DATA ANALYTICS UTT - CHAPTER 9

Operational and informational systems are generally separated because of which of the following factors? A) A data warehouse centralizes data that are scattered throughout disparate operational systems and makes them readily available for decision support applications. B) A properly designed data warehouse decreases value to data. C) A separate data warehouse increases contention for resources. D) Only operational systems allow SQL statements.

A) A data warehouse centralizes data that are scattered throughout disparate operational systems and makes them readily available for decision support applications.

Which of the following factors drive the need for data warehousing? A) Businesses need an integrated view of company information. B) Informational data must be kept together with operational data. C) Data warehouses generally have better security. D) Reduce virus and Trojan horse threats.

A) Businesses need an integrated view of company information.

________ duplicates data across databases. A) Data propagation B) Data duplication C) Redundant replication D) A replication server

A) Data propagation

Data may be loaded from the staging area into the warehouse by following: A) SQL commands (Insert/Update). B) SQL purge. C) custom-written letters. D) virus checking.

A) SQL commands (Insert/Update).

Which of the following is a basic method for single-field transformation? A) Table lookup B) Cross-linking entities C) Cross-linking attributes D) Field-to-field communication

A) Table lookup

Converting data from the format of its source to the format of its destination is called: A) data transformation. B) data loading. C) data scrubbing. D) data storage.

A) data transformation.

Informational and operational data differ in all of the following ways EXCEPT: A) level of detail. B) normalization level. C) scope of data. D) data quality.

A) level of detail.

The major advantage of data propagation is: A) real-time cascading of data changes throughout the organization. B) duplication of non-redundant data. C) the ability to have trickle-feeds. D) virus elimination.

A) real-time cascading of data changes throughout the organization.

Data that are detailed, current, and intended to be the single, authoritative source of all decision support applications are called ________ data. A) reconciled B) subject C) derived D) detailed

A) reconciled

Informational systems are designed for all of the following EXCEPT: A) running a business in real time. B) supporting decision making. C) complex queries. D) data mining.

A) running a business in real time.

An expanded version of a star schema in which all of the tables are fully normalized is called a(n): A) snowflake schema. B) operational schema. C) DSS schema. D) complete schema.

A) snowflake schema.

The characteristic that indicates that a data warehouse is organized around key high-level entities of the enterprise is: A) subject-oriented. B) integrated. C) time-variant. D) nonvolatile.

A) subject-oriented.

One characteristic of independent data marts is complexity for end users when they need to access data in separate data marts. This complexity is caused by not only having to access data from separate databases, but also from: A) the possibility of a new generation of inconsistent data systems, the data marts themselves. B) lack of user training. C) denormalized data. D) incongruent data formats.

A) the possibility of a new generation of inconsistent data systems, the data marts themselves.

Which type of index is commonly used in data warehousing environments? A) Joint index B) Bitmapped index C) Secondary index D) Tri-dex

B) Bitmapped index

________ is/are a new technology which trade(s) off storage space savings for computing time. A) Dimensional modeling B) Columnar databases C) Fact tables D) Snowflake schemas

B) Columnar databases

The real-time data warehouse is characterized by which of the following? A) It accepts batch feeds of transaction data. B) Data are immediately transformed and loaded into the warehouse. C) It provides periodic access for the transaction processing systems to an enterprise data warehouse. D) It is based on Oracle technology.

B) Data are immediately transformed and loaded into the warehouse.

A class of database technology used to store textual and other unstructured data is called: A) mySQL. B) NoSQL. C) KnowSQL. D) PHP.

B) NoSQL.

Which of the following data-mining applications identifies customers for promotional activity? A) Population profiling B) Target marketing C) Usage analysis D) Product affinity

B) Target marketing

When we consider data in the data warehouse to be time variant, we mean: A) that the time of storage varies. B) data in the warehouse contain a time dimension so that they may be used to study trends and changes. C) that there is a time delay between when data are posted and when we report on the data. D) that time is relative.

B) data in the warehouse contain a time dimension so that they may be used to study trends and changes.

A logical data mart is a(n): A) data mart consisting of only logical data. B) data mart created by a relational view of a slightly denormalized data warehouse. C) integrated, subject-oriented, detailed database designed to serve operational users. D) centralized, integrated data warehouse.

B) data mart created by a relational view of a slightly denormalized data warehouse.

All of the following are ways to consolidate data EXCEPT: A) application integration. B) data rollup and integration. C) business process integration. D) user interaction integration.

