2025 C_AIG_2412: SAP CERTIFIED ASSOCIATE - SAP GENERATIVE AI DEVELOPER REALISTIC VALID VCE 100% PASS QUIZ

2025 C_AIG_2412: SAP Certified Associate - SAP Generative AI Developer Realistic Valid Vce 100% Pass Quiz

2025 C_AIG_2412: SAP Certified Associate - SAP Generative AI Developer Realistic Valid Vce 100% Pass Quiz

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SAP C_AIG_2412 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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.
Topic 2
  • 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 that needs to be evaluated.
Topic 3
  • 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 4
  • 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.

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SAP Certified Associate - SAP Generative AI Developer Sample Questions (Q55-Q60):

NEW QUESTION # 55
What are some components of the training pipeline in SAP AI Core? Note: There are 2 correct answers to this question.

  • A. The SAP HANA database for model storage
  • B. Automated deployment to Kubernetes clusters
  • C. Executables that define the training process
  • D. Input datasets stored in a hyperscaler object store

Answer: C,D


NEW QUESTION # 56
Which of the following describes Large Language Models (LLMs)?

  • A. They rely on traditional rule-based algorithms to generate responses
  • B. They generate responses based on pre-defined templates without learning from data
  • C. They utilize deep learning to process and generate human-like text
  • D. They can only process numerical data and are not capable of understanding text

Answer: C

Explanation:
Large Language Models (LLMs) are advanced AI systems that leverage deep learning techniques, specifically transformer architectures with self-attention mechanisms, to process and generate human-like text. Option A is incorrect because LLMs do not rely on traditional rule-based systems; they learn patterns from vast datasets. Option C is false as LLMs are designed for text processing, not limited to numerical data. Option D is also inaccurate since LLMs generate responses based on learned patterns, not static templates. Option B is correct, reflecting how LLMs, like those accessible via SAP's Generative AI Hub, use deep learning to understand context, semantics, and generate coherent text for applications such as chatbots, translations, and content creation.


NEW QUESTION # 57
What advantage can you gain by leveraging different models from multiple providers through the SAP's generative Al hub?

  • A. Design new product interfaces for SAP applications
  • B. Train new models using SAP and non-SAP data
  • C. Enhance the accuracy and relevance of Al applications that use SAP's data assets
  • D. Get more training data for new models

Answer: C

Explanation:
Leveraging different models from multiple providers through SAP's Generative AI Hub offers significant advantages:
1. Access to a Diverse Range of Large Language Models (LLMs):
* Integration with Multiple Providers:SAP's Generative AI Hub provides instant access to a broad spectrum of LLMs from various providers, such as GPT-4 by Azure OpenAI andopen-source models like Falcon-40b.
2. Enhancing Accuracy and Relevance:
* Model Selection Flexibility:By offering a variety of models, developers can select the most suitable one for their specific use cases, thereby enhancing the accuracy and relevance of AI applications that utilize SAP's data assets.
3. Seamless Orchestration and Integration:
* Orchestration Capabilities:The Generative AI Hub enables the orchestration of multiple models, allowing for seamless integration into SAP solutions like SAP S/4HANA and SAP SuccessFactors.


NEW QUESTION # 58
Which of the following statements accurately describe the RAG process?
Note: There are 2 correct answers to this question.

  • A. The embedding model stores the generated answers for future reference.
  • B. The retrieved content is combined with the LLM's capabilities to generate a response.
  • C. The user's question is used to search a knowledge base or a set of documents.
  • D. The LLM directly answers the user's question without accessing external information.

Answer: B,C


NEW QUESTION # 59
What capabilities does the Exploration and Development feature of the generative Al hub provide? Note:
There are 2 correct answers to this question.

  • A. Prompt editor and management
  • B. Automatic model selection
  • C. Develop and debug ABAP code
  • D. Al playground and chat

Answer: A,D

Explanation:
The Exploration and Development feature of SAP's Generative AI Hub provides several capabilities to facilitate AI solution development:
1. AI Playground and Chat:
* Interactive Environment:The AI playground offers an interactive space for developers to experiment with various AI models, test prompts, and observe outputs in real-time.
* Conversational Interface:The chat functionality enables users to engage in dialogue with AI models, refining prompts and understanding model behavior through iterative interactions.
2. Prompt Editor and Management:
* Prompt Creation:The prompt editor allows developers to craft and modify prompts tailored to specific business needs, enhancing the precision of AI responses.
* Prompt Organization:Prompt management tools facilitate the organization, versioning, and storage of prompts, ensuring efficient retrieval and reuse in various projects.


NEW QUESTION # 60
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