CERTIFICATION C-AIG-2412 TRAINING EXAM PASS AT YOUR FIRST ATTEMPT | C-AIG-2412 ACTUAL TEST ANSWERS

Certification C-AIG-2412 Training Exam Pass at Your First Attempt | C-AIG-2412 Actual Test Answers

Certification C-AIG-2412 Training Exam Pass at Your First Attempt | C-AIG-2412 Actual Test Answers

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Tags: Certification C-AIG-2412 Training, C-AIG-2412 Actual Test Answers, New C-AIG-2412 Exam Papers, C-AIG-2412 New Test Camp, Free C-AIG-2412 Download Pdf

The SAP sector is an ever-evolving and rapidly growing industry that is crucial in shaping our lives today. With the growing demand for skilled SAP professionals, obtaining SAP Certified Associate - SAP Generative AI Developer (C-AIG-2412) certification exam has become increasingly important for those who are looking to advance their careers and stay competitive in the job market. Individuals who hold SAP Certified Associate - SAP Generative AI Developer (C-AIG-2412) certification exam demonstrate to their employers and clients that they have the knowledge and skills necessary to succeed in the C-AIG-2412 exam.

SAP C-AIG-2412 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 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 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 evaluated.

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C-AIG-2412 Actual Test Answers & New C-AIG-2412 Exam Papers

You can save time and clear the C-AIG-2412 certification test in one sitting if you skip unnecessary material and focus on our SAP C-AIG-2412 actual questions. It's time to expand your knowledge and skills if you're committed to pass the SAP C-AIG-2412 Exam and get the certification badge to advance your profession.

SAP Certified Associate - SAP Generative AI Developer Sample Questions (Q57-Q62):

NEW QUESTION # 57
You want to assign urgency and sentiment categories to a large number of customer emails. You want to get a valid json string output for creating custom applications. You decide to develop a prompt for the same using generative Al hub.
What is the main purpose of the following code in this context?
prompt_test = """Your task is to extract and categorize messages. Here are some examples:
{{?technique_examples}}
Use the examples when extract and categorize the following message:
{{?input}}
Extract and return a json with the following keys and values:
-"urgency" as one of {{?urgency}}
-"sentiment" as one of {{?sentiment}}
"categories" list of the best matching support category tags from: {{?categories}} Your complete message should be a valid json string that can be read directly and only contains the keys mentioned in t import random random.seed(42) k = 3 examples random. sample (dev_set, k) example_template = """<example> {example_input} examples
'n---n'.join([example_template.format(example_input=example ["message"], example_output=json.dumps (example[ f_test = partial (send_request, prompt=prompt_test, technique_examples examples, **option_lists) response = f_test(input=mail["message"])

  • A. Preprocess a dataset for machine learning
  • B. Evaluate the performance of a language model using few-shot learning
  • C. Train a language model from scratch
  • D. Generate random examples for language model training

Answer: B

Explanation:
The provided code is designed to evaluate the performance of a language model in assigning urgency and sentiment categories to customer emails by utilizing few-shot learning within SAP's Generative AI Hub.
1. Few-Shot Learning in Prompt Engineering:
* Definition:Few-shot learning involves providing a language model with a limited number of examples to enable it to perform a specific task effectively. In this context, the model isgiven a few examples of categorized messages to learn how to assign urgency and sentiment to new, unseen emails.
2. Code Functionality:
* Prompt Template Creation:The prompt_test variable defines a template that instructs the model to extract and categorize messages, specifying the desired output format as a JSON string.
* Example Selection:The code randomly selects a subset of examples from a development set (dev_set) to include in the prompt, demonstrating the expected input-output pairs to the model.
* Model Interaction:The function f_test sends the constructed prompt, along with the input message, to the language model for processing.
* Response Handling:The model's response is expected to be a JSON string containing the assigned urgency, sentiment, and categories for the input message.
3. Purpose of the Code:
* Performance Evaluation:By using few-shot learning, the code evaluates how well the language model can generalize from the provided examples to accurately categorize new customer emails. This approach assesses the model's ability to understand and apply the categorization criteria based on minimal training data.


NEW QUESTION # 58
What defines SAP's approach to LLMs?

