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Python Institute PCED-30-02 Exam Syllabus Topics:

Section	Weight	Objectives
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 - Simple Analytical Techniques 1. Identify basic patterns and trends in data 2. Calculate descriptive statistics (mean, median, mode) - Data Cleaning and Transformation 1. Handle missing and inconsistent data 2. Perform basic data transformation (filtering, sorting, grouping)
Python Basics for Data Analysis	32.5%	- 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 - Python Fundamentals 1. Use variables, data types, and basic operators 2. Define and use functions 3. Implement control flow structures (loops, conditionals)
Introduction to Data and Data Analysis Concepts	22.5%	- Data Ethics and Privacy 1. Understand basic data privacy concepts 2. Recognize ethical considerations in data handling - 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 Analysis Process and Workflow 1. Identify common data sources and collection methods 2. Explain the role of data cleaning and preparation 3. Describe the steps of the data analysis process
Communicating Insights and Reporting	12.5%	- Data Visualization 1. Select appropriate visuals for different data types 2. Recognize common visualization types (bar, line, pie charts) 3. Interpret simple data visualizations - 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

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Python Institute PCED - Certified Entry-Level Data Analyst with Python Sample Questions (Q29-Q34):

NEW QUESTION # 29
You are writing a function named process_data()to read and process numerical input from a file. The function must:
- read the file data.txt,
- attempt to convert the first line into an integer,
- handle file, conversion, or index-related exceptions,
- print the value only if no error occurs,
- and always print a final message after execution.
Which implementation correctly and robustly meets all these conditions? Select the best answer.

Answer: A

Explanation:
It wraps file reading and integer conversion in a try block, catches the relevant file, conversion, and indexing exceptions, prints the value only in the no-error path using an else block, and uses a finally block to always print the final message.

NEW QUESTION # 30
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. 0
B. 1
C. 2
D. 3

Answer: D

Explanation:
Python evaluates the addition first because of operator precedence. The expression 2 + 3 equals
5, then x *= 5 multiplies the current value of x (4) by 5, resulting in a final value of 20.

NEW QUESTION # 31
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 string
B. Syntax error
C. input returns float
D. input returns int

Answer: A

Explanation:
The input() function always returns a string. Attempting to add an integer to a string causes a TypeError. The input must be explicitly converted using int() or float() before performing arithmetic operations.

NEW QUESTION # 32
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 = np.unique(colors)
color_counts = Counter(set(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 = 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

Answer: C

Explanation:
numpy.unique returns the distinct values in the list, and taking its length gives the number of unique colors. Counter(colors) correctly counts how many times each color appears in the responses.

NEW QUESTION # 33
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().strip(', ')
B. fields = line.strip().split(', ')
C. fields = line.split(', ').trim()
D. fields = line.replace(', ', '|').split('|')

Answer: B

NEW QUESTION # 34
......

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