Python Institute PCED-30-02 Test Questions Fee | PCED-30-02 Original Questions

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Python Institute PCED-30-02 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Introduction to Data and Data Analysis Concepts	22.5%	- Data lifecycle and ethical considerations 1. Data collection, storage, processing, usage, and sharing 2. Privacy, security, bias, and fairness in data - Basic statistical concepts 1. Descriptive vs inferential statistics 2. Population, sample, variable, observation - Definition and classification of data 1. Difference between data, information, and knowledge 2. Role of data in decision-making and business 3. Process of turning raw data into insights - Data types and measurement scales 1. Qualitative vs quantitative data 2. Nominal, ordinal, interval, ratio scales
Topic 2: Data Visualization and Communication	20%	- Interpreting and presenting results 1. Deriving conclusions and insights 2. Reporting findings clearly and concisely - Principles of effective data visualization 1. Clarity, simplicity, and accuracy 2. Choosing appropriate chart types - Creating basic visualizations 1. Line charts, bar charts, histograms, pie charts 2. Using text and simple plotting tools
Topic 3: Working with Data and Performing Simple Analysis	25%	- Data cleaning and preparation 1. Handling missing values, duplicates, and errors 2. Filtering, sorting, transforming data 3. Formatting and standardizing values - Exploratory data analysis 1. Calculating mean, median, mode, range, variance, standard deviation 2. Identifying patterns, trends, and outliers - Data aggregation and grouping 1. Summarizing and grouping datasets - Data acquisition and loading 1. Importing data from external sources 2. Reading text, CSV, and structured files
Topic 4: Python Basics for Data Analysis	32.5%	- Core Python syntax and data types 1. Lists, tuples, sets, dictionaries 2. Variables, numbers, strings, booleans - Introduction to NumPy 1. Arrays, basic operations, indexing, slicing - Built-in modules for data work 1. math, statistics, datetime, collections, csv - Control flow and functions 1. Conditional statements, loops, iteration 2. Basic exception handling 3. Defining and calling functions, parameters, return values

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Python Institute PCED - Certified Entry-Level Data Analyst with Python Sample Questions (Q28-Q33):

NEW QUESTION # 28
A financial technology company is reviewing its data practices to ensure legal and ethical compliance.
Which of the following actions would support responsible data handling and align with regulations like GDPR, HIPAA, and CCPA? (Choose two.)

A. Limiting data access only to marketing staff to streamline personalization.
B. Retaining user data permanently unless users explicitly request deletion.
C. Automatically opting users into data sharing unless they manually change settings.
D. Implementing encryption for both stored and transmitted customer data.
E. Providing users with a clear explanation of how their data is used and obtaining explicit consent.

Answer: D,E

Explanation:
Encrypting stored and transmitted data protects sensitive information from unauthorized access and supports regulatory requirements for data security. Providing clear explanations of data usage and obtaining explicit consent ensures transparency and aligns with core principles of data protection regulations such as lawful processing and user rights.

NEW QUESTION # 29
You want to safely read the contents of a file named records.txtand store them in a list. Your requirements are:
- The file should be automatically closed after reading,
- The code should check whether the file exists before opening it, and
- It should handle missing file errors gracefully.
Which of the following is the most appropriate way to accomplish this task in Python? Select the best answer.

Answer: C

Explanation:
It uses a context manager to automatically close the file after reading and wraps the operation in a try-except block to gracefully handle a missing file error, assigning an empty list if the file is not found.

NEW QUESTION # 30
A script attempts to divide two numbers using / and //. The values are 7 and 2. The developer wants to understand the difference between true division and floor division. What are the results respectively?

A. 3.5 and 3
B. 4 and 3
C. 3 and 4
D. 3 and 3.5

Answer: A

Explanation:
The / operator performs true division, returning a float result of 3.5. The // operator performs floor division, truncating the decimal part and returning the integer value 3.

NEW QUESTION # 31
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 limits sampling to one demographic, ensuring high-quality data within a specific group.
B. It combines automated data collection with representative sampling, reducing the risk of bias.
C. It uses unstructured interviews to validate automated tracking, improving bias detection.
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 # 32
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
Then run this code:
import numpy as np
data = numpy.arr([10, 20, 30])
print(numpy.mean(data))
C. To install NumPy:
pip install numpy as np
Then run this code:
import np
data = np.array([10, 20, 30])
print(np.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 # 33
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