A point to point interface architecture will, in general, have more or less interface formats than a service- oriented architecture?
Answer(s): D
In general, a point-to-point interface architecture tends to have fewer interface formats compared to a service- oriented architecture (SOA). In point-to-point systems, each pair of systems communicates directly with each other, often using specific, customized formats. On the other hand, SOA promotes reusable and standardized services, which can result in more varied interface formats to accommodate different service interactions across the architecture.
The first morning an attribute is approaching an established data quality threshold, the following should take place:
Answer(s): A
When an attribute is approaching an established data quality threshold, the best course of action is to establish a set of activities aimed at addressing the emerging issue. This might involve investigating the cause of the potential problem, identifying the impact, and implementing measures to either prevent or mitigate the issue before it crosses the threshold. Proactive planning and actions are key to maintaining data quality.
Which of the following is a Data Quality principle?
Answer(s): E
All of the listed options are important Data Quality principles.Prevention focuses on avoiding data quality issues before they arise.Governance ensures that data management practices are enforced, and responsibilities are clear.Criticality refers to recognizing which data is most important and ensuring its quality is prioritized.Standards Driven means adhering to established data quality standards for consistency and accuracy.
One of a business data steward's key responsibilities is to:
A business data steward is responsible for managing business metadata, which includes defining, maintaining, and ensuring the proper use of business-related data definitions and standards. This role is focused on ensuring that data is accurately classified, understood, and used consistently across the organization in alignment with business goals.
I assess the quality of the data and then, 2 steps later, I seem to have lost the analysis and do it again. This is mainly a failure of:
The issue described reflects a failure in data quality management, where the initial data analysis is lost or not properly tracked, requiring the analysis to be repeated. Effective data quality management ensures that data is accurate, accessible, and well-maintained throughout the analysis process, preventing loss of work or redundancy.
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