AIS Ch 5 MC questions

Lizelle is a financial analyst preparing an analytical database to analyze manufacturing data to determine the most efficient processes. She is currently identifying irrelevant and unreliable data in columns within a table. Which of the following is the most appropriate response when she identifies irrelevant or unreliable data? a. Remove the column with irrelevant or unreliable data. b. Abandon the project because the data are not valid and cannot be analyzed. c. Unpivot the column so that the data are separated into distinct fields. d. Put the relevant, irrelevant, reliable, and unreliable data in separate columns.

a. Remove the column with irrelevant or unreliable data.

Which of the following terms describes the process of profiling, cleaning, restructuring, and integrating data prior to processing and analysis? a. Data profiling b. Data preparation c. Data querying d. Data parsing

b. Data Preparation

Courtier is a high-end luxury goods store that designs and manufactures watches and jewelry and sells them worldwide. The table provides a small sample of the customer information they track. They have a very simple loyalty program. Customers earn "diamond" status if they have bought more than $10,000,000 worth of products, "gold" status if they have bought for more than $1,000,000, and "no" status if they have bought less than $1,000,000. Customer Code LoyaltyStatus Country 1 Diamond USA 2 Gold Italy 3 N/A US 4 Gold Based on the information given to you, how would you describe this data set's issues? a. Incorrect, inconsistent, incomplete, invalid b. Inconsistent, incomplete, invalid c. Inconsistent, invalid d. Incomplete, invalid

b. Inconsistent, incomplete, invalid

Which of the following statements is true about establishing a primary key in a table? a. Primary keys must use the same column title in all related tables. b. Primary keys must have a unique value for each row and have no null values. c. Primary keys and foreign keys are identical information repeated in the same table. d. If a table does not have a primary key, it cannot be included in an analytical database.

b. Primary keys must have a unique value for each row and have no null values.

Consider a scenario where the following validation rule was applied to the Employee table shown. VALID = IF EMPLOYEE.AGE < 24 AND EMPLOYEE.DEGREE = "COLLEGE", THEN "YES", ELSE "NO" Employee Table: Code. Age. Degree 1. 23. High School 2. 23. College 3. 25 College 4. 21 Which of the following statements is incorrect? a. The value for the Valid column for the employee with code "1" is "No." b. The value for the Valid column for the employee with code "2" is "No." c. The value for the Valid column for the employee with code "3" is "No." d. The value for the Valid column for the employee with code "4" is "No."

b. The value for the Valid column for the employee with code "2" is "No."

To restructure a composite column you should a. remove the column data that is not needed. b. split the column data into separate columns. c. create a separate table. d. move the column to another table.

b. split the column data into separate columns.

Hao, an accounts receivable analyst, is reviewing the customer master file for use in an analysis project. He noticed that in the Address field one customer's state is listed as NM, while another customer's state is listed as New Mexico. Which of the following is true? a. Hao has identified an instance of data that is incorrect. b. Hao has identified an instance of data that is invalid. c. Hao has identified an instance of data that is inconsistent. d. Hao has identified an instance of data that is not sizable.

c. Hao has identified an instance of data that is inconsistent.

Spencer, a financial analyst at an athletic apparel store, is preparing a master file of all professional sporting spokespersons for the company. Use the table to identify which of the following statements is true: Spokesperson Name Sport Bharat Arun, Bowling Bharat Arun Bowling Ramakrishnan Sridhar, Fielding Ramakrishnan Sridhar Fielding Virat Kohli, Batsman Virat Kohli Batsman Rohit Sharma, Batsman Rohit Sharma Batsman a. The Spokesperson column contains incorrect data, while the Name column contains invalid data. b. Spokesperson is a single-valued column, whereas Name is a composite column. c. Spokesperson is a composite column, whereas Name is a single-valued column. d. Spokesperson represents an aggregate column, whereas Name and Sport represent sliced columns.

c. Spokesperson is a composite column, whereas Name is a single-valued column.

Which of the following statements about star schemas is incorrect? a. Fact tables represent business transactions. b. Compared to fact tables, dimension tables typically have fewer instances and more columns. c. To make slicing data easier, most measures are defined as part of the dimension tables. d. Dimension tables are used for what, when, and who analysis. e. The grain represents the granularity level of a fact table.

c. To make slicing data easier, most measures are defined as part of the dimension tables.

Data cleaning can involve adding, modifying, and deleting data. In which of the following examples would data modifying be required? a. A file of purchase orders is missing one month of purchase orders. b. You are preparing an analysis of how many purchase orders are greater than $500. The file you are working with has a row at the beginning of each new month with the month name listed in the date column. There is no other data in the row with the month name. c. While examining the purchase orders data, you note that the vendor's name, street address, city and state are listed in separate columns. In some cases, the state column displays the state name, in other cases the state abbreviation is shown. d. During your analysis of purchase orders, you note that some of the orders include the vendor phone number.

c. While examining the purchase orders data, you note that the vendor's name, street address, city and state are listed in separate columns. In some cases, the state column displays the state name, in other cases the state abbreviation is shown.

Which of the following statements about data transformation are correct? 1. Data transformation has three subprocesses: cleaning, restructuring, and integration. 2. Cleaning aims to correct data anomalies. 3. Restructuring does not change any data, just the way they are organized. 4. Integration links the data together by defining relationships. a. 1 and 2 b. 1 and 3 c. 1, 2, and 4 d. 1, 2, 3, and 4

d. 1, 2, 3, and 4

You are preparing an analysis of vendors and purchase orders and have transferred data from the ERP system for all purchase orders in the past year and the data for all vendors. You have been provided with the following information about the data: 1. Total number of purchase orders: 15,786 2. Sum of all purchase orders: $1,567,679 3. Average purchase order amount: $99.31 4. Total number of vendors: 672 Which information is relevant to ensure the transfer of all data? a. 1 and 2 b. 2 and 3 c. 1 and 4 d. 1, 2, 3, and 4

d. 1, 2, 3, and 4

Mithali, a financial analyst, is preparing data to engage in a data analysis project to predict revenue for the next period based on environmental considerations. She is currently reviewing the tables to validate inter-table rules for referential integrity. Which of the following is true about referential integrity? a. The primary and foreign key fields must have the same name. b. The primary key must be single-valued, but the foreign key can be multi-valued. c. The primary and foreign key fields can have different data types. d. All values in a foreign key should also exist as values in the corresponding primary key.

d. All values in a foreign key should also exist as values in the corresponding primary key.

Hamza is transferring data into an analytical database for analysis. Which steps should he take to identify whether all the data transferred? a. Review the columns for appropriate naming conventions. b. Establish data validation rules and identify which data do not comply with the data validation rules. c. Inspect distinct values and analyze frequencies. d. Compare the row count for the analytical database with the row count of the data set in the ETL tool.

d. Compare the row count for the analytical database with the row count of the data set in the ETL tool.

Validation of data transfers is done by comparing the source data with the data in the ETL tool. Which of the following statements about data transfer is incorrect? a. The number of rows helps validate the completeness of data transfers. b. Sequence gap analysis helps validate the completeness of data transfers. c. Comparing the averages of a numeric field helps validate the accuracy of data transfers. d. Comparing column headers helps validate both completeness and accuracy. e. Comparing the sum of numeric fields helps validate the accuracy of data transfers.

d. Comparing column headers helps validate both completeness and accuracy.

You are examining the columns in a data set that you are analyzing. The following columns are in the data set. HotelPropertyID: Contains a unique identification number for each hotel. HotelAge: How many years the hotel has been operating since opening. OpeningDate: The date the hotel opened. RoomsAvailable: The number of rooms in the hotel. RoomsRented: The number of rooms rented during the year. Revenue: The amount of money collected for rooms rented during the year. After reviewing the column headings and description, which of the following would you note? a. There is no overlap between the columns. b. There is an overlap between HotelAge and OpeningDate. c. There is redundancy between RoomsAvailable and RoomsRented. d. There is dependency between HotelAge and OpeningDate.

d. There is dependency between HotelAge and OpeningDate.

Hofflak is a large construction company in the Northwest U.S. Their ERP system keeps track of all their assets. The data dictionary for the asset table looks as follows: Name: Description ID: An asset's unique ID. Description: An asset's description. AssetCategory: An asset's category. Price: The price paid for the asset. Salvam: The estimated book value of the asset at the end of its useful life. EstimatedLifetime: An asset's estimated lifespan. DprMethod: The depreciation method that applies to the asset. What column names would you change for data analysis purposes? a. ID, Salvam, DprMethod b. ID, Price, EstimatedLifetime, DprMethod c. Price, Salvam, EstimatedLifetime, DprMethod d. AssetCategory, Price e. Salvam, EstimatedLifetime, DprMethod f. EstimatedLifetime, DprMethod

e. Salvam, EstimatedLifetime, DprMethod