JT(IT) 2016 PART-B Computer Science

Question 1

If a random variable takes a finite set of values it is called:

Continuous variate
Normal variate
Discrete variate
Exponential variate
       Engineering-Mathematics       Probability
Question 1 Explanation: 
→ A discrete variable is a variable whose value is obtained by counting.
number of students present
number of red marbles in a jar
number of heads when flipping three coins
students’ grade level
→ A discrete random variable X has a countable number of possible values.
Let X represent the sum of two dice.
→ A discrete random variable is one which may take on only a countable number of distinct values such as 0,1,2,3,4,........ Discrete random variables are usually (but not necessarily) counts. If a random variable can take only a finite number of distinct values, then it must be discrete.
Examples of discrete random variables include the number of children in a family, the Friday night attendance at a cinema, the number of patients in a doctor's surgery, the number of defective light bulbs in a box of ten.
Question 2

In client-server computing vertical scaling means:

Adding or removing client workstations with only a slight performance impact
Migrating servers to a new group of client workstations
Migrating client workstations to a larger and faster server machine or multi servers
Combining two or more client workstation groups
       Computer-Networks       Client-Server-Computing
Question 2 Explanation: 
→ Vertical scaling refers to adding more resources (CPU/RAM/DISK) to your server (database or application server is still remains one) as on demand.
→ Vertical Scaling is most commonly used in applications and products of middle-range as well as small and middle-sized companies. One of the most common examples of Virtual Scaling is to buy an expensive hardware and use it as a Virtual Machine hypervisor (VMWare ESX).
→ Vertical Scaling usually means upgrade of server hardware. Some of the reasons to scale vertically includes increasing IOPS (Input / Output Operations), amplifying CPU/RAM capacity, as well as disk capacity.
→ However, even after using virtualization, whenever an improved performance is targeted, the risk for downtimes with it is much higher than using horizontal scaling.
Question 3

If the mean of a poisson distribution is m, then standard deviation of the distribution is:

       Engineering-Mathematics       Probability
Question 3 Explanation: 
Mean and Variance of the Poisson distribution. There is also a formula for the standard deviation, σ, and variance, σ2.
Question 4

The 10Base5 cabling scheme of ethernet uses:

Twisted pairs
Fiber optics
Thin coax
Thick coax
       Computer-Networks       Ethernet
Question 4 Explanation: 
The original cabling standard for Ethernet that uses coaxial cables. The name derives from the fact that the maximum data transfer speed is 10 Mbps, it uses baseband transmission, and the maximum length of cables is 500 meters.
10Base5 is also called thick Ethernet, ThickWire, and ThickNet.
→ The number 10: At the front of each identifier, 10 denotes the standard data transfer speed over these media - ten megabits per second (10Mbps).
→ The word Base: Short for Baseband, this part of the identifier signifies a type of network that uses only one carrier frequency for signaling and requires all network stations to share its use.
→ The segment type or segment length: This part of the identifier can be a digit or a letter:
- Digit - shorthand for how long (in meters) a cable segment may be before attenuation sets in.
For example, a 10Base5 segment can be no more than 500 meters long.
- Letter - identifies a specific physical type of cable.
For example, the T at the end of 10BaseT stands for twisted-pair.
Question 5

The standard deviation of binomial distribution with n observations and probability of success p, probability of failure is q is:

       Engineering-Mathematics       Probability
Question 5 Explanation: 
Mean and Variance of the Binomial Distribution:
The binomial distribution for a random variable X with parameters n and p represents the sum of n independent variables Z which may assume the values 0 or 1. If the probability that each Z variable assumes the value 1 is equal to p, then the mean of each variable is equal to l*p + 0*(l-p) = p, and the variance is equal to p(l-p).
By the addition properties for independent random variables, the mean and variance of the binomial distribution are equal to the sum of the means and variances of the n independent Z variables, so

These definitions are intuitively logical. Imagine, for example 8 flips of a coin. If the coin is fair, then p = 0.5. One would expect the mean number of heads to be half the flips, or np = 8*0.5 = 4.
The variance is equal to np(l-p) = 8*0.5*0.5 = 2.
Question 6

One of the ad-hoc solutions to count to infinity problem in network routing is:

The split horizon hack
Flow based routing
Shortest path routing
       Computer-Networks       Routing
Question 6 Explanation: 
There are 2 possible solutions are:
1. Route Poison
2. The split horizon hack
Question 7

The normal curve is symmetrical about its:

Standard deviation
       Engineering-Mathematics       Probability
Question 7 Explanation: 
Symmetrical distribution occurs when the values of variables occur at regular frequencies and the mean, median and mode occur at the same point. In graph form, symmetrical distribution often appears as a bell curve. If a line were drawn dissecting the middle of the graph, it would show two sides that mirror each other.
The probability density of the normal distribution is

