Data Discrepancies — Learn free

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Questions 1–10 of 10

1. How do Data Discrepancies relate to Data Errors?
Question 1A. They are the same thingB. Data Errors are searched for, identified and fixed based on Data DiscrepanciesC. Data Discrepancies are caused only by Data ErrorsD. Data Errors are a type of Data DiscrepancyE. Neither can occur without a data warehouse
2. What is the dictionary definition of a discrepancy?
Question 2A. An exact match between two factsB. An illogical or surprising lack of compatibility or similarity between two or more factsC. A record that has been deletedD. A summary of detailed dataE. A change in the data arriving at a model
3. Which situation is described as a Data Discrepancy?
Question 3A. A dashboard loading in under three secondsB. A model retrained after new product types appearC. Manual calculations by a data analyst not matching numbers from automated reportsD. A dataset shared with the QA teamE. A query with no WHERE clause
4. In the *I Am Datapedia!* example, why do 9,093 plus 6,104 not equal the 'All Users' total of 14,018?
Question 4A. Because the totals came from different sourcesB. Because the arithmetic was done wronglyC. Because one figure was encryptedD. Because the report was in MDXE. Because the data was synthetic
5. Once Data Discrepancies are found, what is the next step?
Question 5A. Delete the conflicting reportsB. Migrate the data to the cloudC. Retrain the machine learning modelD. Start identifying data errors, code errors and business rule errorsE. Add more filters to the dashboard
6. Does a Data Discrepancy always mean there is a data issue?
Question 6A. Yes, alwaysB. No, discrepancies never indicate data issuesC. No, discrepancies are always caused by hardware faultsD. Yes, unless the data is in the cloudE. No, it may be that not all facts were used when processing both sides of the data
7. In the two-analyst example, why do the two reports' totals not match?
Question 7A. One summed all rows while the other filtered out one of the regionsB. One analyst used SQL and the other used MDXC. One report was run in developmentD. The table had duplicate primary keysE. The second analyst used synthetic data
8. Where are Data Discrepancies normally found?
Question 8A. In synthetic datasetsB. In OLAP cubesC. In data sitting in silosD. In geocoded recordsE. In canned reports
9. Which concept surfaced to address Data Discrepancies caused by silos?
Question 9A. Data IslandB. Scope CreepC. Concept DriftD. Single Source of Truth, delivered by a Data WarehouseE. Canned Data
10. In which data model forms does a Data Warehouse ideally consolidate silos to avoid Data Discrepancies?
Question 10A. Multiple isolated data islandsB. Separate development instances per teamC. A landing zone onlyD. A collection of spreadsheetsE. One Unified Data Model, or a Star or Snowflake schema