PRACTICAL C_AIG_2412 INFORMATION & TOP C_AIG_2412 QUESTIONS

Practical C_AIG_2412 Information & Top C_AIG_2412 Questions

Practical C_AIG_2412 Information & Top C_AIG_2412 Questions

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Tags: Practical C_AIG_2412 Information, Top C_AIG_2412 Questions, Latest C_AIG_2412 Exam Book, C_AIG_2412 Reliable Test Practice, C_AIG_2412 Reasonable Exam Price

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SAP C_AIG_2412 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 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
  • 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 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.

SAP Certified Associate - SAP Generative AI Developer Sample Questions (Q32-Q37):

NEW QUESTION # 32
Which neural network architecture is primarily used by LLMs?

  • A. Convolutional Neural Networks (CNNs)
  • B. Recurrent neural network architecture
  • C. Sequential encoder-decoder architecture
  • D. Transformer architecture with self-attention mechanisms

Answer: D


NEW QUESTION # 33
What are some components of the training pipeline in SAP AI Core?
Note: There are 2 correct answers to this question.

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

Answer: A,B


NEW QUESTION # 34
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
  • B. Create a basic prompt in SAP AI Launchpad
  • C. Create a basic prompt in SAP AI Launchpad

Answer: B


NEW QUESTION # 35
What is the primary function of the embedding model in a RAG system?

  • A. To generate responses based on retrieved documents and user queries
  • B. To evaluate the faithfulness and relevance of generated answers
  • C. To store vector representations of documents and search for relevant passages
  • D. To encode queries and documents into vector representations for comparison

Answer: D


NEW QUESTION # 36
What is the primary function of the generative Al hub in SAP's Al Foundation?

  • A. To manage the Al lifecycle efforts end-to-end
  • B. To provide ready-to-use Al services for document processing
  • C. To store embeddings of unstructured data for semantic data retrieval
  • D. To serve as an abstraction layer to access a range of foundation Al models

Answer: D

Explanation:
SAP's Generative AI Hub, a component of the SAP AI Foundation, provides a unified platform for accessing various foundation AI models.
1. Abstraction Layer Functionality:
* Unified Access:The Generative AI Hub acts as an abstraction layer, offering developers seamless access to a diverse range of large language models (LLMs) from multiple providers, such as GPT-4 by Azure OpenAI and open-source models like Falcon-40b.
* Simplified Integration:By providing a standardized interface, the hub simplifies the integration of these models into applications, reducing complexity and development time.
2. Supporting AI Development:
* Toolset Availability:The hub offers a comprehensive toolset for building custom AI solutions, including prompt engineering tools, SDKs, and fine-tuning services, facilitating efficient AI development.
* Model Orchestration:Developers can orchestrate multiple models, enabling the selection of the most suitable model for specific use cases, thereby enhancing the accuracy and relevance of AI applications.


NEW QUESTION # 37
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