Mastering the data requires a firm understanding of what data are available to you and where they
are stored, as well as being skilled in the process of extracting, transforming, and loading (ETL). -
Answers True
A flat file is a means of storing data in one place, such as in an Excel spreadsheet, as opposed to
storing the data in multiple tables, such as in a relational database. - Answers True
A foreign key is an attribute that is required to exist in each table of a relational database and serves
as the unique identifier for each record in a table. - Answers False
A primary key is an attribute that is required to exist in each table of a relational database and serves
as the unique identifier for each record in a table. - Answers True
A foreign key is an attribute that exists in relational databases in order to carry out the relationship
between two tables - Answers True
A composite primary key is made up of the three or more primary keys in the tables that it is linking. -
Answers False
Descriptive attributes are attributes that exist in relational databases that are neither primary nor
foreign keys. - Answers True
Once you have the extracted the data of interest, it will need to be validated for completeness and
existence. - Answers False
The E in IMPACT Cycle represents existence. - Answers False
The T in IMPACT Cycle represents transfer. - Answers False
The L in IMPACT Cycle represents loading - Answers True
In order to obtain the right data, it is important to have a firm grasp of what data is available and how
it is stored. - Answers True
Data normalization can reduce data redundancy and improve data integrity. - Answers True
Much like the IMPACT cycle, requesting data is often an iterative process. - Answers True
Unlike the IMPACT cycle, requesting data is not an iterative process. - Answers False
If the extraction and transformation steps have been done well, the loading part of the ETL process
should be the simplest step. - Answers True
After determining the purpose and scope of the data request, and obtaining the data, the next step is
to validate the data. - Answers True
Comparing the number of records that were extracted to the number of records in the source
database is an example of validating the data for integrity. - Answers False
Format negative numbers is an example of cleaning the data - Answers True
Format negative numbers is an example of cleaning the data - Answers True
Mastering the data can also be described via the ETL process. The ETL process stands for:
A) Extract, total, and load data.
B) Extract, transform, and load data.
C) Enter, transform, and load data.
D) Enter, total, and load data. - Answers B
All of the following are Audit Data Standards (ADS) developed by the American Institute of Certified
Accountants except:
A) Investments subledger standards
B) General Ledger standards
C) Procure-to-Pay subledger standards
D) Order-to-Cash subledger standards - Answers A
When using [EmployeeID] as the unique identifier of the Employee table, [EmployeeID] is an example
of which of the following: - Answers C
The purpose of extracting data is:
A) To validate the data for completeness and integrity
B) To load the data into the appropriate tool for analysis
C) To identify and obtain the data from the appropriate source
D) To identify which approach to data analytics should be used - Answers C
The purpose of transforming data is:
A) To validate the data for completeness and integrity
B) To load the data into the appropriate tool for analysis
C) To identify and obtain the data from the appropriate source
, D) To identify which approach to data analytics should be used - Answers A
The purpose of loading data is:
A) To validate the data for completeness and integrity
B) To load the data into the appropriate tool for analysis
C) To identify and obtain the data from the appropriate source
D) To identify which approach to data analytics should be used - Answers B
All of the following are included in the five steps of the ETL process except:
A) Determine the purpose and scope of the data request
B) Obtain the data
C) Validate the data for completeness and integrity
D) Scrub the data - Answers D
Which of the following best exemplifies a way that data will need to be cleaned after extraction and
validation:
A) Remove headings and subtotals
B) Validate date/time fields
C) Remove trailing zeroes
D) Compare string limits for text fields - Answers A
________ is the metadata that describes each attribute in a database.
A) Relational database
B) Data dictionary
C) Descriptive attributes
D) Flat file - Answers B
Remove headings or subtotals from data is an example of which of the following:
A) Validating the data for completeness
B) Validating the data for integrity
C) Cleaning the data
D) Obtaining the data - Answers C
Correcting inconsistencies across data is an example of which of the following:
A) Validating the data for completeness
B) Validating the data for integrity
C) Cleaning the data
D) Obtaining the data - Answers C
Formatting negative numbers in the data is an example of which of the following:
A) Validating the Data for Completeness
B) Validating the Data for Integrity
C) Cleaning the Data
D) Obtaining the Data - Answers C
Removing leading zeroes and non-printable characters from the data is an example of which of the
following:
A) Validating the data for completeness
B) Validating the data for integrity
C) Cleaning the data
D) Obtaining the data - Answers C
Comparing descriptive statistics for numeric fields within the data is an example of which of the
following:
A) Validating the data for completeness
B) Validating the data for integrity
C) Cleaning the data
D) Obtaining the data - Answers A
Comparing the number of records within the data is an example of which of the following:
A) Validating the data for completeness
B) Validating the data for integrity
C) Cleaning the data
D) Obtaining the data - Answers A
Validating date/time fields within the data is an example of which of the following:
A) Validating the data for completeness
B) Validating the data for integrity