SAP C_AIG_2412 Exam Dumps

SAP Certified Associate - SAP Generative AI Developer Exam

833 Reviews

Exam Code	C_AIG_2412
Exam Name	SAP Certified Associate - SAP Generative AI Developer Exam
Questions	60 Questions Answers With Explanation
Update Date	February 13,2026
Price	Was : $81 Today : $45 Was : $99 Today : $55 Was : $117 Today : $65

Add To Cart

Demo Questions

Question # 1

Which of the following must you do before connecting to a dataset in order to train a machine learning model in SAP Al Core? Note: There are 2 correct answers to this question. 

A. Store the dataset in a hyperscaler object store. 
B. Grant access rights to the SAP BTP cockpit. 
C. Provide the storage secret to access the dataset. 
D. Store the dataset in the SAP HANA Vector Engine. 

Show Answer

Question # 2

What are some functionalities provided by SAP Al Core? Note: There are 3 correct answers to this question. 

A. Integration of Al services with business applications using a standardized API 
B. Continuous delivery and tenant isolation for scalability 
C. Orchestration of Al workflows such as model training and inference 
D. Management of SAP SHANA cloud infrastructure 
E. Monitoring and retraining models in SAP Al Core 

Show Answer

Question # 3

What does SAP recommend you do before you start training a machine learning model in SAP AI Core? Note: There are 3 correct answers to this question. 

A. Configure the training pipeline using templates. 
B. Define the required infrastructure resources for training. 
C. Perform manual data integration with SAP HANA. 
D. Configure the model deployment in SAP Al Launchpad. 
E. Register the input dataset in SAP AI Core. 

Show Answer

Question # 4

How do resource groups in SAP AI Core improve the management of machine learning workloads? Note: There are 2 correct answers to this question.

A. They ensure workload separation for different tenants or departments. 
B. They enhance pipeline execution speeds through workload distribution. 
C. They enable simultaneous orchestration of Kubernetes clusters. 
D. They provide isolation for datasets and Al artifacts. 

Show Answer

Question # 5

What are some benefits of the SAP AI Launchpad? Note: There are 2 correct answers to this question. 

A. Direct deployment of Al models to SAP HANA. 
B. Integration with non-SAP platforms like Azure and AWS. 
C. Centralized Al lifecycle management for all Al scenarios. 
D. Simplified model retraining and performance improvement. 

Show Answer

Question # 6

What must be defined in an executable to train a machine learning model using SAP AI Core? Note: There are 2 correct answers to this question. 

A. Pipeline containers to be used 
B. Infrastructure resources such as CPUs or GPUs 
C. User scripts to manually execute pipeline steps 
D. Deployment templates for SAP AI Launchpad 

Show Answer

Question # 7

How does the Al API support SAP AI scenarios? Note: There are 2 correct answers to this question. 

A. By integrating Al services into business applications 
B. By providing a unified framework for operating Al services 
C. By integrating Al models into third-party platforms like AWS 
D. By managing Kubernetes clusters automatically 

Show Answer

Question # 8

What are some components of the training pipeline in SAP AI Core? Note: There are 2 correct answers to this question. 

A. Input datasets stored in a hyperscaler object store 
B. Executables that define the training process 
C. The SAP HANA database for model storage D. Automated deployment to Kubernetes clusters  

Show Answer

Question # 9

What can be done once the training of a machine learning model has been completed in SAP AI Core? Note: There are 2 correct answers to this question. 

A. The model can be deployed in SAP HANA. 
B. The model's accuracy can be optimized directly in SAP HANA. 
C. The model can be deployed for inferencing. 
D. The model can be registered in the hyperscaler object store. 

Show Answer

Question # 10

Why would a user include formatting instructions within a prompt? 

A. To force the model to separate relevant and irrelevant output 
B. To ensure the model's response follows a desired structure or style 
C. To increase the faithfulness of the output 
D. To redirect the output to another software program 

Show Answer

Question # 11

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 

Show Answer

Question # 12

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. 

Show Answer

Question # 13

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 

Show Answer

Question # 14

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 

Show Answer

Question # 15

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 

Show Answer

Question # 16

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. 

Show Answer

Question # 17

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 

Show Answer

Question # 18

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 

Show Answer

Question # 19

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 

Show Answer

Question # 20

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. 

Show Answer

Question # 21

What are some metrics to evaluate the effectiveness of a Retrieval Augmented Generation system? Note: There are 2 correct answers to this question. 

A. Carbon footprint 
B. Faithfulness 
C. Speed 
D. Relevance 

Show Answer

Question # 22

Which of the following is a principle of effective prompt engineering? 

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

Show Answer

Question # 23

Which neural network architecture is primarily used by LLMs? 

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

Show Answer

Question # 24

Which statement best describes the Chain-of-Thought (COT) prompting technique? 

A. Linking multiple Al models in sequence, where each model's output becomes the input for the next model in the chain. 
B. Writing a series of connected prompts creating a chain of related information. 
C. Concatenating multiple related prompts to form a chain, guiding the model through sequential reasoning steps. 
D. Connecting related concepts by having the LLM generate chains of ideas. 

Show Answer

Question # 25

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 

Show Answer