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

1. You are working with city names entered by users. These names may contain inconsistent capitalization and unwanted spaces.
To standardize the data, you want to:
- Remove any leading or trailing whitespace, and
- Capitalize the first letter of each word (e.g., convert "new york" to "New York").
For example:
" New york " → "New York"
"lOS ANGELES" → "Los Angeles"
You are given a variable citythat contains the raw input.
Which line of code correctly updates the value of cleaned_cityto apply the required transformation? Select the best answer.

A) cleaned_city = city.upper().replace(" ", "")
B) cleaned_city = city.strip().capitalize()
C) cleaned_city = city.strip().title()
D) cleaned_city = city.upper().strip()

2. A script attempts to divide two numbers using / and //. The values are 7 and 2. The developer wants to understand the difference between true division and floor division. What are the results respectively?

A) 3.5 and 3
B) 3 and 4
C) 3 and 3.5
D) 4 and 3

3. You are given a short Python script that uses both arithmetic and assignment operators. The variable x is initialized as 4, then updated using x *= 2 + 3. What will be printed when the final value of x is displayed?

A) 24
B) 14
C) 20
D) 10

4. You are reviewing a report that compares the number of weekly client meetings and project completion rates across teams. The table below summarizes the data for each team:

Which statement best describes the data shown above? Select the best answer.

A) "Teams with 5-6 weekly meetings tended to complete more projects than those with very high or very low meeting counts."
B) "Project completion numbers were nearly identical across all teams, averaging 3.25 per month."
C) The more meetings a team had, the more projects they finished, especially in high-performing teams like Omega and Delta."
D) "Gamma and Omega were outliers that inflated the meeting average, but most teams finished about 3 projects per month."

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

Solutions: