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SAP C-BCSBS-2502 認定試験の出題範囲：

トピック	出題範囲
トピック 1	SAP Business Suiteモジュールの設定：このセクションでは、SAP機能コンサルタントのスキルを評価し、SAP Business Suiteのコアモジュールを設定するための基本的な手法を網羅します。受験者は、総勘定元帳、調達、受注管理、人事管理などの分野における基本的なシステム設定、マスターデータと組織構造のカスタマイズ、モジュール間のシームレスなデータフローを確保する主要な統合ポイントの検証方法を理解する必要があります。
トピック 2	SAP Business Suiteのポジショニング：この試験セクションでは、SAPソリューションアドバイザーのスキルを評価し、SAP Business Suite、その主要コンポーネント、そして様々な業種における様々なビジネスプロセスのサポート方法についての包括的な理解を問われます。受験者は、財務、物流、人事、分析におけるスイートの機能を明確に説明し、エンドツーエンドの運用における統合パターンを説明し、戦略目標とデジタルトランスフォーメーションロードマップに沿った導入オプションを位置付ける能力を実証する必要があります。
トピック 3	SAP Business Suite ランドスケープの管理と拡張：このセクションでは、SAP Basis 管理者のスキルを評価し、SAP Business Suite 環境の維持と拡張に必要な運用および技術活動を網羅します。受験者は、システムのプロビジョニングとパッチ適用戦略、トランスポート管理、パフォーマンス監視とチューニング、そして拡張パッケージ、アドオンインストール、SAP のクラウドベースの拡張サービスとの統合による機能拡張オプションに関する理解を証明する必要があります。

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SAP Certified Associate - Positioning SAP Business Suite 認定 C-BCSBS-2502 試験問題 (Q26-Q31):質問 # 26
An organization wants to streamline HR processes, ensure compliance with global regulations, and improve workforce planning. Which SAP solutions should they implement? There are 3 correct answers to this question.

• A. SAP SuccessFactors Compensation
• B. SAP Transportation Management
• C. SAP Fieldglass
• D. SAP Workforce Analytics
• E. SAP SuccessFactors Employee Central
  

正解：A、D、E

質問 # 27
What is Machine Learning?

• A. A technology that equips machines with human-like capabilities such as problem-solving, visual perception, speech recognition, decision-making, and language translation.
• B. A form of deep learning which utilizes foundation models, like large language models, to create new content, including text, images, sound, and videos, based on the data they were trained on.
• C. AI systems that use self-supervised learning on vast data to perform a variety of tasks, such as writing documents or creating images.
• D. A subset of AI that focuses on enabling computer systems to learn and improve from experience or data, incorporating elements from fields like computer science, statistics, and psychology.
  

正解：D
解説：
The question asks for the definition ofMachine Learningin the context of AI, which is relevant toSAP Business Suiteand itsSAP Business AIcomponent that leverages machine learning (ML) capabilities.
According to official SAP documentation and widely accepted AI literature,Machine Learningis a subset of artificial intelligence (AI) that focuses on enabling systems to learn and improve from experience or data, drawing on disciplines such as computer science, statistics, and psychology. This makes Option D the correct answer.
Explanation of Correct answer:
Option D: A subset of AI that focuses on enabling computer systems to learn and improve from experience or data, incorporating elements from fields like computer science, statistics, and psychology.
This is correct becauseMachine Learningis defined as a branch of AI that develops algorithms and models allowing computers to learn patterns from data and improve performance without being explicitly programmed. It integrates methodologies from computer science (e.g., algorithm design), statistics (e.g., probabilistic modeling), and psychology (e.g., cognitive modeling for learning behaviors). TheSAP Business AIdocumentation on learning.sap.com, in the context of AI withinSAP Business Suite, states:
"Machine Learning is a subset of AI that enables computer systems to learn from data and improve from experience. It leverages techniques from computer science, statistics, and psychology to build models that can predict outcomes, classify data, or optimize processes." This definition is consistent with industry standards, as noted inSAP Community Blogsand broader AI literature:
"Machine Learning (ML) is a field of AI that focuses on the development of algorithms that allow computers to learn from and make decisions or predictions based on data. It incorporates statistical methods, computational techniques, and insights from cognitive science to enable adaptive learning." WithinSAP Business Suite, machine learning is utilized through components likeSAP DatabricksandSAP Business Technology Platform (BTP)to support scenarios such as predictive analytics, anomaly detection, and process automation. For example,SAP Business AIembeds ML models in business processes (e.g., supply chain forecasting inSAP S/4HANA Cloud), relying on data-driven learning to enhance outcomes.
Explanation of Incorrect Answers:
Option A: A form of deep learning which utilizes foundation models, like large language models, to create new content, including text, images, sound, and videos, based on the data they were trained on.
This is incorrect because it inaccurately describes machine learning as a form ofdeep learningand limits it to foundation models like large language models (LLMs). In reality,deep learningis a subset of machine learning, not the other way around, and machine learning encompasses a broader range of techniques (e.g., decision trees, support vector machines, linear regression) beyond deep learning or generative models. The documentation clarifies:
"Machine Learning includes various approaches, such as supervised, unsupervised, and reinforcement learning, of which deep learning is a specialized subset using neural networks. Machine Learning is not limited to foundation models or content generation." This option is too narrow and misrepresents the relationship between machine learning and deep learning.
Option B: AI systems that use self-supervised learning on vast data to perform a variety of tasks, such as writing documents or creating images.
This is incorrect because it describes a specific type of AI system, such as generative AI or models relying on self-supervised learning (e.g., LLMs), rather than machine learning as a whole. Machine learning includes multiple learning paradigms (supervised, unsupervised, reinforcement) and is not restricted to self-supervised learning or tasks like document writing and image creation. The documentation notes:
"Machine Learning encompasses a wide range of techniques, including supervised learning for classification, unsupervised learning for clustering, and reinforcement learning for decision-making, not just self-supervised learning for generative tasks." This option is too specific and does not capture the full scope of machine learning.
Option C: A technology that equips machines with human-like capabilities such as problem-solving, visual perception, speech recognition, decision-making, and language translation.
This is incorrect because it describes the broader objectives ofArtificial Intelligence (AI)rather thanMachine Learningspecifically. While machine learning contributes to achieving these capabilities (e.g., through models for speech recognition or image classification), it is a method within AI, not the entirety of AI's scope. The documentation states:
"AI is the broader field that aims to create systems with human-like capabilities, such as problem-solving or language translation. Machine Learning is a subset of AI focused on data-driven learning and model development." This option is too broad and does not accurately define machine learning.
Summary:
Machine Learningis accurately defined as a subset of AI that focuses on enabling computer systems to learn and improve from experience or data, incorporating elements from computer science, statistics, and psychology, corresponding to Option D. Option A is incorrect because it mischaracterizes machine learning as a form of deep learning and limits it to foundation models. Option B is too narrow, focusing on self- supervised learning systems. Option C is too broad, describing AI generally. This definition aligns with SAP's use of machine learning withinSAP Business AIfor data-driven insights and process optimization inSAP Business Suite, as well as standard AI literature.

質問 # 28
Which SAP Business Suite applications help organizations manage financial processes? There are 3 correct answers to this question.

• A. SAP Business Planning and Consolidation
• B. SAP Controlling (CO)
• C. SAP Customer Data Cloud
• D. SAP Financial Accounting (FI)
• E. SAP Fieldglass
  

正解：A、B、D

質問 # 29
What are some components of SAP Business AI?
Note: There are 3 correct answers to this question.

• A. Enterprise data
• B. Customer centricity
• C. Technology foundation
• D. Processes
• E. Agility
  

正解：A、C、D
解説：
The question asks for the components ofSAP Business AI, which is a key pillar ofSAP Business Suitethat enables intelligent business processes through artificial intelligence. According to official SAP documentation, SAP Business AIis built on three core components: relevant business processes, enterprise data, and a technology foundation. These align with Options A, D, and E, making them the correct answers.
Explanation of Correct Answers:
Option A: Processes
This is correct becauseSAP Business AIis deeply embedded in business processes to deliver outcome-driven AI capabilities. SAP emphasizes that AI is integrated into end-to-end business processes (e.g., finance, supply chain, procurement) to enhance efficiency, automation, and decision-making. ThePositioning SAP Business Suitedocumentation on learning.sap.com states:
"SAP Business AI is designed to deliver value by embedding AI into relevant business processes. This ensures that AI capabilities are context-aware and drive specific business outcomes, such as optimizing supply chain operations or automating financial reconciliations." For example,SAP Joule, the generative AI copilot, is integrated into processes acrossSAP S/4HANA Cloudand other SAP applications to provide real-time insights and recommendations. The documentation further notes:
"The process component of SAP Business AI refers to the integration of AI into core business workflows, enabling intelligent automation and process optimization." This confirms that processes are a foundational component ofSAP Business AI.
Option D: Enterprise data
This is correct becauseSAP Business AIrelies on enterprise data to train and execute AI models effectively.
SAP emphasizes the importance of harmonized, high-quality data from SAP and third-party sources, managed through solutions likeSAP Datasphere, to power AI-driven insights. The documentation states:
"Enterprise data is a critical component of SAP Business AI, providing the foundation for training and deploying AI models. SAP Business AI leverages data from SAP applications, such as SAP S/4HANA, and external sources to deliver accurate and contextually relevant outcomes." For instance,SAP Business AIuses enterprise data to enable predictive analytics, anomaly detection, and personalized recommendations. The integration withSAP Business Data Cloudensures that data is accessible and governed, supporting AI use cases. The documentation further clarifies:
"SAP Business AI is powered by enterprise data, harmonized through SAP Datasphere, to ensure that AI models are built on a trusted and unified data foundation." This establishes enterprise data as a core component.
Option E: Technology foundation
This is correct becauseSAP Business AIis underpinned by a robust technology foundation, including theSAP Business Technology Platform (BTP), which provides tools for AI development, deployment, and integration.
This foundation includes AI services, machine learning frameworks, and infrastructure for scalability. The documentation notes:
"The technology foundation of SAP Business AI, built on SAP Business Technology Platform (BTP), provides the infrastructure and tools needed to develop, deploy, and manage AI models. This includes prebuilt AI services, integration capabilities, and support for generative AI." For example,SAP BTPenables the integration ofSAP Jouleand other AI capabilities into SAP applications, while also supporting custom AI development through tools like theSAP AI Core. The documentation adds:
"SAP Business AI's technology foundation ensures scalability, security, and seamless integration with SAP and non-SAP systems, enabling customers to innovate with AI." This confirms that technology foundation is a key component.
Explanation of Incorrect Answers:
Option B: Agility
This is incorrect because agility is not a component ofSAP Business AI. While agility may be an outcome or benefit of usingSAP Business AI(e.g., enabling faster decision-making or adaptable processes), it is not a structural component. The documentation does not list agility as part of the core framework ofSAP Business AI
. Instead, it focuses on processes, data, and technology:
"SAP Business AI comprises three main components: relevant business processes, enterprise data, and a technology foundation. These elements work together to deliver intelligent business outcomes." Agility may be associated with the broader value proposition ofSAP Business Suiteor cloud ERP, but it is not specific toSAP Business AI.
Option C: Customer centricity
This is incorrect because customer centricity is not a component ofSAP Business AI. WhileSAP Business AI can support customer-centric outcomes (e.g., personalized experiences through AI-driven insights), it is not a foundational component. The documentation emphasizes technical and operational components rather than strategic principles like customer centricity:
"SAP Business AI is built on a foundation of processes, data, and technology, enabling intelligent automation and insights across the enterprise." Customer centricity may be a guiding principle in SAP's go-to-market strategy or solution design, but it is not part of theSAP Business AIframework.
Summary:
SAP Business AIis composed of three core components: processes (embedding AI into business workflows), enterprise data (providing the data foundation for AI models), and technology foundation (enabling AI development and deployment viaSAP BTP). These correspond to Options A, D, and E. Options B (agility) and C (customer centricity) are incorrect, as they represent outcomes or principles rather than structural components ofSAP Business AI. This aligns with SAP's focus on delivering context-aware, data-driven, and technically robust AI capabilities withinSAP Business Suite.
References:
Positioning SAP Business Suite, learning.sap.com
SAP Business AI: Components and Capabilities, SAP Help Portal
SAP Business Technology Platform and AI Integration, SAP Community Blogs Introducing SAP Business AI, SAP Learning Hub

質問 # 30
Match the solutions to individual challenges in the dropdown box to the respective persona.

正解：
解説：

Explanation:
Step-by-Step Solution
1. CPO (Chief Procurement Officer)
Main Challenge: Procurement, supplier optimization, risk management.
Best Solution:
* Use AI-driven supplier insights to optimize supplier selection and manage procurement risks Reason:
CPOs focus on procurement efficiency, supplier management, and risk minimization. AI insights help select the best suppliers and mitigate procurement risks.
2. CIO (Chief Information Officer)
Main Challenge: IT modernization, technology innovation, and system integration.
Best Solution:
* Deliver IT modernization and AI-powered innovation with the SAP Business Suite Reason:
CIOs drive IT modernization and innovation. SAP Business Suite with AI powers digital transformation and future-ready IT infrastructure.
3. CHRO (Chief Human Resources Officer)
Main Challenge: Workforce planning, employee development, HR efficiency.
Best Solution:
* Utilize AI-infused workforce planning to identify gaps, upskill employees, and enhance HR interactions Reason:
CHROs want to optimize workforce management, fill talent gaps, and make HR processes smarter using AI.
4. COO (Chief Operating Officer)
Main Challenge: Operational efficiency, supply chain management, minimizing disruptions.
Best Solution:
* Harness AI-powered analytics to predict and respond to supply chain disruptions in real-time Reason:
COOs focus on ensuring smooth operations and a resilient supply chain; AI analytics help predict and manage disruptions.
5. CRO (Chief Revenue Officer)
Main Challenge: Customer experience, sales opportunities, revenue growth.
Best Solution:
* Apply AI-enabled personalization to customer interactions and predict sales opportunities Reason:
CROs are responsible for boosting revenue, improving customer relationships, and finding new sales opportunities through personalized experiences.
6. CFO (Chief Financial Officer)
Main Challenge: Financial forecasting, balancing growth with profitability.
Best Solution:
* Leverage AI-powered financial forecasting to enhance planning and balance growth with profitability Reason:
CFOs need accurate forecasting and strategic planning to maintain profitability and support sustainable growth.

質問 # 31
......
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