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Python Institute PCED-30-02 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Working with Data and Performing Simple Analyses	32.5%	- Data Analysis with Python Libraries 1. Utilize the collections module for specialized containers 2. Perform basic operations with NumPy arrays 3. Work with the datetime module for date/time data 4. Use the math and statistics modules for basic calculations - Data Cleaning and Transformation 1. Perform basic data transformation (filtering, sorting, grouping) 2. Handle missing and inconsistent data - Simple Analytical Techniques 1. Identify basic patterns and trends in data 2. Calculate descriptive statistics (mean, median, mode)
Topic 2: Communicating Insights and Reporting	12.5%	- Data Storytelling and Reporting 1. Present insights with visual and verbal techniques 2. Create clear and concise analytical reports 3. Structure insights as a narrative - Data Visualization 1. Interpret simple data visualizations 2. Recognize common visualization types (bar, line, pie charts) 3. Select appropriate visuals for different data types
Topic 3: Python Basics for Data Analysis	32.5%	- Python Fundamentals 1. Define and use functions 2. Use variables, data types, and basic operators 3. Implement control flow structures (loops, conditionals) - Data Structures 1. Apply common operations and methods to data structures 2. Work with lists, tuples, dictionaries, and sets - File Handling 1. Read from and write to files (text, CSV) 2. Use the csv module for basic CSV operations
Topic 4: Introduction to Data and Data Analysis Concepts	22.5%	- Define and Classify Data 1. Differentiate structured, semi-structured, and unstructured data 2. Classify data as quantitative or qualitative 3. Explain how data becomes meaningful - Data Ethics and Privacy 1. Understand basic data privacy concepts 2. Recognize ethical considerations in data handling - Data Analysis Process and Workflow 1. Describe the steps of the data analysis process 2. Identify common data sources and collection methods 3. Explain the role of data cleaning and preparation

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Free PDF 2026 Trustable Python Institute PCED-30-02: Certified PCED - Certified Entry-Level Data Analyst with Python Questions

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Python Institute PCED - Certified Entry-Level Data Analyst with Python Sample Questions (Q26-Q31):

NEW QUESTION # 26
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.split(', ').trim()
B. fields = line.replace(', ', '|').split('|')
C. fields = line.split().strip(', ')
D. fields = line.strip().split(', ')

Answer: D

Explanation:
Stripping removes leading and trailing whitespace from the line, and then splitting on the comma- space delimiter correctly separates the values into a list for further formatting.

NEW QUESTION # 27
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.upper().strip()
C. cleaned_city = city.strip().title()
D. cleaned_city = city.strip().capitalize()

Answer: C

Explanation:
Stripping removes leading and trailing whitespace, and applying title formatting capitalizes the first letter of each word while converting the remaining letters to lowercase, producing the correctly standardized city name.

NEW QUESTION # 28
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 limits sampling to one demographic, ensuring high-quality data within a specific group.
D. It relies on observational methods, which are more accurate than surveys or sensors.

Answer: B

Explanation:
Combining automated wearable data with a randomly selected survey sample integrates objective measurements with representative participant input, which helps reduce bias and improves the reliability and generalizability of the findings.

NEW QUESTION # 29
A script defines a variable x = None and checks its truth value using a conditional statement. The developer wants to understand how Python evaluates None in Boolean contexts. What will bool(x) return?

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

Answer: B

Explanation:
The value None represents the absence of a value and is treated as False in Boolean expressions. Therefore, calling bool(None) returns False, similar to empty containers or zero values.

NEW QUESTION # 30
You have collected sales data and want to compute the average using the NumPy library.
Which option correctly shows how to install NumPy and use it in your code to perform this task?

A. To install NumPy:
from pip install numpy
Then run this code:
import numpy as np
data = np.array([10, 20, 30])
print(np.mean(data))
B. To install NumPy:
pip install numpy as np
Then run this code:
import np
data = np.array([10, 20, 30])
print(np.mean(data))
C. To install NumPy:
pip install numpy
Then run this code:
import numpy as np
data = numpy.arr([10, 20, 30])
print(numpy.mean(data))

Answer: A

Explanation:
NumPy is installed with pip install numpy, then imported with import numpy as np. After that, you can create an array and compute the mean using np.array(...) and np.mean(...).

NEW QUESTION # 31
......

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