Types of PCED-30-02 Exam Practice Test Questions

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Python Institute PCED-30-02 Exam Syllabus Topics:

SectionWeightObjectives
Data Visualization and Communication20%- Creating basic visualizations
  • 1. Line charts, bar charts, histograms, pie charts
  • 2. Using text and simple plotting tools
- 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
Python Basics for Data Analysis32.5%- Built-in modules for data work
  • 1. math, statistics, datetime, collections, csv
- 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
- Control flow and functions
  • 1. Conditional statements, loops, iteration
  • 2. Basic exception handling
  • 3. Defining and calling functions, parameters, return values
Introduction to Data and Data Analysis Concepts22.5%- Definition and classification of data
  • 1. Process of turning raw data into insights
  • 2. Difference between data, information, and knowledge
  • 3. Role of data in decision-making and business
- Data lifecycle and ethical considerations
  • 1. Privacy, security, bias, and fairness in data
  • 2. Data collection, storage, processing, usage, and sharing
- 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
Working with Data and Performing Simple Analysis25%- Data aggregation and grouping
  • 1. Summarizing and grouping datasets
- Data acquisition and loading
  • 1. Reading text, CSV, and structured files
  • 2. Importing data from external sources
- Data cleaning and preparation
  • 1. Filtering, sorting, transforming data
  • 2. Handling missing values, duplicates, and errors
  • 3. Formatting and standardizing values
- Exploratory data analysis
  • 1. Calculating mean, median, mode, range, variance, standard deviation
  • 2. Identifying patterns, trends, and outliers

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Python Institute PCED - Certified Entry-Level Data Analyst with Python Sample Questions (Q47-Q52):

NEW QUESTION # 47
An online retailer collects customer reviews, order transaction logs, and product ratings.
Which of the following correctly classifies these types of data? Select the best answer.

Answer: C

Explanation:
Product ratings are numeric values stored in fixed fields, making them quantitative and structured.
Customer reviews are free-text responses, which are qualitative and unstructured. Transaction logs consist of organized records with defined fields such as order ID, date, and amount, making them structured and quantitative.


NEW QUESTION # 48
A Python loop uses range(5, 0, -2) to iterate backward. The programmer expects a decreasing sequence. Which values will actually be generated when this loop runs?

Answer: A

Explanation:
The range starts at 5 and decreases by 2 each step until it reaches a value greater than 0. The generated sequence is 5, 3, and 1.


NEW QUESTION # 49
Which of the following code snippets will output Truefor both printfunctions by correctly identifying the type of each variable and performing valid operations?

Answer: C

Explanation:
Adding an integer and a float produces a float result, so checking that the type of the sum is float evaluates to True. The variable holding 3.0 is a float, so the isinstance check for float also evaluates to True.


NEW QUESTION # 50
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?

Answer: D

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 # 51
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

Answer: B

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 # 52
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