SAP C_BDCDA AI Role-Plays (Scenarios) Exam Syllabus For New 2026 Format
Are you aware of the latest Exam C_BDCDA Syllabus changes in 2026 by SAP. The C_BDCDA exam topics covered on this page are based on SAP official content helping you understand exactly what skills and knowledge you are expected to demonstrate in the exam. Check your preparation against the latest C_BDCDA Exam Topics for SAP Certified - Data Architect - SAP Business Data Cloud (C_BDCDA_2605) to ensure you're aligned with the current exam requirements.
Please note that the SAP Certified - Data Architect - SAP Business Data Cloud (C_BDCDA_2605) C_BDCDA exam uses AI Role-Plays (Scenarios), meaning you will be evaluated on your ability to apply knowledge in real-world scenarios rather than answering traditional multiple-choice questions. Once you are familiar with what each Lesson expects you to know and do, test yourself using our free C_BDCDA questions. We also offer a premium C_BDCDA practice pack including Flashcards, Revision Notes and AI Role-Plays (Scenarios) Practice fully aligned to SAP 2026 new format, to help you measure your readiness before your actual exam day.
Learning Journey For SAP C_BDCDA Exam
Becoming a Certified SAP Data Architect
After completing this Learning Journey, you will be able to
Communicate complex data concepts clearly to technical and non-technical stakeholders.
Identify and prioritize Data Architecture opportunities, articulate business case and communicate its value to business stakeholders.
Design target-state architectures aligned to different Data Architecture patterns, SAP platform capabilities, and business goals.
Apply storytelling to overcome resistance and encourage adoption of data-driven change.
Develop a data strategy that treats data as a strategic asset in an AI-first world.
Establish the strategic context for Data Architecture across cloud platforms using DAMA-DMBOK and TOGAF as unifying frameworks.
Integrate SAP Business Data Cloud with third-party data platforms for unified AI workloads.
Design enterprise data solutions using SAP Business Data Cloud to unify SAP and non-SAP data through data fabric and data mesh patterns.
After completion of the course, you will be able to: - Explain how Data Architecture enables business value, efficiency, and innovation. - Communicate complex data concepts clearly to technical and non-technical stakeholders. - Identify and prioritize Data Architecture opportunities, articulate business case and communicate its value to business stakeholders. - Design target-state architectures aligned to different Data Architecture patterns, SAP platform capabilities and business goals. - Apply storytelling to overcome resistance and encourage adoption of data-driven change.
| 1 | Understanding Data Architecture Principles and Frameworks |
| 2 | Transforming Business Concepts with Data Modeling |
| 3 | Classifying Metadata for Effective Data Governance |
| 1 | Introducing Contemporary Data Architecture Patterns |
| 2 | Understanding Modern Data Architecture Paradigms |
| 3 | Selecting the Right Data Paradigm |
| 4 | Evaluating Data Integration Patterns: ETL, ELT, and Data Pipelines |
| 5 | Leveraging Data as a Product: The Next Evolution in Data Architecture |
| 1 | Establishing Effective Data Governance Frameworks |
| 2 | Understanding Data Security Measures |
| 3 | Developing Data Strategy for Business Impact |
| 1 | Translating Technical Excellence into Business Value |
| 2 | Positioning Data as a Strategic Enterprise Asset |
| 3 | Building Influence and Navigating Organizational Dynamics |
| 4 | Developing Executive Presence and Leading Strategic Conversations |
At the end of this course you will be able to: Develop a data strategy that treats data as a strategic asset in an AI-first world. Establish the strategic context for Data Architecture across cloud platforms using DAMA-DMBOK and TOGAF as unifying frameworks. Integrate SAP Business Data Cloud with third-party data platforms for unified AI workloads. Design enterprise data solutions using SAP Business Data Cloud to unify SAP and non-SAP data through data fabric and data mesh patterns.
| 1 | Transforming Data into a Strategic Asset |
| 2 | Establishing Data Strategy as the Foundation for AI Readiness |
| 3 | Modernizing Data Architecture for Advanced AI Workloads |
| 4 | Driving Data Strategy Through Leadership and Culture |
| 5 | Connecting Architecture Decisions to Business Value |
| 6 | Measuring Data Strategy Success |
| 7 | Analyze Real-World Examples of Data Architecture Value Patterns |
| 1 | Recognizing the Value of Frameworks in Modern Data Architecture |
| 2 | Evaluating Framework Roles in Data Architecture |
| 3 | Synthesizing TOGAF, DAMA-DMBOK, and SAP Enterprise Architecture Framework |
| 4 | Applying Integrated Frameworks to Data Mesh and Data Fabric |
| 5 | Applying Integrated Frameworks to Generative AI and Modern Data Architectures |
| 1 | Expanding the Open Data Ecosystem with SAP Business Data Cloud Connect |
| 2 | Integrating SAP Business Data Cloud with Databricks |
| 3 | Integrating SAP Business Data Cloud with Snowflake |
| 4 | Integrating SAP Business Data Cloud with Microsoft Fabric |
| 5 | Integrating SAP Business Data Cloud with Google BigQuery |
| 6 | Optimizing Data Federation Performance |
| 1 | Implementing SAP Business Data Cloud as a Business Data Fabric |
| 2 | Implementing Data Mesh in SAP Business Data Cloud |
| 3 | Architecting Semantic Models and Knowledge Graphs for Enterprise AI |
| 4 | Designing Hybrid Integration and Event Patterns |