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SAP C_AIG_2412 Exam Syllabus Topics:

Topic
Details

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.

SAP Certified Associate - SAP Generative AI Developer Sample Questions (Q56-Q61):

NEW QUESTION # 56
What are some metrics to evaluate the effectiveness of a Retrieval Augmented Generation system? Note: There are 2 correct answers to this question.

A. Faithfulness
B. Carbon footprint
C. Relevance
D. Speed

Answer: A,C

NEW QUESTION # 57
You want to extract useful information from customer emails to augment existing applications in your company.
How can you use generative-ai-hub-sdk in this context?

A. Generate JSON strings based on extracted information.
B. Generate random email content and send them to customers.
C. Generate a new SAP application based on the mail data.
D. Train custom models based on the mail data.

Answer: A

NEW QUESTION # 58
What is a part of LLM context optimization?

A. Providing the model with domain-specific knowledge needed to solve a problem
B. Adjusting the model's output format and style
C. Reducing the model's size to improve efficiency
D. Enhancing the computational speed of the model

Answer: A

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 Relevance:Providing 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 # 59
How can few-shot learning enhance LLM performance?

A. By enhancing the model's computational efficiency
B. By offering input-output pairs that exemplify the desired behavior
C. By reducing overfitting through regularization techniques
D. By providing a large training set to improve generalization

Answer: B

NEW QUESTION # 60
What is the primary function of the generative Al hub in SAP's Al Foundation?

A. To serve as an abstraction layer to access a range of foundation Al models
B. To manage the Al lifecycle efforts end-to-end
C. To provide ready-to-use Al services for document processing
D. To store embeddings of unstructured data for semantic data retrieval

Answer: A

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 Orchestration:Developers 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 # 61
......

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