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SAP C_AIG_2412 Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	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 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.

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

NEW QUESTION # 52
Which neural network architecture is primarily used by LLMs?

A. Sequential encoder-decoder architecture
B. Recurrent neural network architecture
C. Convolutional Neural Networks (CNNs)
D. Transformer architecture with self-attention mechanisms

Answer: D

Explanation:
Large Language Models (LLMs) primarily utilize the Transformer architecture, which incorporates self- attention mechanisms.
1. Transformer Architecture:
* Overview:Introduced in 2017, the Transformer architecture revolutionized natural language processing by enabling models to handle long-range dependencies in text more effectively than previous architectures.
GeeksforGeeks
* Components:The Transformer consists of an encoder-decoder structure, where the encoder processes input sequences, and the decoder generates output sequences.
2. Self-Attention Mechanisms:
* Functionality:Self-attention allows the model to weigh the importance of different words in a sequence relative to each other, enabling it to capture contextual relationships regardless of their position.
* Benefits:This mechanism facilitates parallel processing of input data, improving computational efficiency and performance in understanding complex language patterns.
3. Application in LLMs:
* Model Examples:LLMs such as GPT-3 and BERT are built upon the Transformer architecture, leveraging self-attention to process and generate human-like text.
* Advantages:The Transformer architecture's ability to manage extensive context and dependencies makes it well-suited for tasks like language translation, summarization, and question-answering.

NEW QUESTION # 53
What are some benefits of the SAP AI Launchpad? Note: There are 2 correct answers to this question.

A. Direct deployment of Al models to SAP HANA.
B. Integration with non-SAP platforms like Azure and AWS.
C. Centralized Al lifecycle management for all Al scenarios.
D. Simplified model retraining and performance improvement.

Answer: C,D

NEW QUESTION # 54
What is the primary function of the embedding model in a RAG system?

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

Answer: D

NEW QUESTION # 55
What are some metrics to evaluate the effectiveness of a Retrieval Augmented Generation system? Note: There are 2 correct answers to this question.

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

Answer: A,B

NEW QUESTION # 56
How does SAP ensure the enterprise-readiness of its Al solutions?

A. By using generic Al models without business context complying with Al ethics standards
B. By implementing rigorous product standards for Al capabilities
C. By ensuring that Al models make bias-free decisions without human input

Answer: B

NEW QUESTION # 57
......

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Useful C_AIG_2412 Reliable Dumps Pdf & Leader in Certification Exams Materials & First-Grade Exam C_AIG_2412 Labs

Exam C_AIG_2412 Labs, C_AIG_2412 Exam Collection Pdf

SAP C_AIG_2412 Exam Syllabus Topics:

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

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