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SAP C_AIG_2412 Exam Syllabus Topics:| Topic | Details | | Topic 1 | - 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 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 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.
| | Topic 4 | - 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.
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SAP Certified Associate - SAP Generative AI Developer Sample Questions (Q16-Q21):NEW QUESTION # 16
What advantage can you gain by leveraging different models from multiple providers through the SAP's generative Al hub?
- A. Get more training data for new models
- B. Train new models using SAP and non-SAP data
- C. Design new product interfaces for SAP applications
- D. Enhance the accuracy and relevance of Al applications that use SAP's data assets
Answer: D
Explanation:
Leveraging different models from multiple providers through SAP's Generative AI Hub offers significant advantages:
1. Access to a Diverse Range of Large Language Models (LLMs):
* Integration with Multiple Providers:SAP's Generative AI Hub provides instant access to a broad spectrum of LLMs from various providers, such as GPT-4 by Azure OpenAI andopen-source models like Falcon-40b.
2. Enhancing Accuracy and Relevance:
* Model Selection Flexibility:By offering a variety of models, developers can select the most suitable one for their specific use cases, thereby enhancing the accuracy and relevance of AI applications that utilize SAP's data assets.
3. Seamless Orchestration and Integration:
* Orchestration Capabilities:The Generative AI Hub enables the orchestration of multiple models, allowing for seamless integration into SAP solutions like SAP S/4HANA and SAP SuccessFactors.
NEW QUESTION # 17
Which of the following is a benefit of using Retrieval Augmented Generation?
- A. It enables LLMs to learn new languages without additional training.
- B. It reduces the computational resources required for language modeling.
- C. It allows LLMs to access and utilize information beyond their initial training data.
- D. It eliminates the need for fine-tuning LLMs for specific tasks.
Answer: C
Explanation:
Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by enabling them to access and utilize information beyond their initial training data.
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. Benefits of RAG:
* Access to External Information:RAG allows LLMs to access and utilize information beyond their initial training data, enabling them to provide more accurate and relevant responses.
* 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.
* Improved Accuracy and Relevance:By leveraging external data, RAG enhances theaccuracy and relevance of the generated content, making it particularly useful for tasks requiring detailed or domain- specific information.
NEW QUESTION # 18
Which of the following techniques uses a prompt to generate or complete subsequent prompts (streamlining the prompt development process), and to effectively guide Al model responses?
- A. One-shot prompting
- B. Few-shot prompting
- C. Chain-of-thought prompting
- D. Meta prompting
Answer: D
Explanation:
Meta prompting is a technique in prompt engineering where a prompt is designed to generate or refine subsequent prompts.
1. Definition and Purpose:
* Streamlining Prompt Development:Meta prompting automates the creation of effective prompts by utilizing AI to generate or enhance them, thereby streamlining the prompt development process.
* Guiding AI Model Responses:By generating refined prompts, meta prompting effectively guides AI models to produce more accurate and contextually relevant responses.
2. Application in SAP's Generative AI Hub:
* Prompt Engineering Tools:SAP's Generative AI Hub provides tools that support advanced prompt engineering techniques, including meta prompting, to enhance AI model interactions.
NEW QUESTION # 19
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
Explanation:
The Prompt Management feature within SAP AI Launchpad's Generative AI Hub offers users comprehensive tools for handling prompts throughout their lifecycle:
1. Create and Edit Prompts:
* Prompt Editor:Users can utilize the Prompt Editor to craft and modify prompts, facilitating effective prompt engineering and experimentation.
2. Access and Manage Saved Prompts and Their Versions:
* Prompt Lifecycle Management:The platform provides capabilities to manage the lifecycle of prompts, including accessing saved prompts, tracking their versions, and organizing them for efficient reuse and iteration.
Conclusion:
SAP AI Launchpad's Prompt Management feature empowers users to create, edit, and manage prompts effectively, supporting robust prompt engineering and lifecycle management within the Generative AI Hub.
NEW QUESTION # 20
Which technique is used to supply domain-specific knowledge to an LLM?
- A. Fine-tuning the model on general data
- B. Retrieval-Augmented Generation
- C. Domain-adaptation training
- D. Prompt template expansion
Answer: B
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 # 21
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