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SAP C-AIG-2412 Exam Syllabus Topics:

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 that needs to be evaluated.
Topic 2	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.
Topic 3	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.
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.

SAP Certified Associate - SAP Generative AI Developer Sample Questions (Q20-Q25):

NEW QUESTION # 20
What is the purpose of splitting documents into smaller overlapping chunks in a RAG system?

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

Answer: C

Explanation:
In Retrieval-Augmented Generation (RAG) systems, splitting documents into smaller overlapping chunks is a crucial preprocessing step that enhances the system's ability to match relevant passages to user queries.
1. Purpose of Splitting Documents into Smaller Overlapping Chunks:
* Improved Retrieval Accuracy:Dividing documents into smaller, manageable segments allows the system to retrieve the most relevant chunks in response to a user query, thereby improving the precision of the information provided.
* Context Preservation:Overlapping chunks ensure that contextual information is maintained across segments, which is essential for understanding the meaning and relevance of each chunk in relation to the query.
2. Benefits of This Approach:
* Enhanced Matching:By having multiple overlapping chunks, the system increases the likelihood that at least one chunk will closely match the user's query, leading to more accurate and relevant responses.
* Efficient Processing:Smaller chunks are easier to process and analyze, enabling the system to handle large documents more effectively and respond to queries promptly.

NEW QUESTION # 21
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. Perform manual data integration with SAP HANA.
B. Define the required infrastructure resources for training.
C. Register the input dataset in SAP AI Core.
D. Configure the training pipeline using templates.
E. Configure the model deployment in SAP Al Launchpad.

Answer: B,C,D

Explanation:
Before initiating the training of a machine learning model in SAP AI Core, SAP recommends the following steps:
* Configure the training pipeline using templates:Utilize predefined templates to set up the training pipeline, ensuring consistency and efficiency in the training process.
* Define the required infrastructure resources for training:Specify the computational resources, such as CPUs or GPUs, necessary for the training job to ensure optimal performance.
* Register the input dataset in SAP AI Core:Ensure that the dataset intended for training is properly registered within SAP AI Core, facilitating seamless access during the training process.
These preparatory steps are crucial for the successful training of machine learning models within the SAP AI Core environment.

NEW QUESTION # 22
How can few-shot learning enhance LLM performance?

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

Answer: A

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 # 23
Which of the following steps is NOT a requirement to use the Orchestration service?

A. Create an instance of an Al model
B. Create a deployment for orchestration
C. Modify the underlying Al models
D. Get an auth token for orchestration

Answer: C

Explanation:
To utilize the Orchestration service in SAP's Generative AI Hub, several steps are required; however, modifying the underlying AI models is not among them:
1. Required Steps:
* Get an Auth Token for Orchestration:Obtain authentication credentials to access the orchestration service.
* Create an Instance of an AI Model:Set up an instance of the desired AI model to be used within the orchestration pipeline.
* Create a Deployment for Orchestration:Deploy the configured AI model instance to the orchestration service, enabling it for processing requests.
2. Not Required:
* Modify the Underlying AI Models:The orchestration service allows users to utilize pre-existing AI models without the need to alter their foundational architecture or training.

NEW QUESTION # 24
What is the primary function of the embedding model in a RAG system?

A. To encode queries and documents into vector representations for comparison
B. To generate responses based on retrieved documents and user queries
C. To evaluate the faithfulness and relevance of generated answers
D. To store vector representations of documents and search for relevant passages

Answer: A

NEW QUESTION # 25
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Test C-AIG-2412 Cram | Exam C-AIG-2412 Materials

Exam SAP C-AIG-2412 Materials - C-AIG-2412 Latest Practice Questions

SAP C-AIG-2412 Exam Syllabus Topics:

SAP Certified Associate - SAP Generative AI Developer Sample Questions (Q20-Q25):

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