B) data rollup and integration.

A technique using pattern recognition to upgrade the quality of raw data is called: A) data scrounging. B) data scrubbing. C) data gouging. D) data analysis.

B) data scrubbing.

When determining the size of a fact table, estimating the number of possible values for each dimension associated with the fact table is equivalent to: A) determining the number of DDL statements made to create a table. B) determining the number of possible values for each foreign key in the fact table. C) determining the number of DML statements made to create a table. D) determining the number of TRIGGERS used in the database.

B) determining the number of possible values for each foreign key in the fact table.

Going from a summary view to progressively lower levels of detail is called data: A) cubing. B) drill down. C) dicing. D) pivoting.

B) drill down.

A database action that results from a transaction is called a(n): A) transition. B) event. C) log entry. D) journal happening.

B) event.

The level of detail in a fact table determined by the intersection of all the components of the primary key, including all foreign keys and any other primary key elements, is called the: A) span. B) grain. C) selection. D) aggregation.

B) grain.

A method of capturing only the changes that have occurred in the source data since the last capture is called ________ extract. A) static B) incremental C) partial D) update-driven

B) incremental

The analysis of data or information to support decision making is called: A) operational processing. B) informational processing. C) artificial intelligence. D) data scrubbing.

B) informational processing.

A dependent data mart: A) is filled with data extracted directly from the operational system. B) is filled exclusively from the enterprise data warehouse with reconciled data. C) is dependent upon an operational system. D) participates in a relationship with an entity.

B) is filled exclusively from the enterprise data warehouse with reconciled data.

Event-driven propagation: A) provides a means to duplicate data for events. B) pushes data to duplicate sites as an event occurs. C) pulls duplicate data from redundant sites. D) triggers a virus.

B) pushes data to duplicate sites as an event occurs.

Conformed dimensions allow users to do the following: A) delete correlated data. B) query across fact tables with consistency. C) identify viruses in web sites. D) fix viruses in html documents.

B) query across fact tables with consistency.

Every key used to join the fact table with a dimension table should be a ________ key. A) primary B) surrogate C) foreign D) secondary

B) surrogate

Which of the following organizational trends does not encourage the need for data warehousing? A) Multiple, nonsynchronized systems B) Focus on customer relationship management C) Downsizing D) Focus on supplier relationship management

C) Downsizing

________ technologies are allowing more opportunities for real-time data warehouses. A) Web B) MOLAP C) RFID D) GPS

C) RFID

A data mart is a(n): A) enterprise-wide data warehouse. B) smaller system built upon file processing technology. C) data warehouse that is limited in scope. D) generic on-line shopping site.

C) data warehouse that is limited in scope.

A star schema contains both fact and ________ tables. A) narrative B) cross functional C) dimension D) starter

C) dimension

Grain and duration have a direct impact on the size of ________ tables. A) selection B) grain C) fact D) figure

C) fact

An operational data store (ODS) is a(n): A) place to store all unreconciled data. B) representation of the operational data. C) integrated, subject-oriented, updateable, current-valued, detailed database designed to serve the decision support needs of operational users. D) small-scale data mart.

C) integrated, subject-oriented, updateable, current-valued, detailed database designed to serve the decision support needs of operational users.

Data that are never physically altered once they are added to the store are called ________ data. A) transient B) override C) periodic D) complete

C) periodic

Data federation is a technique which: A) creates an integrated database from several separate databases. B) creates a distributed database. C) provides a virtual view of integrated data without actually creating one centralized database. D) provides a real-time update of shared data.

C) provides a virtual view of integrated data without actually creating one centralized database.

An approach to filling a data warehouse that employs bulk rewriting of the target data periodically is called: A) dump mode. B) overwrite mode. C) refresh mode. D) update mode.

C) refresh mode.

All of the following are unique characteristics of a logical data mart EXCEPT: A) logical data marts are not physically separate databases, but rather a relational view of a data warehouse. B) the data mart is always up-to-date since data in a view is created when the view is referenced. C) the process of creating a logical data mart is lengthy. D) data are moved into the data warehouse rather than a separate staging area.

C) the process of creating a logical data mart is lengthy.

The key discovery that triggered the development of data warehouses was: A) computer viruses. B) new ways to present information using mobile devices. C) the recognition of the differences between transactional systems and informational systems. D) the invention of the iPad.

