CISP 501Study Guide Chapters- 3, 4, 5, & 6

CISP 501 Study Guide Chapters- 3, 4, 5, & 6 Chapter 3: Data Management, Big Data Analytics, and Records Management Test Bank Multiple Choice 1. The supply of oranges used by Coca-Cola has a three-month growing seas ...

CISP 501 Study Guide Chapters- 3, 4, 5, & 6 Chapter 3: Data Management, Big Data Analytics, and Records Management Test Bank Multiple Choice 1. The supply of oranges used by Coca-Cola has a three-month growing season, but orange juice production is year-round. Therefore, producing orange juice with a consistent taste year-round is complex. How does Coca-Cola deal with this complexity and keep their orange juice taste consistent? a. An orange juice decision model was developed that combines detailed data on the 600+ flavors that make up an orange. b. A decentralized data model was developed to respond to consumer preferences. c. Legacy systems were replaced with data silos to manage big data. d. A data governance program was implemented to ensure that juice preferences are achieved. 2. Which of the following data management technologies store data that are generated by business apps and operations? a. Data marts b. Data warehouses c. Databases d. Transaction-processing systems (TPS). 3. ________ tools and techniques process data and do statistical analysis for insight and discovery. a. Operational intelligence b. Business intelligence c. Information accessibility d. Information fluidity 4. Ad-hoc requests for specific data from a database or warehouse are called ___________. a. Unplanned reports b. Relational reports c. Volatile d. Queries 5. After data are extracted from a database and processed to standardize their format, they are then loaded into a ________ and ready for analysis. a. Synchronized, low-latency system b. Decision support system c. Data warehouse d. DBMS 6. Users typically access data as needed from a database management system by using ________. a. BI tools b. SQL c. Transaction processing d. Data filtering 7. Data in a ________ are volatile because record transactions. a. Database b. Data warehouse c. Data mart d. Data silo 8. Organizations that cannot afford the cost of a data warehouse, but want the benefits of online analytic processing invest in___________. a. spreadsheets b. big data c. text mining d. data marts 9. Data warehouses and data marts are optimized for all of the following except: a. Transaction processing b. Data mining c. Business intelligence d. Decision support 10. An accurate and consistent view of data throughout the enterprise is needed so one can make informed, actionable decisions that support the business strategy. A function performed by a DBMS to integrate, match, or link data from disparate sources is data _____________. a. filtering b. profiling c. synchronization d. maintenance 11. NoSQL systems have increased in popularity because of the growing need for ________, which means the system can increase in size to handle data growth. a. scalability b. fault tolerance c. handling big data d. large Web apps 12. Which of the following is not a consequence of poor quality or dirty data? a. The company loses sales or customers b. Information workers are constrained in their jobs c. Decision makers face too much uncertainty to make intelligent decisions d. The company faces data ownership problems 13. The primary benefits of centralized database configuration are better ___________ and ____________. a. Reports / Statistical Tools b. Quality / Security c. Speed / Access d. Software / Hardware 14. The primary disadvantage of centralized database systems is ________. a. Data security b. Data quality c. Availability of statistical analysis tools d. Transmission delay when users are geo-dispersed 15. Distributed databases use ___________ architecture to process information requests. a. Client/server b. NoSQL c. Private clouds d. Public clouds 16. In terms of the major functions performed by a DBMS, “data filtering and profiling” refers to which of the following? a. Inspecting the data for errors, inconsistencies, redundancies, and incomplete information. b. Correcting, standardizing, and verifying the integrity of the data. c. Integrating, matching, or linking data from disparate sources. d. Checking and controlling data integrity over time. 17. Sometimes organizations place too much emphasis on the costs associated with acquiring high levels of data quality and ignore the costs of “dirty” or poor quality data. Which of the following sets of factors make up the total costs associated with poor quality data? a. Technology costs + Training costs + Marketing costs b. Value of lost business + Cost to prevent errors + Cost to correct errors c. Cost of repeating analyses + Lost opportunity costs + Cost of unrealized threats d. Value of lost business + Cost to correct errors + Cost of repeating analyses (on corrected data) 18. Despite the need for high-quality data, ____________ and technical issues often make it a difficult goal to achieve. a. High costs b. Untrained or undertrained IT personnel c. User errors d. Organizational politics 19. The principle of ________ explains why most organizations cannot operate at peak performance with blind spots (lack of data availability) of 30 days or longer. Global financial services institutions rely on near-real-time data for peak performance. a. data in context b. 90/90 data use c. diminishing data value d. data quality 20. According to the ________, a majority of data is seldom accessed after a 3 month period. a. Principle of data in context b. Principle of 90/90 data use c. Principle of diminishing data value d. Principle of data quality 21. ___________ is a set of processes that aid organizations in integrating data from various sources or enterprise applications to create and maintain a more unified view of a customer, product, or other core data entity that is shared across systems. a. Client/server architecture b. OLAP c. Master data management d. Data synchronization 22. MDM consolidates data from various data sources into a __________, which then feeds data back to the applications, thereby creating accurate and consistent data across the enterprise. This helps eliminate redundancies, inaccuracies and omissions that might otherwise hinder business processes. a. Data silo b. Master reference file c. Backup file d. Central directory 23. ________ enable managers to predict how customers would behave and use that knowledge to be prepared to respond quickly. a. Big data analytics b. Data discovery c. Master data management d. Information optimization 24. In the past, data was transferred from OTLP databases to data warehouses periodically, say once a month or even once a week. Now, the trend is toward ___________, or real-time, data warehousing so that analysis and decision support apps use current data. a. Active b. Always-on c. Dynamic d. Operational 25. What is the key advantage of an active data warehouse compared to a traditional data warehouse? a. The ADW is less expensive to install and operate b. Data from an ADW can be used for DSS, report generation and Business Intelligence c. Data in an ADW is constantly updated, providing more current data than a traditional data warehouse d. The ADW is more widely available from a larger number of vendors 26. When customer X calls an organization’s customer service center, the service representative has access to a complete history of X’s previous calls, as a list of the services X has purchased, and customer profitability score. All of this information is up-to-date and is accessed from the company’s _________. a. Management information system b. Transactional database system c. Active data warehouse