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SAP C-AIG-2412 Prüfungsplan:

Thema	Einzelheiten
Thema 1	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.
Thema 2	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.
Thema 3	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.
Thema 4	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.

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SAP Certified Associate - SAP Generative AI Developer C-AIG-2412 Prüfungsfragen mit Lösungen (Q46-Q51):

46. Frage
Which neural network architecture is primarily used by LLMs?

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

Antwort: A

Begründung:
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.

47. Frage
What is the primary function of the generative Al hub in SAP's Al Foundation?

A. To store embeddings of unstructured data for semantic data retrieval
B. To provide ready-to-use Al services for document processing
C. To manage the Al lifecycle efforts end-to-end
D. To serve as an abstraction layer to access a range of foundation Al models

Antwort: D

48. Frage
Which of the following techniques uses a prompt to generate or complete subsequent prompts (streamlining the prompt development process), and to effectively guide Al model responses?

A. Chain-of-thought prompting
B. Meta prompting
C. One-shot prompting
D. Few-shot prompting

Antwort: B

49. Frage
How can few-shot learning enhance LLM performance?

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

Antwort: B

Begründung:
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.

50. Frage
Which of the following is a principle of effective prompt engineering?

A. Write vague and open-ended instructions to encourage creativity.
B. Combine multiple complex tasks into a single prompt.
C. Use precise language and providing detailed context in prompts.
D. Keep prompts as short as possible to avoid confusion.

Antwort: C

Begründung:
Effective prompt engineering is crucial for guiding AI models to produce accurate and relevant outputs.
1. Importance of Precision and Context:
* Clarity:Using precise language in prompts minimizes ambiguity, ensuring the AI model comprehends the exact requirements.
* Detailed Context:Providing comprehensive context helps the model understand the background and nuances of the task, leading to more accurate and tailored responses.
2. Best Practices in Prompt Engineering:
* Specificity:Clearly define the desired outcome, including any constraints or specific formats required.
* Instruction Inclusion:Incorporate explicit instructions within the prompt to guide the model's behavior effectively.
* Avoiding Ambiguity:Steer clear of vague or open-ended language that could lead to varied interpretations.
3. Benefits of Effective Prompt Engineering:
* Enhanced Output Quality:Well-crafted prompts lead to responses that closely align with user expectations.
* Efficiency:Reduces the need for iterative refinements, saving time and computational resources.

51. Frage
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Ian Ward Ian Ward

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Die neuesten C-AIG-2412 echte Prüfungsfragen, SAP C-AIG-2412 originale fragen

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SAP Certified Associate - SAP Generative AI Developer C-AIG-2412 Prüfungsfragen mit Lösungen (Q46-Q51):

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