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100% Pass 2025 Accurate C_AIG_2412: Reliable SAP Certified Associate - SAP Generative AI Developer Test Experience
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| 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 evaluated.
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| Topic 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.
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| Topic 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.
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| 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.
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SAP Certified Associate - SAP Generative AI Developer Sample Questions (Q32-Q37):
NEW QUESTION # 32
Which of the following steps is NOT a requirement to use the Orchestration service?
- A. Create an instance of an Al model
- B. Get an auth token for orchestration
- C. Modify the underlying Al models
- D. Create a deployment for orchestration
Answer: C
NEW QUESTION # 33
What is the purpose of splitting documents into smaller overlapping chunks in a RAG system?
- A. To reduce the storage space required for the vector database
- B. To simplify the process of training the embedding model
- C. To improve the efficiency of encoding queries into vector representations
- D. To enable the matching of different relevant passages to user queries
Answer: D
NEW QUESTION # 34
Which of the following statements accurately describe the RAG process? Note: There are 2 correct ans-wers to this question.
- A. The user's questi on is used to search a knowledge base or a set of documents.
- B. The embedding model stores the generated ans wers for future reference.
- C. The retrieved content is combined with the LLM's capabilities to generate a response.
- D. The LLM directly ans wers the user's question without accessing external information.
Answer: A,C
NEW QUESTION # 35
What are some examples of generative Al technologies?
Note: There are 2 correct answers to this question.
- A. Rule-based algorithms
- B. Al models that generate new content based on training data
- C. Foundation models
- D. Robotic process automation
Answer: B,C
NEW QUESTION # 36
How can few-shot learning enhance LLM performance?
- A. By enhancing the model's computational efficiency
- B. By providing a large training set to improve generalization
- C. By reducing overfitting through regularization techniques
- D. By offering input-output pairs that exemplify the desired behavior
Answer: D
NEW QUESTION # 37
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