  • A. Focusing solely on reducing the computational cost of training LLMs
  • B. Ensuring ethical AI practices and seamless business integration
  • C. Limiting LLM usage to non-business applications only
  • D. Prioritizing the development of proprietary LLMs with no integration to existing systems

Answer: B

Explanation:
SAP's approach to Large Language Models (LLMs) is centered on integrating these powerful AI tools into its enterprise ecosystem while adhering to ethical standards. Unlike option A, SAP does not focus solely on proprietary LLMs without integration; instead, it leverages both proprietary and third-party models (e.g., via partnerships with providers like Azure OpenAI) to enhance business applications. Option B is incorrect because reducing computational cost is not the sole focus-SAP prioritizes value delivery through integration with business processes. Option D is also inaccurate, as SAP explicitly targets business applications rather than limiting LLMs to non-business use. Option C is correct because SAP emphasizes ethical AI practices (e.
g., through its AI Ethics Policy) and seamless integration with tools like SAP S/4HANA and SAP SuccessFactors, ensuring LLMs enhance enterprise workflows responsibly and effectively.


NEW QUESTION # 59
Which of the following is a benefit of using Retrieval Augmented Generation?

  • A. It enables LLMs to learn new languages without additional training.
  • B. It reduces the computational resources required for language modeling.
  • C. It allows LLMs to access and utilize information beyond their initial training data.
  • D. It eliminates the need for fine-tuning LLMs for specific tasks.

Answer: C

Explanation:
Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by enabling them to access and utilize information beyond their initial training data.
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. Benefits of RAG:
* Access to External Information:RAG allows LLMs to access and utilize information beyond their initial training data, enabling them to provide more accurate and relevant responses.
* 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.
* Improved Accuracy and Relevance:By leveraging external data, RAG enhances theaccuracy and relevance of the generated content, making it particularly useful for tasks requiring detailed or domain- specific information.


NEW QUESTION # 60
What is the primary function of the generative Al hub in SAP's Al Foundation?

  • A. To provide ready-to-use Al services for document processing
  • B. To manage the Al lifecycle efforts end-to-end
  • C. To store embeddings of unstructured data for semantic data retrieval
  • D. To serve as an abstraction layer to access a range of foundation Al models

Answer: D

Explanation:
SAP's Generative AI Hub, a component of the SAP AI Foundation, provides a unified platform for accessing various foundation AI models.
1. Abstraction Layer Functionality:
* Unified Access:The Generative AI Hub acts as an abstraction layer, offering developers seamless access to a diverse range of large language models (LLMs) from multiple providers, such as GPT-4 by Azure OpenAI and open-source models like Falcon-40b.
* Simplified Integration:By providing a standardized interface, the hub simplifies the integration of these models into applications, reducing complexity and development time.
2. Supporting AI Development:
* Toolset Availability:The hub offers a comprehensive toolset for building custom AI solutions, including prompt engineering tools, SDKs, and fine-tuning services, facilitating efficient AI development.
* Model Orchestration:Developers can orchestrate multiple models, enabling the selection of the most suitable model for specific use cases, thereby enhancing the accuracy and relevance of AI applications.


NEW QUESTION # 61
How can Joule improve workforce productivity? Note: There are 2 correct answers to this question.

  • A. By resolving hardware malfunctions.
  • B. By maintaining strict adherence to data privacy regulations.
  • C. By providing context-based role-specific task assistance.
  • D. By offering generic task recommendations unrelated to specific roles.

Answer: B,C

Explanation:
SAP's AI copilot, Joule, enhances workforce productivity through several key features:
1. Adherence to Data Privacy Regulations:
* Data Security and Privacy:Joule is designed with a strong emphasis on data security and privacy, ensuring compliance with data protection regulations. This adherence builds user trust and allows employees to utilize AI tools confidently, knowing that their data is handled responsibly.
2. Context-Based Role-Specific Task Assistance:
* Personalized Assistance:Joule provides context-aware support tailored to individual roles within an organization. By understanding the specific needs and responsibilities of each user, Joule offers relevant insights and automates routine tasks, thereby enhancing efficiency and allowing employees to focus on higher-value activities.


NEW QUESTION # 62
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