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Title: Quiz 2026 SAP C_AIG_2412: Updated SAP Certified Associate - SAP Generative AI De [Print This Page]
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Title: Quiz 2026 SAP C_AIG_2412: Updated SAP Certified Associate - SAP Generative AI De
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SAP C_AIG_2412 Exam Syllabus Topics:
TopicDetails
Topic 1
  • Advanced AI Techniques with SAP¡¯s Generative AI Hub: This section of the exam measures the skills of Solution Architects and covers advanced techniques available through SAP¡¯s Generative AI Hub. Candidates are assessed on their ability to design, optimize, and scale generative AI solutions that go beyond basic implementations. The focus includes applying sophisticated strategies to integrate advanced models, manage performance, and align AI-driven outcomes with complex enterprise goals.
Topic 2
  • SAP AI Core: This section of the exam measures the skills of AI Developers and covers the fundamental components of SAP AI Core. Candidates are assessed on their ability to work with the core services that allow machine learning models to be deployed and managed within SAP environments. The focus is on understanding how AI Core fits into SAP¡¯s ecosystem and ensures smooth integration with enterprise applications.
Topic 3
  • Large Language Models (LLMs): This section of the exam measures the skills of AI Developers and covers the practical use of large language models in SAP environments. Candidates are expected to understand how LLMs can be applied to automate tasks, enhance decision-making, and improve user interaction within SAP systems. The exam evaluates knowledge of handling model selection, fine-tuning, and adapting LLMs to specific business cases.
Topic 4
  • SAP's Generative AI Hub: This section of the exam measures the skills of Solution Architects and covers SAP¡¯s Generative AI Hub, which acts as the central layer for designing and managing generative AI solutions. The exam tests knowledge of building, deploying, and connecting AI models to business scenarios through the Hub. Emphasis is placed on leveraging the Hub to streamline workflows and ensure scalable solutions that align with organizational needs.

SAP Certified Associate - SAP Generative AI Developer Sample Questions (Q55-Q60):NEW QUESTION # 55
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
'---'.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"])
Answer: A

NEW QUESTION # 56
Which of the following sequence of steps does SAP recommend you use to solve a business problem using generative Al hub?
Answer: B

NEW QUESTION # 57
Match the components of a Retrieval Augmented Generation architecture to the diagram.

Answer:
Explanation:


NEW QUESTION # 58
Which statement best describes the Chain-of-Thought (COT) prompting technique?
Answer: D
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 # 59
What is Machine Learning (ML)?
Answer: A,B,D

NEW QUESTION # 60
......
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