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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 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

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 4

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.

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SAP Certified Associate - SAP Generative AI Developer Sample Questions (Q11-Q16):

NEW QUESTION # 11
You want to use the orchestration service through SAP's generative-Al-hub-sdk. What does the following code do?
from gen_ai_hub.orchestration.models.11m import LLM 11m =
LLM(name="gpt-40", version="latest", parameters={"max_tokens": 256, "temperature": 0.2})

A. Define the Template and Default Input Values
B. Create the Orchestration Configuration
C. Run the Orchestration Request
D. Define the LLM

Answer: D

NEW QUESTION # 12
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 provide isolation for datasets and Al artifacts.
B. They ensure workload separation for different tenants or departments.
C. They enable simultaneous orchestration of Kubernetes clusters.
D. They enhance pipeline execution speeds through workload distribution.

Answer: A,B

NEW QUESTION # 13
What does SAP recommend you do before you start training a machine learning model in SAP AI Core? Note: There are 3 correct answers to this question.

A. Define the required infrastructure resources for training.
B. Configure the training pipeline using templates.
C. Configure the model deployment in SAP Al Launchpad.
D. Perform manual data integration with SAP HANA.
E. Register the input dataset in SAP AI Core.

Answer: A,B,E

NEW QUESTION # 14
What is the primary function of the embedding model in a RAG system?

A. To store vector representations of documents and search for relevant passages
B. To evaluate the faithfulness and relevance of generated Answers
C. To encode queries and documents into vector representations for comparison
D. To generate responses based on retrieved documents and user queries

Answer: C

Explanation:
In a Retrieval-Augmented Generation (RAG) system, the embedding model plays a crucial role in encoding textual data into vector representations, facilitating efficient retrieval and comparison.
1. Function of the Embedding Model:
* Vector Encoding:The embedding model transforms both user queries and documents into high- dimensional vector representations. This numerical encoding captures the semantic meaning of the text, enabling the system to assess similarities between different pieces of text effectively.
* Facilitating Retrieval:By encoding text into vectors, the system can perform efficient similarity searches within a vector database, identifying documents or passages that are most relevant to the user's query.
2. Importance in RAG Systems:
* Semantic Matching:The vector representations allow the system to match user queries with relevant documents based on semantic content rather than mere keyword overlap, enhancing the relevance of retrieved information.
* Efficiency:Vector-based retrieval is computationally efficient, enabling rapid identificationof pertinent information from large datasets, which is essential for real-time applications.
3. Application in SAP's Generative AI Hub:
* Integration with HANA Vector Search:SAP's Generative AI Hub integrates embedding models with HANA's vector search capabilities, allowing for efficient storage and retrieval of vector embeddings.
This integration supports the development of RAG systems that can effectively utilize SAP's data assets.
* Generative AI Hub SDK:SAP provides an SDK that facilitates the implementation of embedding models within RAG systems, enabling developers to encode queries and documents into vector representations seamlessly.

NEW QUESTION # 15
How can few-shot learning enhance LLM performance?

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

Answer: A

NEW QUESTION # 16
......

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Sean Ward Sean Ward

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