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SAP C_AIG_2412 Exam Questions

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SAP Certified Associate - SAP Generative AI Developer Exam

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SAP C_AIG_2412 Sample Questions – Free Practice Test & Real Exam Prep

Question #1

What is the primary function of the embedding model in a RAG system?  

  • A. To generate responses based on retrieved documents and user queries 
  • B. To encode queries and documents into vector representations for comparison 
  • C. To evaluate the faithfulness and relevance of generated Answers 
  • D. To store vector representations of documents and search for relevant passages 
Answer: B
Question #2

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
Question #3

What is the goal of prompt engineering? 

  • A. To replace human decision-making with automated processes 
  • B. To craft inputs that guide Al systems in generating desired outputs 
  • C. To optimize hardware performance for Al computations 
  • D. To develop new neural network architectures for Al models 
Answer: B
Question #4

What is a part of LLM context optimization? 

  • A. Reducing the model's size to improve efficiency 
  • B. Adjusting the model's output format and style 
  • C. Enhancing the computational speed of the model 
  • D. Providing the model with domain-specific knowledge needed to solve a problem 
Answer: D
Question #5

What is the purpose of splitting documents into smaller overlapping chunks in a RAG system? 

  • A. To simplify the process of training the embedding model 
  • B. To enable the matching of different relevant passages to user queries 
  • C. To improve the efficiency of encoding queries into vector representations 
  • D. To reduce the storage space required for the vector database 
Answer: B
Question #6

Which of the following is a benefit of using Retrieval Augmented Generation? 

  • A. It allows LLMs to access and utilize information beyond their initial training data. 
  • B. It enables LLMs to learn new languages without additional training. 
  • C. It eliminates the need for fine-tuning LLMs for specific tasks. 
  • D. It reduces the computational resources required for language modeling. 
Answer: A
Question #7

What are some advantages of using agents in training models? Note: There are 2 correct answers to this question. 

  • A. To guarantee accurate decision making in complex scenarios 
  • B. To improve the quality of results 
  • C. To streamline LLM workflows 
  • D. To eliminate the need for human oversight 
Answer: B,C
Question #8

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 sanitize model outputs 
  • D. To customize outputs for specific types of inputs 
Answer: A,D
Question #9

Which technique is used to supply domain-specific knowledge to an LLM?  

  • A. Domain-adaptation training 
  • B. Prompt template expansion 
  • C. Retrieval-Augmented Generation 
  • D. Fine-tuning the model on general data 
Answer: A
Question #10

What is a Large Language Model (LLM)? 

  • A. A rule-based expert system to analyze and generate grammatically correct sentences. 
  • B. An Al model that specializes in processing, understanding, and generating human language. 
  • C. A database system optimized for storing large volumes of textual data. 
  • D. A gradient boosted decision tree algorithm for predicting text. 
Answer: B
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