Consider the following sample from a distribution that contains a continuous X and label Y that is
either A or B:

Which is the best cut point for X if you want to discretize these values into two buckets in a way
that minimizes the sum of chi-square values?

Consider the following sample from a distribution that contains a continuous X and label Y that is
either A or B:

Which is the best choice of cut points for X if you want to discretize these values into three buckets
that minimizes the sum of chi-square values?

You want to understand more about how users browse your public website. For example, you war
know which pages they visit prior to placing an order. You have a server farm of 200 web server
hosting your website. Which is the most efficient process to gather these web servers access logs
into your Hadoop cluster for analysis?

You have a large file of N records (one per line), and want to randomly sample 10% them. You
have two functions that are perfect random number generators (through they are a bit slow):
Random_uniform () generates a uniformly distributed number in the interval [0, 1]
random_permotation (M) generates a random permutation of the number O through M -1.
Below are three different functions that implement the sampling.
Method A
For line in file:
If random_uniform () < 0.1;
Print line
Method B
i = 0
for line in file:
if i % 10 = = 0;
print line
i += 1
Method C
idxs = random_permotation (N) [: (N/10)]
i = 0
for line in file:
if i in idxs:
print line
i +=1

Which method will have the best runtime performance?

You have a large file of N records (one per line), and want to randomly sample 10% them. You
have two functions that are perfect random number generators (through they are a bit slow):
Random_uniform () generates a uniformly distributed number in the interval [0, 1]
random_permotation (M) generates a random permutation of the number O through M -1.
Below are three different functions that implement the sampling.
Method A
For line in file:
If random_uniform () < 0.1;
Print line
Method B
i = 0
for line in file:
if i % 10 = = 0;
print line
i += 1
Method C
idxs = random_permotation (N) [: (N/10)]
i = 0

for line in file:
if i in idxs:
print line
i +=1
Which method requires the most RAM?

You have a large file of N records (one per line), and want to randomly sample 10% them. You
have two functions that are perfect random number generators (through they are a bit slow):
Random_uniform () generates a uniformly distributed number in the interval [0, 1]
random_permotation (M) generates a random permutation of the number O through M -1.
Below are three different functions that implement the sampling.
Method A
For line in file:
If random_uniform () < 0.1;
Print line
Method B
i = 0
for line in file:
if i % 10 = = 0;
print line

i += 1
Method C
idxs = random_permotation (N) [: (N/10)]
i = 0
for line in file:
if i in idxs:
print line
i +=1
Which method might introduce unexpected correlations?

You have a large file of N records (one per line), and want to randomly sample 10% them. You
have two functions that are perfect random number generators (through they are a bit slow):
Random_uniform () generates a uniformly distributed number in the interval [0, 1]
random_permotation (M) generates a random permutation of the number O through M -1.
Below are three different functions that implement the sampling.
Method A
For line in file:
If random_uniform () < 0.1;
Print line
Method B

i = 0
for line in file:
if i % 10 = = 0;
print line
i += 1
Method C
idxs = random_permotation (N) [: (N/10)]
i = 0
for line in file:
if i in idxs:
print line
i +=1
Which method is least likely to give you exactly 10% of your data?

Assuming the trends shown in this chart continue, what would we expect the value of the revenue to be in Q1 of 2013?

From historical data, you know that 50% of students who take Cloudera’s Introduction to Data
Science: Building Recommenders Systems training course pass this exam, while only 25% of
students who did not take the training course pass this exam. You also know that 50% of this
exam’s candidates also take Cloudera’s Introduction to Data Science: Building Recommendations
Systems training course.
If we know that a person has passed this exam, what is the probability that they took cloudera’s
introduction to Data Science: Building Recommender Systems training course?

From historical data, you know that 50% of students who take Cloudera’s Introduction to Data
Science: Building Recommenders Systems training course pass this exam, while only 25% of
students who did not take the training course pass this exam. You also know that 50% of this
exam’s candidates also take Cloudera’s Introduction to Data Science: Building Recommendations
Systems training course.
What is the probability that any individual exam candidate will pass the data science exam?