This page was exported from Testking Free Dumps [ http://blog.testkingfree.com ] Export date:Sun Mar 9 14:35:58 2025 / +0000 GMT ___________________________________________________ Title: C_AIG_2412 Practice Exams and Training Solutions for Certifications [Q30-Q44] --------------------------------------------------- C_AIG_2412 Practice Exams and Training Solutions for Certifications Dumps Free Test Engine Player Verified Answers Q30. What can be done once the training of a machine learning model has been completed in SAP AI Core? Note: There are 2 correct answers to this question.  The model can be deployed in SAP HANA.  The model’s accuracy can be optimized directly in SAP HANA.  The model can be deployed for inferencing.  The model can be registered in the hyperscaler object store. Q31. What are some components of the training pipeline in SAP AI Core?Note: There are 2 correct answers to this question.  Input datasets stored in a hyperscaler object store  Executables that define the training process  The SAP HANA database for model storage  Automated deployment to Kubernetes clusters Q32. What are some benefits of the SAP AI Launchpad? Note: There are 2 correct answers to this question.  Direct deployment of Al models to SAP HANA.  Integration with non-SAP platforms like Azure and AWS.  Centralized Al lifecycle management for all Al scenarios.  Simplified model retraining and performance improvement. Q33. How do resource groups in SAP AI Core improve the management of machine learning workloads? Note: There are 2 correct answers to this question.  They ensure workload separation for different tenants or departments.  They enhance pipeline execution speeds through workload distribution.  They enable simultaneous orchestration of Kubernetes clusters.  They provide isolation for datasets and Al artifacts. Q34. Which technique is used to supply domain-specific knowledge to an LLM?  Domain-adaptation training  Prompt template expansion  Retrieval-Augmented Generation  Fine-tuning the model on general data Q35. You want to download a json output for a prompt and the response.Which of the following interfaces can you use in SAP’s generative Al hub in SAP AI Launchpad?  Chat  Prompt management  Administration  Prompt Editor Q36. How can Joule improve workforce productivity?Note: There are 2 correct answers to this question.  By maintaining strict adherence to data privacy regulations.  By resolving hardware malfunctions.  By offering generic task recommendations unrelated to specific roles.  By providing context-based role-specific task assistance. Q37. What are some use cases for fine-tuning of a model? Note: There are 2 correct answers to this question.  To introduce new knowledge to a model in a resource-efficient way  To quickly create iterations on a new use case  To sanitize model outputs  To customize outputs for specific types of inputs Q38. How can few-shot learning enhance LLM performance?  By enhancing the model’s computational efficiency  By providing a large training set to improve generalization  By reducing overfitting through regularization techniques  By offering input-output pairs that exemplify the desired behavior Q39. 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”])  Generate random examples for language model training  Evaluate the performance of a language model using few-shot learning  Train a language model from scratch  Preprocess a dataset for machine learning Q40. Which statement best describes the Chain-of-Thought (COT) prompting technique?  Linking multiple Al models in sequence, where each model’s output becomes the input for the next model in the chain.  Writing a series of connected prompts creating a chain of related information.  Concatenating multiple related prompts to form a chain, guiding the model through sequential reasoning steps.  Connecting related concepts by having the LLM generate chains of ideas. Q41. Match the components of a Retrieval Augmented Generation architecture to the diagram. Q42. What are some examples of generative Al technologies?Note: There are 2 correct answers to this question.  Al models that generate new content based on training data  Rule-based algorithms  Robotic process automation  Foundation models Q43. What does the Prompt Management feature of the SAP AI launchpad allow users to do?  Create and edit prompts  Provide personalized user interactions  Interact with models through a conversational interface  Access and manage saved prompts and their versions Q44. What are some drivers for the rapid adoption of generative AI? Note: There are 2 correct answers to this question.  Availability of skilled developers  Significant hardware cost savings  Wide availability  Ease of use  Loading … SAP C_AIG_2412 Exam Syllabus Topics: TopicDetailsTopic 1SAP'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 2SAP 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 3Large 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 4SAP 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.   Q&As with Explanations Verified & Correct Answers: https://www.testkingfree.com/SAP/C_AIG_2412-practice-exam-dumps.html --------------------------------------------------- Images: https://blog.testkingfree.com/wp-content/plugins/watu/loading.gif https://blog.testkingfree.com/wp-content/plugins/watu/loading.gif --------------------------------------------------- --------------------------------------------------- Post date: 2025-03-02 10:27:19 Post date GMT: 2025-03-02 10:27:19 Post modified date: 2025-03-02 10:27:19 Post modified date GMT: 2025-03-02 10:27:19