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[General] Useful SAP C-AIG-2412 Valid Practice Questions Are Leading Materials & First

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【General】 Useful SAP C-AIG-2412 Valid Practice Questions Are Leading Materials & First

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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 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 2
  • 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 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.

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SAP Certified Associate - SAP Generative AI Developer Sample Questions (Q20-Q25):NEW QUESTION # 20
How do resource groups in SAP AI Core improve the management of machine learning workloads? Note:
There are 2 correct answers to this question.
  • A. They ensure workload separation for different tenants or departments.
  • B. They enable simultaneous orchestration of Kubernetes clusters.
  • C. They enhance pipeline execution speeds through workload distribution.
  • D. They provide isolation for datasets and Al artifacts.
Answer: A,D
Explanation:
Resource groups in SAP AI Core play a vital role in managing machine learning workloads by offering mechanisms for separation and isolation, which are essential for maintaining efficiency and security.
1. Ensuring Workload Separation for Different Tenants or Departments:
* Multitenancy Support:Resource groups enable the segregation of workloads among various tenants or departments within an organization, ensuring that each unit's processes are isolated and managed independently.
* Operational Efficiency:This separation prevents interference between workloads, allowing for tailored resource allocation and management strategies that meet the specific needs of each tenant or department.

NEW QUESTION # 21
Which of the following capabilities does the generative Al hub provide to developers? Note: There are 2 correct answers to this question.
  • A. Tools for prompt engineering and experimentation
  • B. Integration of foundation models into applications
  • C. Proprietary LLMs exclusively
  • D. Code generation to extend SAP BTP applications
Answer: A,D

NEW QUESTION # 22
What is the primary function of the generative Al hub in SAP's Al Foundation?
  • A. To store embeddings of unstructured data for semantic data retrieval
  • B. To serve as an abstraction layer to access a range of foundation Al models
  • C. To provide ready-to-use Al services for document processing
  • D. To manage the Al lifecycle efforts end-to-end
Answer: B
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 Orchestrationevelopers 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 # 23
Why is generative Al gaining significant attention and investment in the current business landscape? Note:
There are 2 correct answers to this question.
  • A. It can run entire business operations without human intervention.
  • B. It lowers barriers to adoption.
  • C. It only requires natural language skills to use.
  • D. It can replicate complex technical skills without training or quality control.
Answer: B,C
Explanation:
Generative AI is attracting significant attention and investment in the current business landscape due to several compelling factors:
1. Lowering Barriers to Adoption:
* Accessibility of Tools:The proliferation of user-friendly generative AI tools has made advanced AI capabilities accessible to a broader audience, including those without specialized technical expertise.
* Integration with Existing Systems:Generative AI solutions, such as SAP's Joule, are designed to integrate seamlessly with existing business systems, reducing the complexity and cost associated with adoption.
2. Natural Language Interaction:
* Ease of Use:Generative AI models are capable of understanding and processing natural language inputs, allowing users to interact with AI systems using everyday language. This reduces the need for specialized training and enables more intuitive user experiences.
* Enhanced User Engagement:The ability to communicate with AI systems in natural language fosters greater user engagement and facilitates the integration of AI into daily business operations.

NEW QUESTION # 24
How can few-shot learning enhance LLM performance?
  • A. By providing a large training set to improve generalization
  • B. By reducing overfitting through regularization techniques
  • C. By offering input-output pairs that exemplify the desired behavior
  • D. By enhancing the model's computational efficiency
Answer: C
Explanation:
Few-shot learning enhances the performance of Large Language Models (LLMs) by providing them with a limited number of input-output examples that demonstrate the desired task behavior.
1. Mechanism of Few-Shot Learning:
* Exemplification:By supplying a few examples, the model gains insight into the task requirements, enabling it to generalize from these instances to handle new, unseen inputs effectively.
* Adaptability:This approach allows LLMs to adapt to specific tasks without extensive retraining, making them versatile across various applications.
2. Benefits in Performance Enhancement:
* Improved Accuracy:With clear examples, the model's predictions align more closely with the desired outcomes, reducing errors.
* Efficiency:Few-shot learning minimizes the need for large datasets, accelerating the development process and conserving computational resources.

NEW QUESTION # 25
......
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