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Title: C-AIG-2412 Test Price, Latest C-AIG-2412 Learning Material
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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
  • 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 3
  • 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 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 (Q63-Q68):NEW QUESTION # 63
Which technique is used to supply domain-specific knowledge to an LLM?
Answer: D
Explanation:
Retrieval-Augmented Generation (RAG) is a technique that enhances Large Language Models (LLMs) by integrating external domain-specific knowledge, enabling more accurate and contextually relevant outputs.
1. Understanding Retrieval-Augmented Generation (RAG):
* Definition:RAG combines the generative capabilities of LLMs with retrieval mechanisms that access external knowledge bases or documents. This integration allows the model to incorporate up-to-date and domain-specific information into its responses.
* Mechanism:When presented with a query, the RAG system retrieves pertinent information from external sources and uses this data to inform and generate a more accurate and contextually appropriate response.
2. Application in Supplying Domain-Specific Knowledge:
* Domain Adaptation:By leveraging RAG, LLMs can access specialized information without the need for extensive retraining or fine-tuning. This approach is particularly beneficial for domains with rapidly evolving information or where incorporating proprietary data is essential.
* Efficiency:RAG enables models to provide informed responses by referencing external data, reducing the necessity for large-scale domain-specific training datasets and thereby conserving computational resources.
3. Advantages of Using RAG:
* Up-to-Date Information:Since RAG systems can query current data sources, they are capable of providing the most recent information available, which is crucial in dynamic fields.
* Enhanced Accuracy:Incorporating external knowledge allows the model to produce more precise and contextually relevant outputs, especially in specialized domains.

NEW QUESTION # 64
What are some functionalities provided by SAP Al Core? Note: There are 3 correct answers to this question.
Answer: A,D,E
Explanation:
You're asking about the key functionalities of SAP AI Core. Here's a breakdown of the correct answers:
* A. Integration of AI services with business applications using a standardized API:SAP AI Core provides a standardized way to connect AI models and services to your existing business applications.
This means you can easily integrate AI capabilities into your core business processes, regardless of the specific AI technology you're using. This is done through APIs (Application Programming Interfaces), which allow different software systems to communicate with each other.
* B. Continuous delivery and tenant isolation for scalability:
* Continuous delivery:AI Core supports continuous delivery, which means you can quickly and easily deploy and update your AI models. This allows you to adapt to changing business needs and keep your AI solutions up-to-date.
* Tenant isolation:AI Core provides tenant isolation, which is important for security and scalability. This means that different users or departments within your organization can have their own separate AI environments, preventing interference and ensuring data privacy.
* C. Orchestration of AI workflows such as model training and inference:AI Core helps you manage the entire lifecycle of your AI models, including:
* Training:Automating the process of training your AI models on large datasets.
* Inferenceeploying your trained models and using them to make predictions or generate insights.
* Monitoring:Tracking the performance of your AI models over time.
Why the other options are incorrect:
* D. Management of SAP S/4HANA cloud infrastructure:While AI Core can be used with S/4HANA, it's not specifically designed to manage the cloud infrastructure of S/4HANA. That's handled by other SAP services.
* E. Monitoring and retraining models in SAP AI Core:While AI Core supports monitoring, the retraining of models is typically done using other tools and services within the SAP AI ecosystem.

NEW QUESTION # 65
What is a part of LLM context optimization?
Answer: B
Explanation:
LLM context optimization involves tailoring a Large Language Model's (LLM) input context to enhance its performance on specific tasks, particularly by incorporating domain-specific knowledge.
1. Understanding LLM Context Optimization:
* Definition:Context optimization refers to the process of adjusting the input provided to an LLM to ensure it includes relevant information, thereby enabling the model to generate more accurate and contextually appropriate outputs.
* Domain-Specific Knowledge Integration:By embedding domain-specific information into the model's context, the LLM can better understand and address specialized queries, leading to improved problem- solving capabilities.
2. Importance of Domain-Specific Knowledge:
* Enhanced Relevanceroviding domain-specific context ensures that the model'sresponses are pertinent to the particular field or subject matter, increasing the utility of the generated content.
* Improved Accuracy:With access to specialized knowledge, the LLLM is less likely to produce generic or incorrect answers, thereby enhancing the overall quality of its outputs.
3. Methods of Context Optimization:
* Prompt Engineering:Crafting prompts that include necessary domain-specific information to guide the model towards generating desired responses.
* Retrieval-Augmented Generation (RAG):Incorporating external data sources into the model's context to provide up-to-date and relevant information pertinent to the domain.

NEW QUESTION # 66
Which of the following steps must be performed to deploy LLMs in the generative Al hub?
Answer: D
Explanation:
Deploying Large Language Models (LLMs) in SAP's Generative AI Hub involves a structured process:
1. Provision SAP AI Core:
* Setup:Ensure that SAP AI Core is provisioned in your SAP Business Technology Platform (BTP) account to manage AI workloads.
2. Check for Foundation Model Scenario:
* Validation:Verify the availability of the foundation model scenario within SAP AI Core to confirm that the necessary resources and configurations are in place for deploying LLMs.
3. Create a Configuration:
* Configuration Setupefine the parameters and settings required for the LLM deployment, including model specifications and resource allocations.
4. Create a Deployment:
* Deployment Execution:Initiate the deployment process within SAP AI Core, making the LLM available for integration and use within your applications.

NEW QUESTION # 67
What are some benefits of the SAP AI Launchpad? Note: There are 2 correct answers to this question.
Answer: A,B
Explanation:
SAP AI Launchpad offers several benefits that enhance the development, deployment, and management of AI models within an organization.
1. Centralized AI Lifecycle Management for All AI Scenarios:
* Unified Platform:SAP AI Launchpad provides a centralized platform to manage the entire AI lifecycle, including model development, training, deployment, monitoring, and maintenance.
* Efficiency:This centralized approach streamlines workflows, reduces complexity, and ensures consistency across various AI projects and scenarios.

NEW QUESTION # 68
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