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

2. A program compares two values using == and is. The variables a = 256 and b = 256 are defined.
What is the expected result of a is b in most Python implementations?

A) None
B) False
C) True
D) Error

3. Consider the following Python code:

What will be printed when the code above is executed?
40

A) [10, 20, 30, 40]
0
[1, 2, 3, 4, 5, 6]
50
B) [20, 30, 40]
1
[1, 2, 4, 5, 6]
C) [10, 20, 30, 40]
1
[1, 2, 4, 5, 6]
50
D) [20, 30, 40]
1
[1, 2, 4, 5, 6]
50

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

5. A government agency collects citizen data for various public services.
What is the primary reason for emphasizing careful data management throughout the entire data lifecycle in this context? Select the best answer.

A) To streamline the initial capture of citizen details and minimize the administrative burden on both staff and the public.
B) To enable seamless sharing of anonymized citizen information with other governmental bodies for collaborative initiatives.
C) To guarantee the long-term availability and integrity of crucial citizen datasets for future policy formulation and evaluation.
D) To uphold data quality for effective service delivery, safeguard citizen privacy and prevent misuse, and comply with data protection laws.

Solutions: