1.4.1 Data Types

Question 1

What are the 5 primitive data types?

Answer 1

• Real / Floating Point – Stores decimal numbers (3.141)
• Character - A single letter, number or special character (‘H’)
• String – A collection of characters (“Hello World”)
• Boolean – TRUE or FALSE
• Integer – A positive or negative whole number (24, -34)

Question 2

What is casting?

Answer 2

The process of changing one data type into another.

Question 3

What is a character set?

What are some examples?

Answer 3

• A list of the characters the computer can represent.
• Each character is represented by a unique binary value.
• Used to map binary values to characters.
• Examples: UNICODE and ASCII

Question 4

Describe the ASCII character set

Answer 4

• ASCII is a character set which is a subset of UNICODE
• Uses 7 bits, or 8 bits for extended ASCII
• Fewer characters can be represented than UNICODE
• Characters from different languages cannot be represented in ASCII

Question 5

Describe the UNICODE character set

Answer 5

• Each character is represented by 1-4 bytes.
• It supports a very large number of characters
• It is backwards compatible with ASCII
• Text using UNICODE rather than ASCII would take up more storage (roughly 4 times more)

Question 6

Left shift the following 8-bit number 3 places: 00111010

What mathematical operation is this equivalent to?

Answer 6

Remove the required number of bits from the left

Add the same number of zeros to the right

11010000

Equivalent to multiplying the number

Question 7

Right shift the following 8-bit number 3 places: 00111010

What mathematical operation is this equivalent to?

Answer 7

Remove the required number of bits from the right

Add the same number of zeros to the left

00000111

Equivalent to dividing the number

Question 8

Convert 177 to an unsigned 8-bit binary number

Answer 8

10110001

128+32+16+1 = 177

Question 9

Convert the unsigned 8-bit binary number 10110010 to denary

Answer 9

128+32+16+2 = 178

Question 10

Convert 188 to Hex

Answer 10

BC

Question 11

Convert the hex FE to a denary number

Answer 11

11111110 = 254

Question 12

Convery -49 to an 8-bit binary number using two’s complement

Answer 12

11001111

Question 13

Convert 49 to an 8-bit binary number using two’s complement

Answer 13

00110001

Question 14

Convert -49 to an 8-bit binary number using sign and magnitude

Answer 14

10110001

Question 15

Add the following two binary numbers

01101010 + 00111111

Answer 15

10101001

Carries - 11111100

Question 16

Subtract the following two binary numbers

00101111 - 00010111

Answer 16

00011000

An overflow should have occurred

Question 17

Mask 11010101 using the AND mask 10101010

Answer 17

10000000

Question 18

Mask 11010101 using the OR mask 10101010

Answer 18

11111111

Question 19

Mask 11010101 using the XOR mask 10101010

Answer 19

01111111

Question 20

Normalise 0001011000 001100

Answer 20

0101100000 001010

(Remove the two extra bits from the front of the mantissa and reduce the exponent value by 2)

Question 21

Convert 5.25 to an 6-bit mantissa and 3-bit exponent

Answer 21

010101 011

Question 22

Convert -5.25 to an 6-bit mantissa and 3-bit exponent

Answer 22

101011 011

Question 23

Convert 01110 0001 to floating point denary

Answer 23

1.75

Question 24

Convert 10010 0001 to floating point denary

Answer 24

-1.75

Question 25

Convert 0.125 to an 6-bit mantissa and 3-bit exponent

Answer 25

010000 110

Question 26

Convert -0.125 to an 6-bit mantissa and 3-bit exponent

Answer 26

110000 110

Remember to normalise

Question 27

Add the following floating point binary numbers stored in the two’s complement format.

0110 0010 and 0100 0011

Answer 27

0111 0011

Question 28

Why do we normalise floating point numbers?

Answer 28

Allows for more accuracy/precision from the given number of bits

So that the representation of each binary value is unique

Question 29

What impact does increasing the size of the exponent have?

Answer 29

Increases the size of the number that can be stored.

Question 30

What impact does increasing the size of the mantissa have?

Answer 30

Increasing the number of bits used for the mantissa increases the precision of the number that can be stored.

Question 31

Subtract the following floating point binary numbers stored in the two’s complement format.

010010 0100 - 010010 0010

Answer 31

0110110 0011