C) the recognition of the differences between transactional systems and informational systems.

________ is an ill-defined term applied to databases where size strains the ability of commonly used relational DBMSs to manage the data. A) Mean data B) Small data C) Star data D) Big data

D) Big data

Which of the following is NOT an objective of derived data? A) Ease of use for decision support systems B) Faster response time for user queries C) Support data mining applications D) Eliminate the need for application software

D) Eliminate the need for application software

The process of transforming data from a detailed to a summary level is called: A) extracting. B) updating. C) joining. D) aggregating.

D) aggregating.

A characteristic of reconciled data that means the data reflect an enterprise-wide view is: A) detailed. B) historical. C) normalized. D) comprehensive.

D) comprehensive.

All of the following are ways to handle changing dimensions EXCEPT: A) overwrite the current value with the new value. B) for each dimension attribute that changes, create a current value field and as many old value fields as we wish. C) create a new dimension table row each time the dimension object changes. D) create a snowflake schema.

D) create a snowflake schema.

All of the following are tasks of data cleansing EXCEPT: A) decoding data to make them understandable for data warehousing applications. B) adding time stamps to distinguish values for the same attribute over time. C) generating primary keys for each row of a table. D) creating foreign keys.

D) creating foreign keys.

All of the following are some beneficial applications for real-time data warehousing EXCEPT: A) just-in-time transportation. B) e-commerce. For example, an abandoned shopping cart can trigger an e-mail promotional message. C) fraud detection in credit card transactions. D) data entry.

D) data entry.

A technique using artificial intelligence to upgrade the quality of raw data is called: A) dumping. B) data reconciliation. C) completion backwards updates. D) data scrubbing.

D) data scrubbing.

Loading data into a data warehouse does NOT involve: A) appending new rows to the tables in the warehouse. B) updating existing rows with new data. C) purging data that have become obsolete or were incorrectly loaded. D) formatting the hard drive.

D) formatting the hard drive.

All of the following are limitations of the independent data mart EXCEPT: A) separate extraction, transformation, and loading processes are developed for each data mart. B) data marts may not be consistent with one another. C) there is no capability to drill down into greater detail in other data marts. D) it is often more expedient to build a data mart than a data warehouse.

D) it is often more expedient to build a data mart than a data warehouse.

The process of combining data from various sources into a single table or view is called: A) extracting. B) updating. C) selecting. D) joining.

D) joining.

User interaction integration is achieved by creating fewer ________ that feed different systems. A) clients B) networks C) computers D) user interfaces

D) user interfaces

Factless fact tables may apply when: A) we are deleting sales. B) we are tracking sales. C) we are taking inventory of the set of possible occurrences. D) we are deleting correlated data.

D) we are deleting correlated data.

T/F: A conformed dimension is one or more dimension tables associated with only one fact table.

FALSE

T/F: A data mart is a data warehouse that contains data that can be used across the entire organization.

FALSE

T/F: A fact table holds descriptive data about the business.

FALSE

T/F: A separate data warehouse causes more contention for resources in an organization.

FALSE

T/F: A snowflake schema is usually heavily aggregated.

FALSE

T/F: After the extract, transform, and load is done on data, the data warehouse is never fully normalized.

FALSE

T/F: An approach in which only changes in the source data are written to the data warehouse is called refresh mode.

FALSE

T/F: An operational data store (ODS) is not designed for use by operational users.

FALSE

T/F: An operational data store typically holds a history of snapshots of the state of an organization whereas an enterprise data warehouse does not typically contain history.

FALSE

T/F: Data are moved to the staging area before extraction takes place.

FALSE

T/F: Data federation consolidates all data into one database.

FALSE

T/F: Data nationalization provides a virtual view of integrated data without actually bringing the data into one physical database.

FALSE

T/F: Data propagation duplicates data across databases, usually with some real-time delay.

FALSE

T/F: Data transformation is not an important part of the data reconciliation process.

FALSE

T/F: Independent data marts do not generally lead to redundant data and efforts.

FALSE

T/F: Logical data marts are physically separate databases from the enterprise data warehouse.

FALSE

T/F: Multidimensional OLAP (MOLAP) tools use variations of SQL and view the database as a relational database, in either a star schema or other normalized or denormalized set of tables.

FALSE

T/F: NoSQL is a great technology for storing well-structured data.

FALSE

T/F: Operational metadata are derived from the enterprise data model.

