Data Hub — Learn free · DataAI Nexus

Self-check

Questions 1–10 of 10

1. What role does a data hub play in enterprise data architecture?
Question 1A. A new type of storage system replacing the data lakeB. A new analytical framework for machine learningC. The central integration and distribution point connecting analytical platforms with operational systemsD. A department-level data martE. A tool for scraping external websites
2. Which category of enterprise system includes ERP, CRM and core banking applications?
Question 2A. Data warehousesB. Data lakesC. Operational systems (OLTP)D. Semantic layersE. Data hubs
3. What practical problem does the division of enterprise systems into OLTP, data warehouse and data lake create?
Question 3A. Data cannot be encrypted across systemsB. Insights such as KPIs, predictions and classifications are hard to share back with the applications that drive day-to-day businessC. Warehouses cannot store historyD. OLTP systems become too slowE. Data lakes cannot store unstructured data
4. In the core banking example, which of the following is NOT one of the three separate pipelines connecting to the core banking system?
Question 4A. Integration with the mobile or online banking applicationB. Integration with the data warehouse for reporting and data miningC. Integration with the data lake for advanced analytics and data scienceD. Integration with a social media scraping toolE. Integration with the data lake for machine learning models
5. How does a data hub change the way applications connect to data sources?
Question 5A. Every application builds its own pipeline to every sourceB. Applications connect once to the hub, which in turn connects to all underlying datastoresC. Applications connect directly to the data lake onlyD. Applications export data to spreadsheetsE. Applications must be rewritten in a common language
6. In what forms can a data hub publish insights from BI, machine learning and data mining for enterprise applications?
Question 6A. Printed reportsB. APIs, data services or event streamsC. Tape backupsD. Star schemasE. Encrypted archives
7. How does a data hub go beyond the serving or semantic layer of a data warehouse or lakehouse?
Question 7A. It stores more historyB. It exposes curated data only to BI toolsC. It operationalises the same data, making it available to applications such as mobile banking, customer service platforms or ERP systemsD. It replaces dimensional modellingE. It removes the need for a data lake
8. What governance benefit comes from centralising access through a data hub?
Question 8A. Data products are distributed consistently with proper lineage, access controls and usage policiesB. Governance can be skipped because the hub is trustedC. Every application manages its own securityD. History is preserved automaticallyE. Data is stored in a single country
9. What problems arise from the proliferation of ad hoc pipelines that a data hub is meant to prevent?
Question 9A. Faster delivery and lower costB. Duplication, inconsistencies and security risksC. Loss of unstructured dataD. Excessive normalisationE. Slower OLTP transactions
10. What kind of exchange does a data hub create between analytical and transactional worlds?
Question 10A. A one-way flow from operational systems into the warehouseB. A two-way exchange, letting insights from warehouses and lakes flow back into operational systemsC. A batch-only overnight transferD. A read-only mirror of the data lakeE. No exchange; it isolates the two worlds