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The Data Architecture exam assesses candidates' understanding and proficiency in designing, implementing, and managing data architecture solutions within organizations. Data architecture involves the development of data models, databases, data warehouses, and data integration processes to support business objectives and enable data-driven decision-making. This exam covers fundamental principles, methodologies, and best practices related to data architecture, including data modeling, database design, data governance, and data integration.
The Data Architecture exam covers the following topics :-
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While formal prerequisites vary by institution, candidates are generally expected to have prior experience in data modeling, database management, or data engineering.
The exam is designed to assess a candidate's ability to design, implement, and manage scalable, secure, and efficient data architecture solutions aligned with organizational needs.
Most exams have a duration of 2 to 3 hours, depending on the certifying body and the inclusion of practical components.
The exam typically includes multiple-choice questions, case studies, and practical design-based scenarios covering data modeling, integration, storage, governance, and cloud architecture.
The exam may be vendor-neutral or aligned with specific platforms such as AWS, Azure, or GCP, depending on the issuing organization. Some certifications focus on general architectural principles applicable across platforms.
Key focus areas include data modeling, data governance, integration strategies, cloud architecture, and performance optimization.
Yes, many organizations offer the exam online with remote proctoring, though in-person options may also be available at designated testing centers.
Passing scores typically range between 65% and 75%, depending on the exam provider and the overall difficulty of the exam.
Certification validity varies but is often valid for two to three years, after which recertification or continuing education may be required.
Candidates are encouraged to use official study guides, whitepapers, architectural frameworks (e.g., TOGAF, DAMA-DMBOK), online training platforms, and practical experience with data architecture tools and cloud environments.