FREE PDF SAP - C-AIG-2412–TRUSTABLE CERTIFICATION DUMPS

Free PDF SAP - C-AIG-2412–Trustable Certification Dumps

Free PDF SAP - C-AIG-2412–Trustable Certification Dumps

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Tags: Certification C-AIG-2412 Dumps, Question C-AIG-2412 Explanations, C-AIG-2412 Valid Exam Cram, Valid C-AIG-2412 Test Pass4sure, Exam C-AIG-2412 Format

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SAP C-AIG-2412 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 2
  • 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 3
  • 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.
Topic 4
  • 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.

>> Certification C-AIG-2412 Dumps <<

Free PDF Quiz 2025 C-AIG-2412 - Certification SAP Certified Associate - SAP Generative AI Developer Dumps

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SAP Certified Associate - SAP Generative AI Developer Sample Questions (Q22-Q27):

NEW QUESTION # 22
What are some components of the training pipeline in SAP AI Core? Note: There are 2 correct answers to this question.

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

Answer: A,B


NEW QUESTION # 23
Which neural network architecture is primarily used by LLMs?

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

Answer: B

Explanation:
Large Language Models (LLMs) primarily utilize the Transformer architecture, which incorporates self- attention mechanisms.
1. Transformer Architecture:
* Overview:Introduced in 2017, the Transformer architecture revolutionized natural language processing by enabling models to handle long-range dependencies in text more effectively than previous architectures.
GeeksforGeeks
* Components:The Transformer consists of an encoder-decoder structure, where the encoder processes input sequences, and the decoder generates output sequences.
2. Self-Attention Mechanisms:
* Functionality:Self-attention allows the model to weigh the importance of different words in a sequence relative to each other, enabling it to capture contextual relationships regardless of their position.
* Benefits:This mechanism facilitates parallel processing of input data, improving computational efficiency and performance in understanding complex language patterns.
3. Application in LLMs:
* Model Examples:LLMs such as GPT-3 and BERT are built upon the Transformer architecture, leveraging self-attention to process and generate human-like text.
* Advantages:The Transformer architecture's ability to manage extensive context and dependencies makes it well-suited for tasks like language translation, summarization, and question-answering.


NEW QUESTION # 24
What are some metrics to evaluate the effectiveness of a Retrieval Augmented Generation system? Note:
There are 2 correct answers to this question.

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

Answer: A,B

Explanation:
Evaluating the effectiveness of a Retrieval-Augmented Generation (RAG) system involves assessing specific metrics that determine the quality and reliability of the generated content.
1. Faithfulness:
* Definition:Faithfulness measures the degree to which the generated output accurately reflects the information retrieved from source documents without introducing unsupported content.
* Importance:High faithfulness ensures that the system's responses are trustworthy and based on factual data, which is crucial for applications requiring precise information dissemination.
2. Relevance:
* Definition:Relevance assesses how pertinent the generated content is to the user's query or the task at hand.
* Importance:Ensuring relevance guarantees that the system provides information that directly addresses user needs, enhancing user satisfaction and system utility.
3. Application in RAG Systems:
* Performance Evaluation:By measuring faithfulness and relevance, developers can fine-tune RAG systems to produce outputs that are both accurate and pertinent, thereby improving overall system performance.
* User Trust:Maintaining high levels of these metrics fosters user trust, as the system consistently delivers reliable and contextually appropriate information.


NEW QUESTION # 25
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. Monitoring and retraining models in SAP Al Core
  • C. Continuous delivery and tenant isolation for scalability
  • D. Management of SAP S/4HANA cloud infrastructure
  • E. Orchestration of Al workflows such as model training and inference

Answer: A,C,E


NEW QUESTION # 26
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 provide isolation for datasets and Al artifacts.
  • B. They ensure workload separation for different tenants or departments.
  • C. They enhance pipeline execution speeds through workload distribution.
  • D. They enable simultaneous orchestration of Kubernetes clusters.

Answer: A,B


NEW QUESTION # 27
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