision-making across various business functions.

>> Latest C-AIG-2412 Dumps Questions <<

Free PDF Quiz SAP - C-AIG-2412 - SAP Certified Associate - SAP Generative AI Developer Pass-Sure Latest Dumps Questions

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SAP Certified Associate - SAP Generative AI Developer Sample Questions (Q30-Q35):

NEW QUESTION # 30
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. Generate random examples for language model training
  • B. Evaluate the performance of a language model using few-shot learning
  • C. Train a language model from scratch
  • D. Preprocess a dataset for machine learning

Answer: B


NEW QUESTION # 31
What are the benefits of SAP's generative Al hub? Note: There are 2 correct answers to this question.

  • A. Provide libraries for no-code development
  • B. Build custom Al solutions and extend SAP applications
  • C. Accelerate Al development with flexible access to a broad range of models
  • D. Send your data to various LLM providers for training feedback

Answer: B,C


NEW QUESTION # 32
Where can you configure language models in generative Al hub?

  • A. The Configuration tab of the SAP BTP cockpit
  • B. The Models tab in Prompt Editor
  • C. The Orchestration tab in SAP AI Launchpad
  • D. The Configuration tab within ML Operations in SAP AI Launchpad

Answer: D

Explanation:
In SAP's Generative AI Hub, configuring language models is a crucial step for integrating AI capabilities into applications.
1. Accessing the Configuration Interface:
* ML Operations Section:Within SAP AI Launchpad, navigate to theML Operationssection to manage machine learning models and operations.
* Configuration Tab:In theConfigurationtab, you can set up and manage various aspects of language models, including selecting model providers, specifying model versions, and configuring deployment settings.
2. Steps to Configure Language Models:
* Model Selection:Choose the desired language model from the available options, such as GPT-4 or other large language models (LLMs) provided through the Generative AI Hub.
* Parameter Configuration:Set parameters like model version, input constraints, and performance settings to tailor the model's behavior to your application's requirements.
* Deployment Setup:Configure deployment details to ensure the model is accessible andintegrated appropriately within your application's infrastructure.


NEW QUESTION # 33
What are some use cases for fine-tuning of a model?
Note: There are 2 correct answers to this question.

  • A. To sanitize model outputs
  • B. To introduce new knowledge to a model in a resource-efficient way
  • C. To quickly create iterations on a new use case
  • D. To customize outputs for specific types of inputs

Answer: B,D


NEW QUESTION # 34
Which of the following is a principle of effective prompt engineering?

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

Answer: C

Explanation:
Effective prompt engineering is crucial for guiding AI models to produce accurate and relevant outputs.
1. Importance of Precision and Context:
* Clarity:Using precise language in prompts minimizes ambiguity, ensuring the AI model comprehends the exact requirements.
* Detailed Context:Providing comprehensive context helps the model understand the background and nuances of the task, leading to more accurate and tailored responses.
2. Best Practices in Prompt Engineering:
* Specificity:Clearly define the desired outcome, including any constraints or specific formats required.
* Instruction Inclusion:Incorporate explicit instructions within the prompt to guide the model's behavior effectively.
* Avoiding Ambiguity:Steer clear of vague or open-ended language that could lead to varied interpretations.
3. Benefits of Effective Prompt Engineering:
* Enhanced Output Quality:Well-crafted prompts lead to responses that closely align with user expectations.
* Efficiency:Reduces the need for iterative refinements, saving time and computational resources.


NEW QUESTION # 35
......

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