SAP C_AIG_2412 Questions - Quick Tips To Pass [2025]

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SAP C_AIG_2412 Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	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 3	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.
Topic 4	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.

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SAP Certified Associate - SAP Generative AI Developer Sample Questions (Q53-Q58):

NEW QUESTION # 53
Where can you configure language models in generative Al hub?

A. The Configuration tab within ML Operations in SAP AI Launchpad
B. The Orchestration tab in SAP AI Launchpad
C. The Configuration tab of the SAP BTP cockpit
D. The Models tab in Prompt Editor

Answer: A

Explanation:
In SAP's Generative AI Hub, configuring language models is a crucial step for integrating AI capabilities into applications.
1. Accessing the Configuration Interface:
* ML Operations Section:Within SAP AI Launchpad, navigate to theML Operationssection to manage machine learning models and operations.
* Configuration Tab:In theConfigurationtab, you can set up and manage various aspects of language models, including selecting model providers, specifying model versions, and configuring deployment settings.
2. Steps to Configure Language Models:
* Model Selection:Choose the desired language model from the available options, such as GPT-4 or other large language models (LLMs) provided through the Generative AI Hub.
* Parameter Configuration:Set parameters like model version, input constraints, and performance settings to tailor the model's behavior to your application's requirements.
* Deployment Setup:Configure deployment details to ensure the model is accessible andintegrated appropriately within your application's infrastructure.

NEW QUESTION # 54
What must be defined in an executable to train a machine learning model using SAP AI Core? Note:
There are 2 correct answers to this question.

A. Infrastructure resources such as CPUs or GPUs
B. Deployment templates for SAP AI Launchpad
C. User scripts to manually execute pipeline steps
D. Pipeline containers to be used

Answer: A,D

NEW QUESTION # 55
Which technique is used to supply domain-specific knowledge to an LLM?

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

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

Answer:

Explanation:

NEW QUESTION # 57
What advantage can you gain by leveraging different models from multiple providers through the SAP's generative Al hub?

A. Train new models using SAP and non-SAP data
B. Enhance the accuracy and relevance of Al applications that use SAP's data assets
C. Get more training data for new models
D. Design new product interfaces for SAP applications

Answer: B

NEW QUESTION # 58
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