Broadly, there are two types of knowledge, shallow and deep. Shallow knowledge

is simply what makes up a computer's response. If we can retrieve any answer by

framing a data query $($using SQL$)$ from an existing database system $($e$.$g$.$ JCU

student database system$),$ the output result retrieved will constitute shallow

knowledge about the data. For example, we may learn that Australian Stock

Exchange generally follows the lead of Wall Street, but we wouldn't necessarily

know why. Deep knowledge is the underlying reason behind such relationships.

Deep knowledge can be extracted using different machine learning techniques.

Data query is about searching in the data when we know exactly what we are

looking for. Example are:

A list of customers who used MasterCard to buy medicine from a pharmacy.

A list of employees who will reach retiring age next year.

These are all in the domain of shallow knowledge, which can easily be obtained by

simple data queries using, for instance, SQL$.$ By contrast, let us consider the

following examples:

Develop a profile of MasterCard holders who will take advantage of the

forthcoming sale promotion at the pharmacy

Develop a list of employees who are likely to avail themselves of the voluntary

early retirement scheme when they reach retirement age.

These are examples of deep knowledge whose answers cannot be obtained from

data queries, although machine learning techniques can unveil the information.

Identify whether each of the following is a data query or a machine learning

task$($s$)$:

a$)$ A social worker is interested in learning about the proportion of males to

females in the population of a particular region.

B$)$ a stock market analyst

c$)$ do single men play more golfe then married men

d$)$ determine the charecteristic of a succesful car salesperson

e$)$ determine weather credit card a credit card transaction is valid or fraudulent