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

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Python Institute PCED - Certified Entry-Level Data Analyst with Python Sample Questions (Q11-Q16):

NEW QUESTION # 11
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 # 12
A dictionary is defined as d = {"a":1, "b":2}. The programmer accesses d["c"]. What happens when this line executes?

A. Raises KeyError
B. Adds key automatically
C. Returns None
D. Returns 0

Answer: A

Explanation:
Accessing a non-existent key using square brackets raises a KeyError. Python does not automatically create missing keys, so the program will terminate unless the exception is handled.

NEW QUESTION # 13
You are writing a function to validate battery voltage readings. The rules are:
- If the reading is "MISSING"or None, return "Missing".
- If the value is below 2.5or above 4.2, return "Outlier".
Otherwise, return "Normal".
Which version correctly implements this logic using the most robust conditional and nested structures? Select the best answer.

Answer: C

Explanation:
It first handles the non-numeric cases ("MISSING" and None) in a single condition, preventing invalid comparisons, and then cleanly checks the numeric outlier bounds using an or condition (<
2.5 or > 4.2) before defaulting to "Normal".

NEW QUESTION # 14
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().strip()
B. cleaned_city = city.strip().capitalize()
C. cleaned_city = city.strip().title()
D. cleaned_city = city.upper().replace(" ", "")

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 # 15
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.arange(0, 181, 10)
target_temps = np.linspace(35.0, 37.0, 5)
B. timestamps = np.linspace(0, 181, 10)
target_temps = np.arange(35.0, 37.0, 0.5)
import numpy a3 np
C. timestamps = np.linspace(0, 180, 10)
target_temps = np.arange(35.0, 37.0, 5)
import numpy as np
D. timestamps = np.arange(0, 180, 10)
target_temps = np.linspace(35.0, 37.0, 4)
import numpy as np

Answer: A

Explanation:
np.arange(0, 181, 10) generates timestamps from 0 through 180 in 10-minute steps (end is excluded, so using 181 includes 180). np.linspace(35.0, 37.0, 5) generates five evenly spaced temperatures including both 35.0°C and 37.0°C.

NEW QUESTION # 16
......

The PCED - Certified Entry-Level Data Analyst with Python (PCED-30-02) certification is one of the hottest career advancement credentials in the modern Python Institute world. The PCED-30-02 certification can help you to demonstrate your expertise and knowledge level. With only one badge of PCED-30-02 certification, successful candidates can advance their careers and increase their earning potential. The Python Institute PCED-30-02 Certification Exam also enables you to stay updated and competitive in the market which will help you to gain more career opportunities.

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