Reliable C_AIG_2412 Test Questions High-quality Questions Pool Only at Prep4pass
Reliable C_AIG_2412 Test Questions High-quality Questions Pool Only at Prep4pass
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2025 First-grade SAP C_AIG_2412: Reliable SAP Certified Associate - SAP Generative AI Developer Test Questions
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SAP Certified Associate - SAP Generative AI Developer Sample Questions (Q16-Q21):
NEW QUESTION # 16
Which neural network architecture is primarily used by LLMs?
- A. Transformer architecture with self-attention mechanisms
- B. Convolutional Neural Networks (CNNs)
- C. Sequential encoder-decoder architecture
- D. Recurrent neural network architecture
Answer: A
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 # 17
You want to download a json output for a prompt and the response.
Which of the following interfaces can you use in SAP's generative Al hub in SAP AI Launchpad?
- A. Administration
- B. Prompt Editor
- C. Prompt management
- D. Chat
Answer: C
NEW QUESTION # 18
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 # 19
What are the benefits of SAP's generative Al hub?
Note: There are 2 correct answers to this question.
- A. Send your data to various LLM providers for training feedback
- B. Build custom Al solutions and extend SAP applications
- C. Accelerate Al development with flexible access to a broad range of models
- D. Provide libraries for no-code development
Answer: B,C
NEW QUESTION # 20
How can few-shot learning enhance LLM performance?
- A. By reducing overfitting through regularization techniques
- B. By offering input-output pairs that exemplify the desired behavior
- C. By providing a large training set to improve generalization
- D. By enhancing the model's computational efficiency
Answer: B
Explanation:
Few-shot learning enhances the performance of Large Language Models (LLMs) by providing them with a limited number of input-output examples that demonstrate the desired task behavior.
1. Mechanism of Few-Shot Learning:
* Exemplification:By supplying a few examples, the model gains insight into the task requirements, enabling it to generalize from these instances to handle new, unseen inputs effectively.
* Adaptability:This approach allows LLMs to adapt to specific tasks without extensive retraining, making them versatile across various applications.
2. Benefits in Performance Enhancement:
* Improved Accuracy:With clear examples, the model's predictions align more closely with the desired outcomes, reducing errors.
* Efficiency:Few-shot learning minimizes the need for large datasets, accelerating the development process and conserving computational resources.
NEW QUESTION # 21
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