Try Python Institute : PCAD-31-02 valid & accurate questions and answers

Updated: Sep 03, 2026

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Python Institute PCAD-31-02 Exam Overview:

Certification Vendor:Python Institute
Exam Name:Python Institute Certified Associate Data Analyst with Python
Exam Number:PCAD-31-02
Exam Format:Single-select questions, Multiple-select questions
Real Exam Qty:48
Passing Score:75%
Available Languages:English, German, Spanish, French, Japanese
Exam Duration:60 minutes
Certificate Validity Period:6 years
Exam Price:$195 USD
Related Certifications:PCAP – Certified Associate in Python Programming
PCED – Certified Associate in Python Engineering
PCEP – Certified Entry-Level Python Programmer
Recommended Training:PCAD Official Syllabus and Resources
Edube Interactive Learning Platform
Exam Registration:Python Institute Official Registration
Sample Questions:Python Institute PCAD-31-02 Sample Questions
Exam Way:Online proctored or onsite at authorized test centers
Pre Condition:No mandatory prerequisites; recommended: PCAP – Certified Associate in Python Programming or equivalent knowledge, basic understanding of statistics and data concepts
Official Syllabus URL:https://pythoninstitute.org/pcad

Python Institute PCAD-31-02 Exam Syllabus Topics:

SectionWeightObjectives
SQL and Database Integration10%- Python-database connectivity
  • 1. Connecting to SQLite, MySQL or PostgreSQL
  • 2. Error handling and best practices
  • 3. Executing queries and retrieving results
- Relational database concepts
  • 1. Tables, keys, relationships and normalization
- SQL querying
  • 1. Subqueries and filtering
  • 2. SELECT, WHERE, JOIN, GROUP BY, aggregate functions
Data Exploration and Statistical Analysis25%- Descriptive statistics
  • 1. Frequency distributions and percentiles
  • 2. Measures of central tendency and dispersion
  • 3. Correlation and covariance analysis
- Inferential statistics
  • 1. Probability concepts and distributions
  • 2. Statistical significance and interpretation
  • 3. Hypothesis testing and confidence intervals
- Exploratory data analysis
  • 1. Feature selection and dimensionality reduction basics
  • 2. Identifying patterns, trends and outliers
Data Visualization and Communication15%- Data storytelling and reporting
  • 1. Written and verbal presentation techniques
  • 2. Structuring insights and conclusions
- Visualization principles and best practices
  • 1. Audience-focused presentation
  • 2. Color, layout and clarity
  • 3. Choosing appropriate chart types
- Visualization with Matplotlib and Seaborn
  • 1. Line, bar, scatter, histogram, box plots
  • 2. Customization and styling
  • 3. Heatmaps, pair plots and correlation matrices
Data Modeling and Machine Learning Basics20%- Model performance and optimization
  • 1. Hyperparameter tuning basics
  • 2. Accuracy, precision, recall, F1-score
  • 3. Overfitting, underfitting and generalization
- Supervised learning fundamentals
  • 1. Model training, testing and evaluation
  • 2. Classification: logistic regression, k-NN, decision trees
  • 3. Regression: linear, multiple, polynomial
Data Acquisition and Preprocessing30%- Data preparation with Pandas and NumPy
  • 1. Data reshaping and aggregation
  • 2. Indexing, filtering, sorting and grouping
  • 3. Data structures: Series, DataFrame, ndarray
- Data collection, integration and storage
  • 1. Data collection methods and sources
  • 2. Data formats and storage systems
  • 3. Data integration and merging
- Data cleaning and validation
  • 1. Quality assurance and validation techniques
  • 2. Handling missing, duplicate and invalid values
  • 3. Data standardization and transformation

Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions:

Question 1

Which actions are valid techniques for handling erroneous categorical values in a dataset?
(Choose two)

A. Replacing inconsistent labels with a standardized value
B. Converting all values to integers
C. Normalizing using min-max scaling
D. Removing rows with invalid labels


Question 2

When analyzing a sales dataset using Pandas, which of the following techniques help extract key insights by grouping and summarizing the data?
(Choose two)

A. Using rename() to change column headers
B. Applying agg() to calculate multiple metrics like mean and max
C. Using fillna() to replace missing product prices
D. Using groupby() to compute total sales per region


Question 3

Which method is typically used in Pandas to check if a column contains only values within an expected range?

A. df.sort_values()
B. df.sample()
C. df.clip()
D. df.isin()


Question 4

Which of the following practices contributes most to making Python scripts modular and maintainable?

A. Hard-coding file paths in every function
B. Writing all logic in the global scope
C. Structuring code with reusable functions and modules
D. Using global variables freely


Question 5

Which of the following improves the readability of a spreadsheet used for collaborative data analysis?

A. Adding freeze panes to lock header rows
B. Using colored fonts to highlight every cell
C. Converting numeric values into images
D. Removing gridlines from all sheets


Solutions:

Question 1
Answer: A,D
Question 2
Answer: B,D
Question 3
Answer: D
Question 4
Answer: C
Question 5
Answer: A

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