C_AIG_2412 LATEST EXAM PRACTICE & VALUABLE C_AIG_2412 FEEDBACK

C_AIG_2412 Latest Exam Practice & Valuable C_AIG_2412 Feedback

C_AIG_2412 Latest Exam Practice & Valuable C_AIG_2412 Feedback

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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 (Q10-Q15):

NEW QUESTION # 10
What are some advantages of using agents in training models? Note: There are 2 correct answers to this question.

  • A. To improve the quality of results
  • B. To eliminate the need for human oversight
  • C. To streamline LLM workflows
  • D. To guarantee accurate decision making in complex scenarios

Answer: A,C

Explanation:
Incorporating agents into the training and deployment of Large Language Models (LLMs) offers notable advantages:
1. Improving the Quality of Results:
* Specialized Task Handling:Agents can be designed to manage specific tasks or subtasks within a larger process, ensuring that each component is handled with expertise, thereby enhancing the overall quality of the output.
* Error Reduction:By delegating particular functions to specialized agents, the likelihood of errors decreases, leading to more accurate and reliable results.
2. Streamlining LLM Workflows:
* Process Automation:Agents can automate repetitive or time-consuming tasks within the LLM workflow, increasing efficiency and allowing human resources to focus on more complex aspects of model development and deployment.
* Workflow Management:Agents facilitate the coordination of various stages in the LLM pipeline, ensuring seamless transitions between tasks and improving overall workflow efficiency.
3. Enhancing Model Performance:
* Adaptive Learning:Agents can monitor model performance and implement adjustments in real-time, promoting continuous improvement and adaptability to new data or requirements.
* Resource Optimization:By managing specific tasks, agents help in optimizing computational resources, ensuring that the LLM operates efficiently without unnecessary expenditure of processing power.


NEW QUESTION # 11
What are some use cases for fine-tuning of a model? Note: There are 2 correct answers to this question.

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

Answer: A,D


NEW QUESTION # 12
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. Define the required infrastructure resources for training.
  • B. Configure the model deployment in SAP Al Launchpad.
  • C. Register the input dataset in SAP AI Core.
  • D. Configure the training pipeline using templates.
  • E. Perform manual data integration with SAP HANA.

Answer: A,C,D


NEW QUESTION # 13
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. Interact with models through a conversational interface
  • C. Create and edit prompts
  • D. Provide personalized user interactions

Answer: A,C


NEW QUESTION # 14
What is the purpose of splitting documents into smaller overlapping chunks in a RAG system?

  • A. To enable the matching of different relevant passages to user queries
  • B. To simplify the process of training the embedding model
  • C. To reduce the storage space required for the vector database
  • D. To improve the efficiency of encoding queries into vector representations

Answer: A


NEW QUESTION # 15
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