SAP C-AIG-2412 LATEST EXAM LABS: SAP CERTIFIED ASSOCIATE - SAP GENERATIVE AI DEVELOPER - DUMPTORRENT BRING YOU THE BEST PRODUCTS

SAP C-AIG-2412 Latest Exam Labs: SAP Certified Associate - SAP Generative AI Developer - DumpTorrent Bring you The Best Products

SAP C-AIG-2412 Latest Exam Labs: SAP Certified Associate - SAP Generative AI Developer - DumpTorrent Bring you The Best Products

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SAP C-AIG-2412 Exam Syllabus Topics:

TopicDetails
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
  • 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 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.
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 (Q26-Q31):

NEW QUESTION # 26
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 encode queries and documents into vector representations for comparison
  • C. To generate responses based on retrieved documents and user queries
  • D. To evaluate the faithfulness and relevance of generated Answers

Answer: B

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 # 27
Which of the following are features of the SAP AI Foundation?
Note: There are 2 correct answers to this question.

  • A. Joule integration in SAP SuccessFactors
  • B. Al runtimes and lifecycle management
  • C. Ready-to-use Al services
  • D. Open source Al model repository

Answer: B,C


NEW QUESTION # 28
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. Infrastructure resources such as CPUs or GPUs
  • B. User scripts to manually execute pipeline steps
  • C. Deployment templates for SAP AI Launchpad
  • D. Pipeline containers to be used

Answer: A,D


NEW QUESTION # 29
Which technique is used to supply domain-specific knowledge to an LLM?

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

Answer: C

Explanation:
Retrieval-Augmented Generation (RAG) is a technique that enhances Large Language Models (LLMs) by integrating external domain-specific knowledge, enabling more accurate and contextually relevant outputs.
1. Understanding Retrieval-Augmented Generation (RAG):
* Definition:RAG combines the generative capabilities of LLMs with retrieval mechanisms that access external knowledge bases or documents. This integration allows the model to incorporate up-to-date and domain-specific information into its responses.
* Mechanism:When presented with a query, the RAG system retrieves pertinent information from external sources and uses this data to inform and generate a more accurate and contextually appropriate response.
2. Application in Supplying Domain-Specific Knowledge:
* Domain Adaptation:By leveraging RAG, LLMs can access specialized information without the need for extensive retraining or fine-tuning. This approach is particularly beneficial for domains with rapidly evolving information or where incorporating proprietary data is essential.
* Efficiency:RAG enables models to provide informed responses by referencing external data, reducing the necessity for large-scale domain-specific training datasets and thereby conserving computational resources.
3. Advantages of Using RAG:
* Up-to-Date Information:Since RAG systems can query current data sources, they are capable of providing the most recent information available, which is crucial in dynamic fields.
* Enhanced Accuracy:Incorporating external knowledge allows the model to produce more precise and contextually relevant outputs, especially in specialized domains.


NEW QUESTION # 30
Which of the following describes Large Language Models (LLMs)?

  • A. They rely on traditional rule-based algorithms to generate responses
  • B. They generate responses based on pre-defined templates without learning from data
  • C. They utilize deep learning to process and generate human-like text
  • D. They can only process numerical data and are not capable of understanding text

Answer: C

Explanation:
Large Language Models (LLMs) are advanced AI systems that leverage deep learning techniques, specifically transformer architectures with self-attention mechanisms, to process and generate human-like text. Option A is incorrect because LLMs do not rely on traditional rule-based systems; they learn patterns from vast datasets. Option C is false as LLMs are designed for text processing, not limited to numerical data. Option D is also inaccurate since LLMs generate responses based on learned patterns, not static templates. Option B is correct, reflecting how LLMs, like those accessible via SAP's Generative AI Hub, use deep learning to understand context, semantics, and generate coherent text for applications such as chatbots, translations, and content creation.


NEW QUESTION # 31
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