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Python Institute PCED-30-02 Exam Syllabus Topics:

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

1. 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) Bar chart
C) Bubble chart
D) Line chart

2. A student writes a program using input() to collect user data, then tries to add 5 to the input value without conversion. The program crashes. What is the most likely cause of this behavior?

A) input returns int
B) input returns float
C) Syntax error
D) input returns string

3. 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().title()
C) cleaned_city = city.strip().capitalize()
D) cleaned_city = city.upper().strip()

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

5. A data analyst exports a cleaned dataset from Python and wants to share it with a colleague for easy use in Excel.
Which file format is best suited for this purpose? Select the best answer.

A) SQL, because it directly converts tabular data into database tables that Excel can open.
B) JSON, because it stores hierarchical data and is designed for visual presentation in Excel.
C) XML, because it automatically preserves spreadsheet formatting and charts.
D) CSV, because it stores tabular data in plain text and is compatible with spreadsheet applications.

Solutions: