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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 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。
トピック 3	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。

SAP Certified Associate - Positioning SAP Business Suite 認定 C_BCSBS_2502 試験問題 (Q10-Q15):質問 # 10
What are some scenarios that SAP Business Data Cloud supports?
Note: There are 3 correct answers to this question.

• A. Training large language models
• B. Machine learning and artificial intelligence
• C. Advanced data modeling and data warehousing
• D. Risk management reporting
• E. Out-of-the-box reporting
  

正解：B、C、E
解説：
The question asks for scenarios supported bySAP Business Data Cloud, a Software-as-a-Service (SaaS) solution that integrates data management, analytics, and AI capabilities to meet the needs of modern organizations. According to official SAP documentation,SAP Business Data Cloudsupports a range of scenarios, including machine learning and artificial intelligence, advanced data modeling and data warehousing, and out-of-the-box reporting. These align with Options C, D, and E, making them the correct answers.
Explanation of Correct Answers:
Option C: Machine learning and artificial intelligence
This is correct becauseSAP Business Data Cloudexplicitly supports machine learning (ML) and artificial intelligence (AI) scenarios, particularly through its integration withSAP Databricks. This component provides data scientists with tools to develop and deploy AI/ML models using harmonized SAP and third-party data.
TheDescribing SAP Business Data Cloudlesson on learning.sap.com states:
"SAP Business Data Cloud can handle many use-cases including: Support the development of AI and machine learning models. ... SAP Databricks - to provide the data scientist with artificial intelligence (AI) / machine learning (ML) development tools." learning.sap.com Additionally, the documentation highlights:
"What makes SAP Business Data Cloud so powerful, is that it offers the tools and technologies to meet all data and analytics requirements of a modern and agile organization. It uses the latest technology to support scenarios such as: ... Machine learning and artificial intelligence." learning.sap.com This confirms thatSAP Business Data Cloudsupports AI/ML scenarios, such as predictive analytics, anomaly detection, and advanced automation, by leveragingSAP DatabricksandSAP Business Technology Platform (BTP)for scalable model development and deployment.
Option D: Advanced data modeling and data warehousing
This is correct becauseSAP Business Data Cloudprovides robust capabilities for advanced data modeling and data warehousing, primarily throughSAP Datasphere, which serves as the foundational data management layer. The documentation states:
"SAP Business Data Cloud provides data warehousing features including a manual data integration and data modeling approach, AI and machine learning based extensions of data models as well as innovative out-of-the- box reporting capabilities side-by-side." learning.sap.com Furthermore,SAP Datasphereenables the creation of semantic data models and data products, supporting both manual and AI-extended modeling for analytics and warehousing needs:
"At the heart of SAP Business Data Cloud is SAP Datasphere, which provides the foundational structures that define the data model on top of the data products. This includes predelivered SAP Business Data Cloud Intelligent Applications and Data Product scenarios but also scenarios with custom data models that can be manually extended with machine learning or AI." learning.sap.com This establishes advanced data modeling and data warehousing as a core scenario, enabling organizations to build and manage complex data architectures for analytics and reporting.
Option E: Out-of-the-box reporting
This is correct becauseSAP Business Data Cloudoffers innovative out-of-the-box reporting throughSAP Business Data Cloud Intelligent Applications, which provide prebuilt dashboards and insights with minimal configuration. The documentation notes:
"A key highlight of SAP Business Data Cloud is its out-of-the-box reporting capability, featuring SAP Business Data Cloud Intelligent Applications, which create business insights with a single click, empowering informed decision-making." learning.sap.com These Intelligent Applications automate the creation of artifacts, data provisioning, and dashboards, primarily usingSAP Analytics Cloudfor visualization:
"SAP Analytics Cloud stories are used to provide the required dashboard in out-of-the-box reporting scenarios with SAP Business Data Cloud Intelligent Applications. With its advanced visualization and planning functions, SAP Analytics Cloud serves the business user as a central tool for exploring the requested business insights or executing planning functions." learning.sap.com This confirms that out-of-the-box reporting is a supported scenario, streamlining analytics for business users.
Explanation of Incorrect Answers:
Option A: Training large language models
This is incorrect becauseSAP Business Data Clouddocumentation does not explicitly list training large language models (LLMs) as a supported scenario. WhileSAP Business Data Cloudsupports AI and ML throughSAP DatabricksandSAP BTP, the focus is on general ML models (e.g., predictive analytics, classification, forecasting) rather than specifically training LLMs, which require specialized infrastructure and massive datasets typically beyond the scope ofSAP Business Data Cloud. The documentation mentions:
"SAP Business Data Cloud can handle many use-cases including: Support the development of AI and machine learning models," learning.sap.com However, there is no reference to LLMs specifically. WhileSAP Business AIintegrates with generative AI (e.g., Jouleand partnerships with Cohere), these are focused on embedding AI capabilities into processes, not training LLMs from scratch. Training LLMs is more aligned with hyperscaler platforms or specialized AI frameworks, not a primary scenario forSAP Business Data Cloud.pages.community.sap.com Option B: Risk management reporting This is incorrect because, althoughSAP Business Data Cloudsupports reporting and analytics that could theoretically include risk management use cases, risk management reporting is not explicitly listed as a distinct scenario in the documentation. The supported scenarios focus on broader categories like out-of-the- box reporting, AI/ML, and data modeling/warehousing. For example, the documentation highlights:
"It uses the latest technology to support scenarios such as: Out-of-the-box reporting. Machine learning and artificial intelligence. Advanced data modeling and data warehousing. Powerful planning and reporting.
Intelligent data management." learning.sap.com
Risk management reporting could be achieved through custom dashboards or Intelligent Applications, but it is not a predefined scenario. In contrast,SAP Business AIsupports risk management in specific contexts (e.g., fraud detection in finance), but this is not a core scenario ofSAP Business Data Cloud. sap.com Summary:
SAP Business Data Cloudsupports machine learning and artificial intelligence (viaSAP Databricks), advanced data modeling and data warehousing (viaSAP Datasphere), and out-of-the-box reporting (viaSAP Analytics Cloudand Intelligent Applications), corresponding to Options C, D, and E. Option A (training large language models) is not a supported scenario, as the platform focuses on general AI/ML rather than LLM training.
Option B (risk management reporting) is not explicitly listed, as it falls under broader reporting capabilities rather than a distinct scenario. These answers align with SAP's focus on delivering a unified data and analytics platform for modern enterprises.
References:
Describing SAP Business Data Cloud, learning.sap.com learning.sap.com
Introducing SAP Business Data Cloud, learning.sap.com learning.sap.com
SAP Business Data Cloud,www.sap.comsap.com
SAP Business AI,www.sap.comsap.com
SAP Business AI | SAP Community, pages.community.sap.com

質問 # 11
What are some components of SAP Business AI?
Note: There are 3 correct answers to this question.

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

正解：B、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 # 12
A global retail company is struggling with fragmented customer data across multiple departments, leading to inefficiencies in sales and service operations. They need an SAP solution that integrates customer interactions, optimizes sales processes, and enhances customer insights. Which SAP solutions should they implement? There are 3 correct answers to this question.

• A. SAP CRM
• B. SAP Predictive Analytics
• C. SAP Ariba
• D. SAP ERP
• E. SAP Business Warehouse
  

正解：A、B、E

質問 # 13
What are some characteristics of trustworthy business AI? Note: There are 3 correct answers to this question.

• A. Reusable
• B. Reliable
• C. Resourceful
• D. Responsible
• E. Relevant
  

正解：B、D、E
解説：
Trustworthy business AI is a cornerstone of SAP's Business AI strategy, ensuring that AI solutions are ethical, effective, and aligned with enterprise needs. SAP emphasizes characteristics that build trust in AI deployments, particularly in the context of SAP Business Data Cloud and SAP S/4HANA, to deliver outcomes that are dependable and business-ready. The question asks for the characteristics of trustworthy business AI, with three correct answers. Below, each option is evaluated based on official SAP documentation, SAP Learning materials, and relevant web sources from the provided search results, ensuring alignment with the "Positioning SAP Business Suite" and "SAP Business AI" narratives.
* Option A: ResourcefulWhile being resourceful (i.e., efficiently utilizing resources) may be a desirable trait for AI systems in general, it is not explicitly identified as a characteristic of trustworthy business AI in SAP's documentation. SAP focuses on attributes like relevance, responsibility, and reliability to define trustworthiness, emphasizing ethical and dependable outcomes over resource efficiency. The term "resourceful" does not appear in the context of trustworthy AI in the provided materials.Extract:
"SAP Business AI is built on a foundation of responsible AI, ensuring transparency, fairness, and compliance. Our solutions prioritize ethical AI practices to minimize bias and deliver trusted outcomes for your business." This option is incorrect.
* Option B: ReusableReusability, such as reusing AI models or data products across applications, is a practical feature in some AI systems but is not a defining characteristic of trustworthy business AI according to SAP's framework. Trustworthy AI is more about ensuring the AI is ethical, accurate, and contextually appropriate, rather than its ability to be reused. The documentation does not highlight reusability as a key attribute of trustworthy AI, focusing instead on attributes that ensure trust and dependability.Extract: "Foster reliable AI: Ensure data across applications and operations has a foundation for generative AI that is reliable, responsible, and relevant." This option is incorrect.
* Option C: RelevantRelevance is a critical characteristic of trustworthy business AI, ensuring that AI outputs are contextually appropriate and aligned with specific business needs. SAP's Business AI, including tools like Joule and SAP Business Data Cloud, leverages semantically rich data to deliver AI insights that are relevant to business processes in areas like Finance, Supply Chain, and HR. The documentation explicitly identifies relevance as a key attribute, emphasizing that trustworthy AI must provide meaningful, business-specific results.Extract: "Foster reliable AI: Ensure data across applications and operations has a foundation for generative AI that is reliable, responsible, and relevant." Extract: "SAP Business AI delivers relevant outcomes by embedding AI into business processes, ensuring that insights and recommendations are tailored to your specific business context." This option is correct.
* Option D: ResponsibleResponsibility is a fundamental characteristic of trustworthy business AI, encompassing ethical practices, transparency, and fairness to minimize bias and ensure compliance with regulations. SAP's AI strategy prioritizes responsible AI to build trust, ensuring that AI systems operate ethically and align with corporate governance standards. This is a core focus in SAP's documentation and marketing materials, making it a key characteristic of trustworthy AI.Extract: "SAP Business AI is built on a foundation of responsible AI, ensuring transparency, fairness, and compliance. Our solutions prioritize ethical AI practices to minimize bias and deliver trusted outcomes for your business." Extract:
"Foster reliable AI: Ensure data across applications and operations has a foundation for generative AI that is reliable, responsible, and relevant." This option is correct.
* Option E: ReliableReliability is a crucial characteristic of trustworthy business AI, ensuring that AI systems deliver consistent, accurate, and dependable results. SAP emphasizes reliability to ensure that AI outputs can be trusted for critical business decisions, supported by high-quality data and robust governance. The documentation consistently highlights reliability as a key attribute of trustworthy AI, particularly in the context of SAP Business Data Cloud and SAP Business AI.Extract: "Foster reliable AI: Ensure data across applications and operations has a foundation for generative AI that is reliable, responsible, and relevant." Extract: "SAP Business AI ensures reliable outcomes by leveraging trusted data and advanced governance, enabling businesses to depend on AI for critical decision-making." This option is correct.
Summary of Correct Answers:
* C: Relevant AI ensures contextually appropriate, business-specific outcomes, aligning with enterprise needs.
* D: Responsible AI prioritizes ethical practices, transparency, and fairness to minimize bias and ensure compliance.
* E: Reliable AI delivers consistent, accurate, and dependable results, building trust in business applications.
References:
SAP.com: SAP Business AI
SAP Learning: Positioning SAP Business Suite
SAP Learning: Positioning SAP Business Data Cloud
SAP.com: SAP Business Data Cloud
Delaware UK & Ireland: Unleash transformative insights with SAP Business Data Cloud SAP and Databricks Power New Era of Business Data and AI | Procurement Magazine SAP Launches Business Data Cloud to Transform Enterprise AI | Technology Magazine

質問 # 14
How does integrating SAP Databricks within SAP Business Data Cloud reduce IT overhead for customers?

• A. By eliminating the need for rebuilding data structures and business logic externally
• B. By providing pre-built connectors to various data sources
• C. By streamlining data governance processes and minimizing the need for complex data security configurations
• D. By automating data ingestion pipelines
  

正解：A
解説：
SAP Business Data Cloud (BDC) is a fully managed Software-as-a-Service (SaaS) solution that unifies and governs SAP and non-SAP data, integrating SAP Databricks to enable advanced analytics and AI-driven insights. The question asks how the integration of SAP Databricks within SAP BDC reduces IT overhead for customers, with one correct answer. Below, each option is evaluated based on official SAP documentation, SAP Learning materials, and relevant web sources from the provided search results, ensuring alignment with the "Positioning SAP Business Data Cloud" narrative and focusing on the role of SAP Databricks.
* Option A: By automating data ingestion pipelinesWhile SAP BDC, including its SAP Datasphere component, supports data integration and pipeline management, the automation of data ingestion pipelines is not a primary focus of SAP Databricks' integration. SAP Databricks is designed to enhance AI/ML, data science, and data engineering capabilities, leveraging zero-copy data sharing via Delta Sharing to access data products. Although SAP BDC as a whole may reduce some pipeline management overhead, the specific role of SAP Databricks is not to automate ingestion pipelines but to utilize pre-curated data products without requiring complex ETL processes. The documentation does not emphasize automated ingestion pipelines as a key IT overhead reduction mechanism for SAP Databricks.Extract: "SAP Business Data Cloud is deeply integrated across SAP applications, so your most critical data retains its original business context and semantics and the hidden costs of data extracts are eliminated-saving you time, resources, and effort." This option is incorrect.
* Option B: By providing pre-built connectors to various data sourcesSAP BDC provides pre-built connectors to SAP and non-SAP data sources through its foundation services and SAP Datasphere, enabling seamless data integration. However, this capability is not specifically tied to the SAP Databricks component. SAP Databricks leverages these connections indirectly by accessing data products shared via Delta Sharing, but it does not provide the connectors itself. The documentation highlights SAP BDC's overall integration capabilities, not SAP Databricks' role in providing connectors, as the primary mechanism for reducing IT overhead.Extract: "Effortlessly connect to contextual SAP data and blend with third-party data-without managing pipelines and copying data." This option is incorrect.
* Option C: By streamlining data governance processes and minimizing the need for complex data security configurationsSAP Databricks integrates with Unity Catalog for governance, which enhances data management and security within the SAP BDC environment. SAP BDC itself provides unified provisioning, security, and compliance, reducing some governance overhead. However, while governance is improved, the primary IT overhead reduction from SAP Databricks comes from eliminating the need to replicate and re-engineer data externally, not from streamlining governance processes. The documentation emphasizes data sharing and semantic preservation over governance simplification as the key benefit of SAP Databricks integration.Extract: "SAP Databricks uses both generative and traditional AI to understand your organization's data, business terms, and key metrics, so teams can work with data using natural language. It makes it easier to find, organize, manage, and govern data through Unity Catalog..." This option is incorrect.
* Option D: By eliminating the need for rebuilding data structures and business logic externallyThe integration of SAP Databricks within SAP BDC significantly reduces IT overhead by eliminating the need to rebuild data structures and business logic externally. Traditionally, customers replicate SAP data into external platforms, requiring complex ETL processes to clean, transform, and recreate business logic, which increases costs and maintenance efforts. SAP Databricks, through native integration and zero-copy Delta Sharing, provides direct access to curated, semantically rich SAP data products (e.g., from SAP S/4HANA) within the SAP BDC environment. This preserves business context and semantics, avoiding the need to re-engineer data structures or logic, thus reducing development, maintenance, and operational overhead. This is explicitly highlighted in the documentation as a key benefit of the SAP-Databricks partnership.Extract: "Today, customers often replicate SAP data into external platforms to clean, train models, deploy them, run inference, and push results back-introducing complexity, higher costs, and governance gaps. SAP Databricks offers a better path. Customers can now run end-to-end AI, ML, and analytics directly within SAP Business Data Cloud-without needing separate platforms or physical data replication." Extract: "Built-In Business Semantics: Because SAP data already carries deep business context and semantics, Databricks can provide powerful analytics and machine learning without forcing customers to re-invent data pipelines or guess at the meaning of fields." Extract: "SAP Databricks also offers significantly improved data latency... This enhanced latency is possible due to the Delta Sharing approach which enables direct access to clean, curated and context-rich data products with business semantics already incorporated. ... [This] results in a reduction of processing costs and lowering the overheads for initial development and ongoing maintenance of ETL processes." This option is correct.
Summary of Correct answer:
* D: Integrating SAP Databricks within SAP BDC reduces IT overhead by eliminating the need to rebuild data structures and business logic externally, leveraging zero-copy Delta Sharing to access curated SAP data products with preserved business semantics, thus minimizing complex ETL processes and maintenance costs.
References:
SAP.com: SAP Business Data Cloud
SAP.com: SAP Databricks in Business Data Cloud
SAP Learning: Illustrating the Role of SAP Databricks in SAP Business Data Cloud Databricks Blog: Announcing the General Availability of SAP Databricks on SAP Business Data Cloud Advancing Analytics: SAP Databricks: Solving The SAP Interoperability Challenge?
SAP Community: SAP Databricks in SAP Business Data Cloud: Unifying SAP Business Data with Lakehouse Intelligence SAP Business Data Cloud - Making Data Work Together | by Sandip Roy | Medium

質問 # 15
......
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