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C-AIG-2412 Exam Pass4sure & 2025 SAP Realistic SAP Certified Associate - SAP Generative AI Developer Exam Pass4sure

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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 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 2

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 3

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.

Topic 4

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.

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SAP Certified Associate - SAP Generative AI Developer Sample Questions (Q52-Q57):

NEW QUESTION # 52
What are some functionalities provided by SAP Al Core? Note: There are 3 correct answers to this question.

A. Management of SAP S/4HANA cloud infrastructure
B. Orchestration of Al workflows such as model training and inference
C. Continuous delivery and tenant isolation for scalability
D. Integration of Al services with business applications using a standardized API
E. Monitoring and retraining models in SAP Al Core

Answer: B,C,D

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.
* Inference:Deploying 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 # 53
What are some SAP recommendations to evaluate pricing and rate information of model usage within SAP's generative Al hub?
Note: There are 2 correct answers to this question.

A. Adopt best practice pricing strategies, such as outcome-based pricing
B. Use pricing models that have fixed rates irrespective of the usage patterns
C. Avoid subscription-based pricing models
D. Weigh the cost of using advanced models against the expected return on investment

Answer: A,D

Explanation:
When evaluating pricing and rate information for model usage within SAP's Generative AI Hub, SAP recommends:
1. Adopting Best Practice Pricing Strategies:
* Outcome-Based Pricing:Implementing pricing strategies that align costs with achieved outcomes ensures that expenditures are directly tied to the value derived from AI models.
2. Assessing Cost Against Expected Return on Investment (ROI):
* Cost-Benefit Analysis:Carefully evaluating the expenses associated with advanced models in relation to the anticipated ROI helps in making informed decisions about model selection and usage.
Conclusion:
By adopting best practice pricing strategies and assessing costs against expected ROI, businesses can make informed decisions regarding model usage within SAP's Generative AI Hub, ensuring cost-effectiveness and value alignment.

NEW QUESTION # 54
Which technique is used to supply domain-specific knowledge to an LLM?

A. Retrieval-Augmented Generation
B. Domain-adaptation training
C. Fine-tuning the model on general data
D. Prompt template expansion

Answer: A

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 # 55
What are some use cases for fine-tuning of a model? Note: There are 2 correct answers to this question.

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

Answer: A,C

NEW QUESTION # 56
Match the components of a Retrieval Augmented Generation architecture to the diagram.

Answer:

Explanation:

NEW QUESTION # 57
......

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Rick Bell Rick Bell

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