Python Institute PCED-30-02 Actual Tests : PCED - Certified Entry-Level Data Analyst with Python

Python Institute PCED-30-02 Exam Syllabus Topics:

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

1. You are reading a data.csvfile line by line. To prepare each line for formatting with f-strings, you need to remove extra whitespace and split the values by commas.
Which line should you insert to correctly clean and parse the input?

A) fields = line.replace(', ', '|').split('|')
B) fields = line.split(', ').trim()
C) fields = line.strip().split(', ')
D) fields = line.split().strip(', ')

2. A health researcher uses wearable devices to record physical activity and sends a survey to randomly selected participants across age groups.
Why can this approach be effective? Select the best answer.

A) It uses unstructured interviews to validate automated tracking, improving bias detection.
B) It combines automated data collection with representative sampling, reducing the risk of bias.
C) It relies on observational methods, which are more accurate than surveys or sensors.
D) It limits sampling to one demographic, ensuring high-quality data within a specific group.

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

4. How do the analysis and visualization stages of the data lifecycle typically work together?

A) Analysis produces charts and graphs, while visualization interprets the data to detect outliers.
B) Analysis summarizes archived data, and visualization transforms it into structured tables for storage.
C) Analysis identifies patterns and insights in processed data, which are then communicated through visualizations for informed decision-making.
D) Analysis retrieves raw inputs from dashboards, while visualization uses that input to clean the data.

5. An e-commerce site records every product view and purchase.
Which of the following best describes how these logs become marketing insights?

A) Raw event logs are structured, analyzed, and interpreted into information - like conversion rates by product - which then builds knowledge of customer preferences.
B) Raw event logs are validated, organized, and compiled into datasets - like timestamped user actions - which then support system debugging.
C) Raw event logs are stored, archived, and indexed - like by user ID and session - which then facilitate audit trails.
D) Raw event logs are filtered, aggregated, and formatted into summaries - like total visits per category - which then provide traffic reports.

Solutions: