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[General] Free PDF 2026 SAP C-AIG-2412: SAP Certified Associate - SAP Generative AI Develo

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【General】 Free PDF 2026 SAP C-AIG-2412: SAP Certified Associate - SAP Generative AI Develo

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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 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 2
  • 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.
Topic 3
  • 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 4
  • 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.

SAP Certified Associate - SAP Generative AI Developer Sample Questions (Q40-Q45):NEW QUESTION # 40
What are some benefits of using an SDK for evaluating prompts within the context of generative Al?
Note: There are 3 correct answers to this question.
  • A. Supporting low code evaluations using graphical user interface
  • B. Providing metrics to quantitatively assess response quality
  • C. Maintaining data privacy by using data masking techniques
  • D. Creating custom evaluators that meet specific business needs
  • E. Automating prompt testing across various scenarios
Answer: B,D,E

NEW QUESTION # 41
What capabilities does the Exploration and Development feature of the generative Al hub provide?
Note: There are 2 correct answers to this question.
  • A. Develop and debug ABAP code
  • B. Prompt editor and management
  • C. Al playground and chat
  • D. Automatic model selection
Answer: B,C

NEW QUESTION # 42
How does SAP deal with vulnerability risks created by generative Al? Note: There are 2 correct answers to this question.
  • A. By focusing on technological advancement only.
  • B. By relying on external vendors to manage security threats.
  • C. By implementing responsible Al use guidelines and strong product security standards.
  • D. By identifying human, technical, and exfiltration risks through an Al Security Taskforce.
Answer: C,D
Explanation:
SAP addresses vulnerability risks associated with generative AI through a comprehensive strategy:
1. Implementation of Responsible AI Use Guidelines and Strong Product Security Standards:
* AI Ethics Policy:SAP has established an AI Ethics Policy that mandates responsible AI usage, ensuring that AI systems are designed and deployed ethically, with considerations for fairness, transparency, and accountability.
* Product Security Standards:SAP integrates robust security measures into its AI products, adhering to stringent security protocols to protect against vulnerabilities and potential threats.
2. Identification of Risks through an AI Security Taskforce:
* AI Security Taskforce:SAP has established an AI Security Taskforce dedicated to identifying and mitigating risks associated with generative AI, including human factors, technical vulnerabilities, and data exfiltration threats.

NEW QUESTION # 43
Where can you configure language models in generative Al hub?
  • A. The Models tab in Prompt Editor
  • B. The Orchestration tab in SAP AI Launchpad
  • C. The Configuration tab within ML Operations in SAP AI Launchpad
  • D. The Configuration tab of the SAP BTP cockpit
Answer: C
Explanation:
In SAP's Generative AI Hub, configuring language models is a crucial step for integrating AI capabilities into applications.
1. Accessing the Configuration Interface:
* ML Operations Section:Within SAP AI Launchpad, navigate to theML Operationssection to manage machine learning models and operations.
* Configuration Tab:In theConfigurationtab, you can set up and manage various aspects of language models, including selecting model providers, specifying model versions, and configuring deployment settings.
2. Steps to Configure Language Models:
* Model Selection:Choose the desired language model from the available options, such as GPT-4 or other large language models (LLMs) provided through the Generative AI Hub.
* Parameter Configuration:Set parameters like model version, input constraints, and performance settings to tailor the model's behavior to your application's requirements.
* Deployment Setup:Configure deployment details to ensure the model is accessible andintegrated appropriately within your application's infrastructure.

NEW QUESTION # 44
What are the applications of generative Al that go beyond traditional chatbot applications? Note: There are 2 correct answers to this question.
  • A. To interpret human instructions and control software systems without necessarily producing output for human consumption.
  • B. To interpret human instructions and control software systems always producing output for human consumption.
  • C. To follow a specific schema - human input, Al processing, and output for human consumption.
  • D. To produce outputs based on software input.
Answer: A,B
Explanation:
* C. To interpret human instructions and control software systems without necessarily producing output for human consumption.This is a key area where generative AI is breaking new ground. Think of it as AI acting as a "middleman" between you and software. Here are some examples:
* Automating complex tasks:You could tell the AI to "optimize this database for performance" or
"find and fix security vulnerabilities in this code." The AI would then interact with the software systems to carry out these instructions, without needing to show you every step or result.
* Controlling robots or IoT devices:Imagine instructing an AI to "adjust the lighting in the meeting room" or "have the robot retrieve the package from the warehouse." The AI translates your instructions into actions for those systems.
* Managing cloud resources:AI could dynamically allocate cloud resources based on your needs, scaling them up or down without your direct intervention.
* D. To interpret human instructions and control software systems always producing output for human consumption.This is more in line with traditional chatbot interactions, but with a broader scope. It's about AI generating outputs that are directly useful or informative for humans. Examples include:
* Creating realistic images or videos:Based on your description, the AI could generate a photorealistic image of a new product design or a short video clip for a marketing campaign.
* Writing different kinds of creative text formats:AI can generate stories, poems,articles, summaries, and even code, all tailored to your specifications.
* Providing personalized recommendations:AI can analyze your preferences and provide recommendations for products, services, or information.
Why the other options are incorrect:
* A. To produce outputs based on software input.This is a general capability of AI, not something specific to generative AI or beyond chatbots. Many AI systems analyze software input (like sensor data or log files) to produce outputs.
* B. To follow a specific schema - human input, AI processing, and output for human consumption.
This describes the basic interaction pattern of many AI systems, including chatbots. It's not something that specifically differentiates generative AI or goes beyond typical chatbot applications.

NEW QUESTION # 45
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