Data Virtualization — Learn free

Self-check

Questions 1–10 of 10

1. What is data virtualization?
Question 1A. A physical copy of all source systems held in a data warehouseB. A virtual consumption layer that connects directly with many data storesC. A technique for compressing data before it is storedD. A backup process that snapshots databases nightlyE. A method of encrypting data in transit
2. What happens to the data models of the source systems when data virtualization is used?
Question 2A. They are converted to a star schemaB. They are merged into a single physical tableC. They remain intact with no changeD. They are replaced by NoSQL documentsE. They are archived and rebuilt in the data lake
3. Data virtualization is described as one of the solutions for which problem statement?
Question 3A. Data sovereigntyB. Data compressionC. Data encryptionD. Data subsettingE. Data scraping
4. What does a data virtualization layer host in order to expose data?
Question 4A. Physical copies of every source tableB. Scheduled ETL jobs that move data nightlyC. Views connecting directly with the data stores, which may be one-to-one or complex SQL viewsD. Encrypted archives of historical dataE. Cubes pre-aggregated for each department
5. What name is given to the second, third or fourth virtual data model layers built on top of the base virtualization layer?
Question 5A. Staging areaB. Virtual semantic layerC. Operational data storeD. Data martE. Business vault
6. In the data quality example, a data issue is found in three source systems and one data mart. What does data virtualization allow?
Question 6A. The issue is logged and each system owner fixes it separatelyB. Only the data mart copy can be correctedC. The four data stores are dropped and reloadedD. The data in all four data stores can be fixed directly from the virtualization layerE. The affected rows are hidden from the BI tool until IT resolves them
7. A user wants one more column in a BI dashboard, but the pipeline from Data Lake to Data Warehouse to Data Marts to OLAP would take weeks or months. What solution does data virtualization offer?
Question 7A. Virtualize the source systems to get the column immediately, while IT processes the pipeline in parallelB. Skip the data warehouse permanently and build the dashboard on the lakeC. Ask the user to wait until the next pipeline releaseD. Hard-code the column value in the BI toolE. Export the source table to a spreadsheet for the user
8. Which of the following is NOT an advantage claimed for data virtualization?
Question 8A. Single access point to all data storesB. No additional storage is neededC. Users write queries in one language and a wrapper talks to the connected systemsD. Historical data is maintained automaticallyE. Write-back capability into source systems
9. Why does data virtualization carry a performance impact as a disadvantage?
Question 9A. Because all data is duplicated into an in-memory cacheB. Because some queries run at the source systemsC. Because the semantic layer must be rebuilt after each queryD. Because ETL jobs compete for the same serverE. Because views are stored on slow disk
10. Alongside which data store is data virtualization said to be best suited?
Question 10A. A Kafka streamB. A NoSQL databaseC. A data warehouse already in placeD. A web scraping toolE. A spreadsheet