HOW TO PREPARE FOR C-AIG-2412 CERTIFICATION EXAM?

How to Prepare for C-AIG-2412 Certification Exam?

How to Prepare for C-AIG-2412 Certification Exam?

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SAP Certified Associate - SAP Generative AI Developer Sample Questions (Q42-Q47):

NEW QUESTION # 42
Which of the following steps is NOT a requirement to use the Orchestration service?

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

Answer: C


NEW QUESTION # 43
Which of the following are functionalities provided by the generative-Al-hub-SDK ? Note: There are 2 correct answers to this question.

  • A. Interact with LLMs
  • B. Configure SAP BTP credentials
  • C. Create chat responses and embeddings
  • D. Customize SAP AI Launchpad

Answer: A,C

Explanation:
The Generative AI Hub SDK offers functionalities that empower developers to:
1. Interact with Large Language Models (LLMs):
* Model Access:The SDK provides a developer-friendly way to consume foundational models available in the SAP Generative AI Hub, facilitating seamless interactions with these models.
2. Create Chat Responses and Embeddings:
* Natural Language Processing:With this SDK, developers can interact with models to create natural language completions, chat responses, and embeddings, enabling the development of sophisticated AI- driven applications.
Conclusion:
The Generative AI Hub SDK enables developers to interact with LLMs and create chat responses and embeddings, supporting the development of advanced AI functionalities within applications.


NEW QUESTION # 44
Which statement best describes the Chain-of-Thought (COT) prompting technique?

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

Answer: A

Explanation:
Chain-of-Thought (CoT) prompting is a technique that involves concatenating multiple related prompts to guide a language model through a series of reasoning steps, leading to a final conclusion.
1. Structure of CoT Prompting:
* Sequential Reasoning:By breaking down a complex problem into a sequence of intermediate prompts, the model addresses each step methodically, enhancing its problem-solving capabilities.
* Logical Progression:Each prompt builds upon the previous one, ensuring a coherent flow of information that mirrors human logical reasoning.
2. Advantages of CoT Prompting:
* Enhanced Comprehension:This structured approach helps the model understand and process intricate tasks by focusing on one aspect at a time.
* Improved Accuracy:By guiding the model through detailed reasoning steps, CoT prompting reduces the likelihood of errors in the final output.


NEW QUESTION # 45
Which of the following steps must be performed to deploy LLMs in the generative Al hub?

  • A. Provision SAP AI
    *Core Create a configuration
    *Run the booster
  • B. Check for foundation model scenario
    *Create a deployment
    *Configuring entitlements
  • C. Run the booster
    *Create service keys
    *Select the executable ID
  • D. Provision SAP AI Core
    *Check for foundation model scenario
    *Create a configuration
    *Create a deployment

Answer: D

Explanation:
Deploying Large Language Models (LLMs) in SAP's Generative AI Hub involves a structured process:
1. Provision SAP AI Core:
* Setup:Ensure that SAP AI Core is provisioned in your SAP Business Technology Platform (BTP) account to manage AI workloads.
2. Check for Foundation Model Scenario:
* Validation:Verify the availability of the foundation model scenario within SAP AI Core to confirm that the necessary resources and configurations are in place for deploying LLMs.
3. Create a Configuration:
* Configuration Setup:Define the parameters and settings required for the LLM deployment, including model specifications and resource allocations.
4. Create a Deployment:
* Deployment Execution:Initiate the deployment process within SAP AI Core, making the LLM available for integration and use within your applications.


NEW QUESTION # 46
What is the primary function of the embedding model in a RAG system?

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

Answer: C

Explanation:
In a Retrieval-Augmented Generation (RAG) system, the embedding model plays a crucial role in encoding textual data into vector representations, facilitating efficient retrieval and comparison.
1. Function of the Embedding Model:
* Vector Encoding:The embedding model transforms both user queries and documents into high- dimensional vector representations. This numerical encoding captures the semantic meaning of the text, enabling the system to assess similarities between different pieces of text effectively.
* Facilitating Retrieval:By encoding text into vectors, the system can perform efficient similarity searches within a vector database, identifying documents or passages that are most relevant to the user's query.
2. Importance in RAG Systems:
* Semantic Matching:The vector representations allow the system to match user queries with relevant documents based on semantic content rather than mere keyword overlap, enhancing the relevance of retrieved information.
* Efficiency:Vector-based retrieval is computationally efficient, enabling rapid identificationof pertinent information from large datasets, which is essential for real-time applications.
3. Application in SAP's Generative AI Hub:
* Integration with HANA Vector Search:SAP's Generative AI Hub integrates embedding models with HANA's vector search capabilities, allowing for efficient storage and retrieval of vector embeddings.
This integration supports the development of RAG systems that can effectively utilize SAP's data assets.
* Generative AI Hub SDK:SAP provides an SDK that facilitates the implementation of embedding models within RAG systems, enabling developers to encode queries and documents into vector representations seamlessly.


NEW QUESTION # 47
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