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SAP C_AIG_2412試験に合格するC_AIG_2412科目対策：SAP Certified Associate - SAP Generative AI Developerを効率的に学習する

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SAP C_AIG_2412 認定試験の出題範囲：

トピック
出題範囲

トピック 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.

トピック 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.

トピック 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.

トピック 4

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.

>> C_AIG_2412科目対策 <<

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SAP Certified Associate - SAP Generative AI Developer 認定 C_AIG_2412 試験問題 (Q41-Q46):

質問 # 41
What are some benefits of SAP Business Al? Note: There are 3 correct answers to this question.

A. Automatic human emotion recognition
B. Personalized recommendations based on Al algorithms
C. Al-powered forecasting and predictions
D. Intelligent business document processing
E. Face detection and face recognition

正解：B、C、D

質問 # 42
How can few-shot learning enhance LLM performance?

A. By enhancing the model's computational efficiency
B. By providing a large training set to improve generalization
C. By reducing overfitting through regularization techniques
D. By offering input-output pairs that exemplify the desired behavior

正解：D

解説：
Few-shot learning enhances the performance of Large Language Models (LLMs) by providing them with a limited number of input-output examples that demonstrate the desired task behavior.
1. Mechanism of Few-Shot Learning:
* Exemplification:By supplying a few examples, the model gains insight into the task requirements, enabling it to generalize from these instances to handle new, unseen inputs effectively.
* Adaptability:This approach allows LLMs to adapt to specific tasks without extensive retraining, making them versatile across various applications.
2. Benefits in Performance Enhancement:
* Improved Accuracy:With clear examples, the model's predictions align more closely with the desired outcomes, reducing errors.
* Efficiency:Few-shot learning minimizes the need for large datasets, accelerating the development process and conserving computational resources.

質問 # 43
How does SAP ensure the enterprise-readiness of its Al solutions?

A. By using generic Al models without business context complying with Al ethics standards
B. By ensuring that Al models make bias-free decisions without human input
C. By implementing rigorous product standards for Al capabilities

正解：C

解説：
SAP ensures the enterprise-readiness of its AI solutions through the implementation of rigorous product standards:
1. Rigorous Product Standards for AI Capabilities:
* Development Guidelines:SAP adheres to strict guidelines during the development of AI systems, ensuring they meet high standards of quality, security, and performance.
* Ethical Framework:SAP's AI Ethics Policy governs the development, deployment, use, and sale of AI systems, defining clear ethical rules aligned with global standards.
* Compliance and Governance:SAP has established governance bodies and processes to oversee AI ethics, ensuring that AI solutions are developed and deployed responsibly.

質問 # 44
Which of the following is unique about SAP's approach to Al?

A. Utilizing Al mainly for marketing purposes.
B. Offering Al capabilities in their future products as of 2025.
C. Focusing Al solely on customer support services.
D. SAP's deep integration of Al with business processes and analytics.

正解：D

解説：
SAP distinguishes itself by deeply embedding Artificial Intelligence (AI) into its core business processes and analytics, enhancing efficiency and decision-making across various enterprise functions.
1. Integration of AI into Business Processes:
* SAP Business AI:SAP focuses on solving customers' business problems by integrating AI directly into business processes, rather than offering general-purpose AI platforms. This approach ensures that AI solutions are tailored to specific business needs, enhancing process efficiency and effectiveness.
* SAP S/4HANA Integration:By embedding AI into SAP S/4HANA, SAP enables real-time data analysis and process optimization. This integration allows for improved supply chain efficiency, enhanced financial decision-making, and personalized customer experiences.
2. AI-Driven Analytics:
* SAP Analytics Cloud:This solution combines AI with analytics and planning, unlocking the full potential of business data. It provides advanced analytics capabilities, enabling businesses to make informed decisions based on real-time insights.
* Predictive Analytics Library:SAP HANA includes a Predictive Analytics Library with native algorithms for statistical measures, clustering, classification, and time series analysis. This facilitates advanced data processing and predictive analytics within business applications.
3. AI in Enterprise Applications:
* SAP SuccessFactors:AI is integrated into SAP SuccessFactors to enhance human resources processes, such as talent acquisition and employee engagement, by providing data-driven insights and automating routine tasks.
* SAP AI Business Services:These services offer reusable AI capabilities that can be integrated across various business processes, automating tasks like document processing andenriching customer experiences.

質問 # 45
What are some advantages of using agents in training models? Note: There are 2 correct answers to this question.

A. To guarantee accurate decision making in complex scenarios
B. To streamline LLM workflows
C. To improve the quality of results
D. To eliminate the need for human oversight

正解：B、C

解説：
Incorporating agents into the training and deployment of Large Language Models (LLMs) offers notable advantages:
1. Improving the Quality of Results:
* Specialized Task Handling:Agents can be designed to manage specific tasks or subtasks within a larger process, ensuring that each component is handled with expertise, thereby enhancing the overall quality of the output.
* Error Reduction:By delegating particular functions to specialized agents, the likelihood of errors decreases, leading to more accurate and reliable results.
2. Streamlining LLM Workflows:
* Process Automation:Agents can automate repetitive or time-consuming tasks within the LLM workflow, increasing efficiency and allowing human resources to focus on more complex aspects of model development and deployment.
* Workflow Management:Agents facilitate the coordination of various stages in the LLM pipeline, ensuring seamless transitions between tasks and improving overall workflow efficiency.
3. Enhancing Model Performance:
* Adaptive Learning:Agents can monitor model performance and implement adjustments in real-time, promoting continuous improvement and adaptability to new data or requirements.
* Resource Optimization:By managing specific tasks, agents help in optimizing computational resources, ensuring that the LLM operates efficiently without unnecessary expenditure of processing power.

質問 # 46
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C_AIG_2412日本語版試験解答: https://jp.fast2test.com/C_AIG_2412-premium-file.html