Data Interpretation Questions for Competitive Exams

Data Interpretation: Data Interpretation Questions are usually asked in  all competitive exams like SSC, SBI, IBPS, RBI, LIC, RRB, AAI, DRDO, ISRO, NTR, FSSAI, CWC, LIC, SSC CGL, Railways and other state government exams. 
Data Interpretation questions require clear observation of charts like pie chart, bar chart, radar chart, line chart, table chart. On this page, we are providing all varieties of Data Interpretation Questions along with previous year Data Interpretation Questions.
Data Interpretation Questions are usually based on basic knowledge of arithmetic & fast calculation which includes good command on multiplication, division, addition, subtraction along with Vedic Math. 
Data Interpretation Questions is a very important section of Quantitative Aptitude. Most of the questions come from Data Interpretation in Quantitative Aptitude section.

Set-71
Study the given bar-chart carefully and answer the following questions.
The graph shows the number of villages in four different states where Solar power was done in different years.

1. The number of villages in Jaipur where solar power was done in 2002 is what percentage of the number of villages in Delhi where solar power was done in 2003?

Ans:5
Required percentage = 20/60 × 100
= 2000/60
= 33.3%

2. What is the ratio of the villages in Bangaluru to those in Chennai where solar power was done in 2002?

Ans:4
Number of villages in Bangaluru where Solar power was done in 2002 = 40
Number of villages in Chennai where Solar power was done in 2002 = 50
So, required ratio = 4: 5

3. In which state was the Solar power work done in maximum villages during the given three years?

Ans:3
In Bangaluru, the number of villages where Solar power was done = 30 + 40 + 30 = 100
In Chennai = 40 + 50 + 60 = 150
In Delhi = 40 + 50 + 60 = 150
In Jaipur = 40 + 20 + 50 = 110
So, maximum Solar power was in both Delhi and Chennai

4. If the cost of Solar power of a village is Rs.75 lakh then what is the cost of Solar power in four states during the given period?

Ans:2
Total number of villages in four states where Solar power was done = 100 + 150 + 150 + 110 = 510
So, cost of Solar power = 7500000 × 510 = Rs.3825000000

5. In which year was the Solar power work done in maximum number of villages?

Ans:1
Number of villages where solar power was done in 2001 = 50 + 30 + 60 + 40
= 180
Number of villages where solar power was done in 2002 = 20 + 40 + 50 + 50
= 160
Number of villages where solar power was done in 2003 = 40 + 30 + 40 + 60
= 170
In 2001 maximum Solar power work was done.

Set-72
Read the following graph carefully and answer the questions below.
Number of employee working in three different companies in 6 different years (in thousands)

6. The number of employees in company B in 2011 is what percent more than the number of employees in company C in 2012(approx.)

Ans:4

No. of employees in company B in 2011= 30 thousand
No. of employees in company B in 2010= 26 thousand
Difference = 30 – 26 = 4
Percent = 4/26 * 100 = 15% (approx.)

7. In which year the total number of employees in all the companies is Maximum?

Ans:4
From the table it is clear that in year 2014 total number of employees in all the companies is Maximum.

8. What is the difference between total number of employees in company B and C in all the years together?

Ans:1
Total no. of employees in company B= 177 thousand
Total no. of employees in company C= 172 thousand
Difference= 177 – 172 = 5 thousand

9. The number of employees in company A in 2012 is approximately what percentage of the number of employees in the same company in 2014?

Ans:2
Total no. of employees in company A in 2012= 27
Total no. of employees in company A in 2014= 31
Percent = 27/31 * 100 = 87% (approx.)

10. What is the respective ratio between the average number of employees in company A and average number of employees in company B in all the years together?

Ans:3
Average number of employees in company A= 166/6
Average number of employees in company 6= 177/6
Ratio = 166/6: 177/6
= 166: 177

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8 thoughts on “Data Interpretation For Prelims”

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