• μ is the mean or expectation of the distribution (and also its median and mode).
• σ is the standard deviation, and
• 2 is the variance
Question 8

Given a bit rate of b bits/sec, the time required to send 16 bits is:

16*b sec
16/b sec
16b sec
b16 sec
       Computer-Networks       Ethernet
Question 8 Explanation: 
The speed of the data is expressed in bits per second (bits/s or bps).
The data rate R is a function of the duration of the bit or bit time (TB).
R = 1/TB
Question 9

The density of uniform distribution over the interval -⍺ < a < b < ⍺ is given by:

f(x) = λe-λx , x>=0
f(x) = qkp
f(x) = 1/(b-a), a
f(x) = (⍺/c)x⍺-1
       Engineering-Mathematics       Probability
Question 9 Explanation: 
The probability density function of the continuous uniform distribution is:

The values of f(x) at the two boundaries a and b are usually unimportant because they do not alter the values of the integrals of f(X) dX over any interval, not of X f(X) dX or any higher moment. Sometimes they are chosen to be zero, and sometimes chosen to be 1/(b – a). The latter is appropriate in the context of estimation by the method of maximum likelihood. In the context of Fourier analysis, one may lake the value of f(a) to be 1|2(b – a), since then the inverse transform of may integral transform of this uniform function will yield back the function itself, rather than a function which equal ‘almost everywhere’, i.e except on a set of points with zero measure. Also, it is consistent with the sign function which has no such ambiguity.
In terms of mean and variance σ2, the probability density may be written as:
Question 10

The built in HTTP request method to request to read a web page is:

       Computer-Networks       Application-Layer-Protocol
Question 10 Explanation: 
Request Method:
The request method indicates the method to be performed on the resource identified by the given Request-URI. The method is case-sensitive and should always be mentioned in uppercase. The following table lists all the supported methods in HTTP/1.1.
Question 11

Exponential distribution is special case of ____ distribution.

       Engineering-Mathematics       Probability
Question 11 Explanation: 
The exponential distribution is not the same as the class of exponential families of distributions, which is a large class of probability distributions that includes the exponential distribution as one of its members, but also includes the normal distribution, binomial distribution, gamma distribution, Poisson, and many others.
Question 12

The traditional cryptographic cipher that records the letters but do not disguise them is:

Substitute cipher
One-time pads
Secret key algorithms
Transposition cipher
       Computer-Networks       Network-Security
Question 12 Explanation: 
A transposition cipher reorders the letters but does not disguise them. The key is a word or phrase not containing any repeated letters. Its purpose is to number the columns, column 1 being under the letter closest to the start of the alphabet, and so on.
Also this code can be easily broken.
Question 13
The algebraic sum of the deviations of all the variables from their mean i.e., is:
       Engineering-Mathematics       Probability
Question 13 Explanation: 
The sum of the deviations from the mean is zero. This will always be the case as it is a property of the sample mean, i.e., the sum of the deviations below the mean will always equal the sum of the deviations above the mean. However, the goal is to capture the magnitude of these deviations in a summary measure.
Question 14

The segmentation memory management scheme suffers from:

External fragmentation
Internal fragmentation
       Operating-Systems       Memory-Management
Question 14 Explanation: 
• Segmentation avoids internal fragmentation but still it suffers from external fragmentation.
Paging avoids external fragmentation but still it suffers from internal fragmentation. < br> • Internal fragmentation is the wasted space within each allocated block because of rounding up from the actual requested allocation to the allocation granularity.
• External fragmentation is the various free spaced holes that are generated in either your memory or disk space. External fragmented blocks are available for allocation, but may be too small to be of any use.
• Resource starvation is a problem encountered in concurrent computing where a process is perpetually denied necessary resources to process its work. Starvation may be caused by errors in a scheduling or mutual exclusion algorithm.
• Ageing is a scheduling technique used to avoid starvation.
Question 15

The mean, mode and median are connected by the empirical relationship:

Mean-mode = 2(mean-median)
Mean-mode = 3(mean-median)
Mean-mode = (mean-mode)/2
Mean-mode = (mean-mode)/3
       Engineering-Mathematics       Probability
Question 15 Explanation: 
A distribution in which the values of mean, median and mode coincide (i.e. mean = median = mode) is known as a symmetrical distribution. Conversely, when values of mean, median and mode are not equal the distribution is known as asymmetrical or skewed distribution. In moderately skewed or asymmetrical distribution a very important relationship exists among these three measures of central tendency. In such distributions the distance between the mean and median is about one-third of the distance between the mean and mode, as will be clear from the diagrams 1 and 2. Karl Pearson expressed this relationship as:
Mode = mean - 3 [mean - median]
Mode = 3 median - 2 mean
and Median = mode + ⅔ [mean-mode]
There are 15 questions to complete.

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