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100% Pass-Rate Accurate C_AIG_2412 Answers | Accurate C_AIG_2412 Latest Test Preparation: SAP Certified Associate - SAP Generative AI Developer

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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 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 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'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.

SAP Certified Associate - SAP Generative AI Developer Sample Questions (Q25-Q30):

NEW QUESTION # 25
Which neural network architecture is primarily used by LLMs?

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

Answer: C

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 # 26
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 quickly create iterations on a new use case
C. To customize outputs for specific types of inputs
D. To sanitize model outputs

Answer: A,C

NEW QUESTION # 27
Which technique is used to supply domain-specific knowledge to an LLM?

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

Answer: D

NEW QUESTION # 28
What capabilities does the Exploration and Development feature of the generative Al hub provide? Note:
There are 2 correct answers to this question.

A. Prompt editor and management
B. Develop and debug ABAP code
C. Automatic model selection
D. Al playground and chat

Answer: A,D

Explanation:
The Exploration and Development feature of SAP's Generative AI Hub provides several capabilities to facilitate AI solution development:
1. AI Playground and Chat:
* Interactive Environment:The AI playground offers an interactive space for developers to experiment with various AI models, test prompts, and observe outputs in real-time.
* Conversational Interface:The chat functionality enables users to engage in dialogue with AI models, refining prompts and understanding model behavior through iterative interactions.
2. Prompt Editor and Management:
* Prompt Creation:The prompt editor allows developers to craft and modify prompts tailored to specific business needs, enhancing the precision of AI responses.
* Prompt Organization:Prompt management tools facilitate the organization, versioning, and storage of prompts, ensuring efficient retrieval and reuse in various projects.

NEW QUESTION # 29
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. Avoid subscription-based pricing models
C. Weigh the cost of using advanced models against the expected return on investment
D. Use pricing models that have fixed rates irrespective of the usage patterns

Answer: A,C

NEW QUESTION # 30
......

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