Free Senior Data Scientist Practice Questions

A.  

Box Plots

B.  

Pie Charts

C.  

Histogram

D.  

Scatter Chart

E.  

Bar Charts

A.  

Minimize the approximation error

B.  

Calculate the closest possible outcomes

C.  

Both A and B

D.  

None of the above

A.  

Data is massive

B.  

Data is not massive

C.  

Both A and B

D.  

None of the above

A.  

Supervised learning

B.  

Unsupervised learning

C.  

Reinforcement learning

D.  

All of the above

A.  

The "By" analysis technique reinforces the process of "thinking like a data scientist."

B.  

"By" analysis is a technique by which business subject matter experts (SMEs) and the Data Science team could collaborate to uncover new variables and metrics that might be better predictors of business performance.

C.  

"By" analysis is used to create a collaborative technique to drive alignment between the business users and the data scientists to identify and brainstorm variables and metrics that might be better predictors of business performance.

D.  

Both B and C

E.  

All of the above

A.  

NumPy

B.  

Scikit-learn

C.  

Matplotlib

D.  

SciPy

E.  

None of the above

A.  

The data lake can make both of the Business Intelligence and Data Science environments more agile and more productive

B.  

The data lake enables organizations to gather, manage, enrich, and analyze many new sources of data, whether structured or unstructured

C.  

The data lake enables organizations to treat data as an organizational asset to be gathered and nurtured versus a cost to be minimized

D.  

The data lake can make both of the Business Intelligence and Data Science environments less agile and more productive

E.  

None of the above

A.  

The central object in Pandas is called a DataFrame

B.  

DataFrame is similar to SQL tables or R data frames

C.  

A data frame is a table with rows and columns

D.  

Both A and B

E.  

All of the above

A.  

Apache claimed that Spark is able to run parallel jobs 100 times faster in memory and 10 times faster on disk in comparison to the traditional Hadoop MapReduce

B.  

Apache claimed that Spark is able to run parallel jobs 10 times faster in memory and 100 times faster on disk in comparison to the traditional Hadoop MapReduce

C.  

Apache claimed that Spark is able to run parallel jobs 1000 times faster in memory and 100 times faster on disk in comparison to the traditional Hadoop MapReduce

D.  

Apache claimed that Spark is able to run parallel jobs 50 times faster in memory and 5 times faster on disk in comparison to the traditional Hadoop MapReduce

A.  

Name quotas

B.  

Space quotas

C.  

Both A and B

D.  

None of the above