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

PCED-30-02 real exams

Exam Code: PCED-30-02

Exam Name: PCED - Certified Entry-Level Data Analyst with Python

Updated: Sep 15, 2026

Q & A: 52 Questions and Answers

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Python Institute PCED-30-02 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Data Visualization and Communication20%- Interpreting and presenting results
  • 1. Reporting findings clearly and concisely
  • 2. Deriving conclusions and insights
- Principles of effective data visualization
  • 1. Clarity, simplicity, and accuracy
  • 2. Choosing appropriate chart types
- Creating basic visualizations
  • 1. Using text and simple plotting tools
  • 2. Line charts, bar charts, histograms, pie charts
Topic 2: Introduction to Data and Data Analysis Concepts22.5%- Data lifecycle and ethical considerations
  • 1. Data collection, storage, processing, usage, and sharing
  • 2. Privacy, security, bias, and fairness in data
- Definition and classification of data
  • 1. Difference between data, information, and knowledge
  • 2. Process of turning raw data into insights
  • 3. Role of data in decision-making and business
- Basic statistical concepts
  • 1. Descriptive vs inferential statistics
  • 2. Population, sample, variable, observation
- Data types and measurement scales
  • 1. Qualitative vs quantitative data
  • 2. Nominal, ordinal, interval, ratio scales
Topic 3: Python Basics for Data Analysis32.5%- Built-in modules for data work
  • 1. math, statistics, datetime, collections, csv
- Control flow and functions
  • 1. Basic exception handling
  • 2. Defining and calling functions, parameters, return values
  • 3. Conditional statements, loops, iteration
- Introduction to NumPy
  • 1. Arrays, basic operations, indexing, slicing
- Core Python syntax and data types
  • 1. Variables, numbers, strings, booleans
  • 2. Lists, tuples, sets, dictionaries
Topic 4: Working with Data and Performing Simple Analysis25%- Data cleaning and preparation
  • 1. Handling missing values, duplicates, and errors
  • 2. Filtering, sorting, transforming data
  • 3. Formatting and standardizing values
- Data aggregation and grouping
  • 1. Summarizing and grouping datasets
- Exploratory data analysis
  • 1. Calculating mean, median, mode, range, variance, standard deviation
  • 2. Identifying patterns, trends, and outliers
- Data acquisition and loading
  • 1. Reading text, CSV, and structured files
  • 2. Importing data from external sources

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

Question #1

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.

  • A.
  • B.
  • C.
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

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Question #2

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 and 4
  • B. 4 and 3
  • C. 3 and 3.5
  • D. 3.5 and 3
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

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Question #3

A Python developer is testing truth values of different data types using the bool() function. The expression bool([0]) is evaluated in the script. What result should be expected when this expression is printed?

  • A. None
  • B. Error
  • C. False
  • D. True
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

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Question #4

Which of the following code snippets will output Truefor both printfunctions by correctly identifying the type of each variable and performing valid operations?

  • A.
  • B.
  • C.
  • D.
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

Explanation: Only visible for BootcampPDF members. You can sign-up / login (it's free).

Question #5

Which task is traditionally or typically performed by a data analyst, and not by a data scientist or analytics specialist? Select the best answer.

  • A. Designing a data pipeline to ingest and process real-time sensor data.
  • B. Recommending pricing strategies using predictive modeling and A/B testing.
  • C. Developing machine learning algorithms to predict user behavior.
  • D. Creating summary tables and charts to describe patterns in historical data.
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

Explanation: Only visible for BootcampPDF members. You can sign-up / login (it's free).

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