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[General] ACE THE SAP C-AIG-2412 EXAM BY CONSIDERING THE BEST PLATFORM

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【General】 ACE THE SAP C-AIG-2412 EXAM BY CONSIDERING THE BEST PLATFORM

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SAP C-AIG-2412 Exam Syllabus Topics:
TopicDetails
Topic 1
  • Advanced AI Techniques with SAP’s Generative AI Hub: This section of the exam measures the skills of Solution Architects and covers advanced techniques available through SAP’s Generative AI Hub. Candidates are assessed on their ability to design, optimize, and scale generative AI solutions that go beyond basic implementations. The focus includes applying sophisticated strategies to integrate advanced models, manage performance, and align AI-driven outcomes with complex enterprise goals.
Topic 2
  • Large Language Models (LLMs): This section of the exam measures the skills of AI Developers and covers the practical use of large language models in SAP environments. Candidates are expected to understand how LLMs can be applied to automate tasks, enhance decision-making, and improve user interaction within SAP systems. The exam evaluates knowledge of handling model selection, fine-tuning, and adapting LLMs to specific business cases.
Topic 3
  • SAP's Generative AI Hub: This section of the exam measures the skills of Solution Architects and covers SAP’s Generative AI Hub, which acts as the central layer for designing and managing generative AI solutions. The exam tests knowledge of building, deploying, and connecting AI models to business scenarios through the Hub. Emphasis is placed on leveraging the Hub to streamline workflows and ensure scalable solutions that align with organizational needs.
Topic 4
  • SAP AI Core: This section of the exam measures the skills of AI Developers and covers the fundamental components of SAP AI Core. Candidates are assessed on their ability to work with the core services that allow machine learning models to be deployed and managed within SAP environments. The focus is on understanding how AI Core fits into SAP’s ecosystem and ensures smooth integration with enterprise applications.

SAP Certified Associate - SAP Generative AI Developer Sample Questions (Q63-Q68):NEW QUESTION # 63
Match the components of a Retrieval Augmented Generation architecture to the diagram.

Answer:
Explanation:


NEW QUESTION # 64
What does SAP recommend you do before you start training a machine learning model in SAP AI Core?
Note: There are 3 correct answers to this question.
  • A. Configure the model deployment in SAP Al Launchpad.
  • B. Define the required infrastructure resources for training.
  • C. Perform manual data integration with SAP HANA.
  • D. Register the input dataset in SAP AI Core.
  • E. Configure the training pipeline using templates.
Answer: B,D,E
Explanation:
Before initiating the training of a machine learning model in SAP AI Core, SAP recommends the following steps:
* Configure the training pipeline using templates:Utilize predefined templates to set up the training pipeline, ensuring consistency and efficiency in the training process.
* Define the required infrastructure resources for training:Specify the computational resources, such as CPUs or GPUs, necessary for the training job to ensure optimal performance.
* Register the input dataset in SAP AI Core:Ensure that the dataset intended for training is properly registered within SAP AI Core, facilitating seamless access during the training process.
These preparatory steps are crucial for the successful training of machine learning models within the SAP AI Core environment.

NEW QUESTION # 65
Which of the following are grounding principles included in SAP's AI Ethics framework?
Note: There are 3 correct answers to this question.
  • A. Human agency and oversight
  • B. Maximize business profits
  • C. Store all user data for legal proceedings
  • D. Avoid bias and discrimination
  • E. Transparency and explainability
Answer: A,D,E

NEW QUESTION # 66
Which neural network architecture is primarily used by LLMs?
  • A. Convolutional Neural Networks (CNNs)
  • B. Transformer architecture with self-attention mechanisms
  • C. Recurrent neural network architecture
  • D. Sequential encoder-decoder architecture
Answer: B
Explanation:
Large Language Models (LLMs) primarily utilize the Transformer architecture, which incorporates self- attention mechanisms.
1. Transformer Architecture:
* Overview:Introduced in 2017, the Transformer architecture revolutionized natural language processing by enabling models to handle long-range dependencies in text more effectively than previous architectures.
GeeksforGeeks
* Components:The Transformer consists of an encoder-decoder structure, where the encoder processes input sequences, and the decoder generates output sequences.
2. Self-Attention Mechanisms:
* Functionality:Self-attention allows the model to weigh the importance of different words in a sequence relative to each other, enabling it to capture contextual relationships regardless of their position.
* Benefits:This mechanism facilitates parallel processing of input data, improving computational efficiency and performance in understanding complex language patterns.
3. Application in LLMs:
* Model ExamplesLMs such as GPT-3 and BERT are built upon the Transformer architecture, leveraging self-attention to process and generate human-like text.
* Advantages:The Transformer architecture's ability to manage extensive context and dependencies makes it well-suited for tasks like language translation, summarization, and question-answering.

NEW QUESTION # 67
What does the Prompt Management feature of the SAP AI launchpad allow users to do?
  • A. Access and manage saved prompts and their versions
  • B. Create and edit prompts
  • C. Provide personalized user interactions
  • D. Interact with models through a conversational interface
Answer: A,B

NEW QUESTION # 68
......
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