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C_AIG_2412 Interactive Course - C_AIG_2412 Test Study Guide

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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

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 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

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 (Q61-Q66):

NEW QUESTION # 61
How do resource groups in SAP AI Core improve the management of machine learning workloads? Note: There are 2 correct answers to this question.

A. They enable simultaneous orchestration of Kubernetes clusters.
B. They ensure workload separation for different tenants or departments.
C. They provide isolation for datasets and Al artifacts.
D. They enhance pipeline execution speeds through workload distribution.

Answer: B,C

NEW QUESTION # 62
Which of the following are functionalities provided by the generative-Al-hub-SDK ? Note: There are 2 correct answers to this question.

A. Interact with LLMs
B. Customize SAP AI Launchpad
C. Create chat responses and embeddings
D. Configure SAP BTP credentials

Answer: A,C

NEW QUESTION # 63
What can be done once the training of a machine learning model has been completed in SAP AI Core? Note: There are 2 correct answers to this question.

A. The model can be registered in the hyperscaler object store.
B. The model can be deployed in SAP HANA.
C. The model can be deployed for inferencing.
D. The model's accuracy can be optimized directly in SAP HANA.

Answer: A,C

NEW QUESTION # 64
Which of the following is unique about SAP's approach to Al?

A. Utilizing Al mainly for marketing purposes.
B. Offering Al capabilities in their future products as of 2025.
C. Focusing Al solely on customer support services.
D. SAP's deep integration of Al with business processes and analytics.

Answer: D

Explanation:
SAP distinguishes itself by deeply embedding Artificial Intelligence (AI) into its core business processes and analytics, enhancing efficiency and decision-making across various enterprise functions.
1. Integration of AI into Business Processes:
* SAP Business AI:SAP focuses on solving customers' business problems by integrating AI directly into business processes, rather than offering general-purpose AI platforms. This approach ensures that AI solutions are tailored to specific business needs, enhancing process efficiency and effectiveness.
* SAP S/4HANA Integration:By embedding AI into SAP S/4HANA, SAP enables real-time data analysis and process optimization. This integration allows for improved supply chain efficiency, enhanced financial decision-making, and personalized customer experiences.
2. AI-Driven Analytics:
* SAP Analytics Cloud:This solution combines AI with analytics and planning, unlocking the full potential of business data. It provides advanced analytics capabilities, enabling businesses to make informed decisions based on real-time insights.
* Predictive Analytics Library:SAP HANA includes a Predictive Analytics Library with native algorithms for statistical measures, clustering, classification, and time series analysis. This facilitates advanced data processing and predictive analytics within business applications.
3. AI in Enterprise Applications:
* SAP SuccessFactors:AI is integrated into SAP SuccessFactors to enhance human resources processes, such as talent acquisition and employee engagement, by providing data-driven insights and automating routine tasks.
* SAP AI Business Services:These services offer reusable AI capabilities that can be integrated across various business processes, automating tasks like document processing andenriching customer experiences.

NEW QUESTION # 65
Which of the following statements accurately describe the RAG process? Note: There are 2 correct ans-wers to this question.

A. The embedding model stores the generated ans wers for future reference.
B. The retrieved content is combined with the LLM's capabilities to generate a response.
C. The user's questi on is used to search a knowledge base or a set of documents.
D. The LLM directly ans wers the user's question without accessing external information.

Answer: B,C

NEW QUESTION # 66
......

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