FALSE

T/F: Reconciled data are data that have been selected, formatted, and aggregated for end-user decision support applications.

FALSE

T/F: Static extract is a method of capturing only the changes that have occurred in the source data since the last capture.

FALSE

T/F: The development of the relational data model did not contribute to the emergence of data warehousing.

FALSE

T/F: The grain of a data warehouse indicates the size and depth of the records.

FALSE

T/F: The process of partitioning data according to predefined criteria is called aggregation.

FALSE

T/F: The process of transforming data from a detailed to a summary level is called selection.

FALSE

T/F: The process of transforming data from detailed to summary levels is called normalization.

FALSE

T/F: The representation of data in a graphical format is called data mining.

FALSE

T/F: The status of data is the representation of the data after an event has occurred.

FALSE

T/F: There are six major steps to ETL.

FALSE

T/F: Transient data are never changed.

FALSE

T/F: Update mode is used to create a data warehouse.

FALSE

T/F: When multiple systems in an organization are synchronized, the need for data warehousing increases.

FALSE

T/F: A corporate information factory (CIF) is a comprehensive view of organizational data in support of all user data requirements.

TRUE

T/F: A dependent data mart is filled from the enterprise data warehouse and its reconciled data.

TRUE

T/F: A method of capturing data in a snapshot at a point in time is called static extract.

TRUE

T/F: An enterprise data warehouse is the control point and single source of all data made available to end users for decision support applications.

TRUE

T/F: An enterprise data warehouse that accepts near-real time feeds of transactional data and immediately transforms and loads the appropriate data is called a real-time data warehouse.

TRUE

T/F: An event is a database action that results from a transaction.

TRUE

T/F: An independent data mart is filled with data extracted from the operational environment without the benefit of a data warehouse.

TRUE

T/F: An operational data store is typically a relational database and normalized, but it is tuned for decision-making applications.

TRUE

T/F: Application integration is achieved by coordinating the flow of event information between business applications.

TRUE

T/F: Bitmapped indexing is often used in a data warehouse environment.

TRUE

T/F: Data reconciliation occurs in two stages, an initial load and subsequent updates.

TRUE

T/F: Data scrubbing is a technique using pattern recognition and other artificial intelligence techniques to upgrade the quality of raw data before transforming and moving the data to the data warehouse.

TRUE

T/F: Drill-down involves analyzing a given set of data at a finer level of detail.

TRUE

T/F: ETL is short for Extract, Transform, Load.

TRUE

T/F: For performance reasons, it may be necessary to define more than one fact table for a star schema.

TRUE

T/F: Grain and duration have a direct impact on the size of fact tables.

TRUE

T/F: Informational systems are designed to support decision making based on historical point-in- time and prediction data.

TRUE

T/F: Joining is often complicated by problems such as errors in source data.

TRUE

T/F: Loading data into the warehouse typically means appending new rows to tables in the warehouse as well as updating existing rows with new data.

TRUE

T/F: Medical claims and pharmaceutical data would be an example of big data.

TRUE

T/F: One of the biggest challenges of the extraction process is managing changes in the source system.

TRUE

T/F: Organizations adopt data mart architectures because it is easier to have separate, small data warehouses than to get all organizational parties to agree to one view of the organization in a central data warehouse.

TRUE

T/F: Periodic data are data that are never physically altered or deleted once they have been added to the store.

TRUE

T/F: Refresh mode is an approach to filling the data warehouse that employs bulk rewriting of the target data at periodic intervals.

TRUE

T/F: Scalable technology is often critical to a data mart.

TRUE

T/F: The data reconciliation process is responsible for transforming operational data to reconciled data.

TRUE

T/F: The enterprise data model controls the phased evolution of the data warehouse.

TRUE

T/F: The first requirement for building a user-friendly interface is a set of metadata that describes the data in the data mart in business terms that users can easily understand.

TRUE

T/F: The major advantage of the data propagation approach to data integration is the near real- time cascading of data changes throughout the organization.

TRUE

T/F: The need for data warehousing in an organization is driven by its need for an integrated view of high-quality data.

TRUE

T/F: There are applications for fact tables without any nonkey data, only the foreign keys for the associated dimensions.

TRUE

T/F: User interaction integration is achieved by creating fewer user interfaces.

TRUE

T/F: When a dimension participates in a hierarchy, the database designer can normalize the dimension into a nested set of tables with 1:M relationships between them.

TRUE