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Python Institute PCED - Certified Entry-Level Data Analyst with Python Sample Questions:

1. The following chart shows how a student spends 24 hours in a day:

Which statements are most accurate? (Choose two.)

A) Leisure accounts for a moderate portion of the student's daily schedule, larger than commuting but smaller than the time spent at school.
B) Leisure and meals combined equal the time spent at school.
C) School and homework together take up half of the day's time.
D) Commuting is the second most time-consuming activity.
E) Sleep and school take up the majority of time in a day.
F) More time is spent commuting than on leisure.

2. You are developing a temperature control module for a laboratory incubator. Your objectives are to:
- generate timestamps every 10 minutes over a 3-hour span (i.e., 0 to 180 minutes), and
- simulate five evenly spaced target temperatures between 35.0°C and 37.0°C for system calibration.
Which code snippet correctly produces both sequences using NumPy? Select the best answer.
import numpy as np

A) timestamps = np.arange(0, 180, 10)
target_temps = np.linspace(35.0, 37.0, 4)
import numpy as np
B) timestamps = np.arange(0, 181, 10)
target_temps = np.linspace(35.0, 37.0, 5)
C) timestamps = np.linspace(0, 180, 10)
target_temps = np.arange(35.0, 37.0, 5)
import numpy as np
D) timestamps = np.linspace(0, 181, 10)
target_temps = np.arange(35.0, 37.0, 0.5)
import numpy a3 np

3. You are analyzing survey results from students about their favorite colors. The list colorsstores individual responses:

You want to:
- find the number of unique colors mentioned using NumPy, and
- determine how often each color was chosen using Counter.
Which code snippet correctly performs both tasks? Select the best answer.
from numpy import unique

A) from collections import Counter
unique_colors = Counter(colors)
color_counts = sum(np.unique(colors))
import numpy as np
B) from collections import Counter
unique_colors = len(set(colors))
color_counts = np.unique(colors)
C) from collections import Counter
unique_colors = len(unique(colors))
color_counts = Counter(colors)
import numpy as np
D) from collections import Counter
unique_colors = np.unique(colors)
color_counts = Counter(set(colors))
import numpy as np

4. A teacher wants to compare the number of students in different school clubs:

Which type of chart would be most appropriate to show this comparison? Select the best answer.

A) Pie chart
B) Line chart
C) Bubble chart
D) Bar chart

5. What will be the output of the following code?

A) 10 5
B) 10 10
C) 5 10
D) 5 5

Solutions: