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Updated: Sep 02, 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
Available Languages:German, French, English, Spanish, Japanese
Passing Score:75%
Exam Duration:60 minutes
Exam Format:Multiple-select questions, Single-select questions
Certificate Validity Period:6 years
Related Certifications:PCEP – Certified Entry-Level Python Programmer
PCED – Certified Associate in Python Engineering
PCAP – Certified Associate in Python Programming
Exam Price:$195 USD
Real Exam Qty:48
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
Topic 1: 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. Hypothesis testing and confidence intervals
  • 2. Statistical significance and interpretation
  • 3. Probability concepts and distributions
- Exploratory data analysis
  • 1. Identifying patterns, trends and outliers
  • 2. Feature selection and dimensionality reduction basics
Topic 2: Data Modeling and Machine Learning Basics20%- Model performance and optimization
  • 1. Accuracy, precision, recall, F1-score
  • 2. Hyperparameter tuning basics
  • 3. Overfitting, underfitting and generalization
- Supervised learning fundamentals
  • 1. Regression: linear, multiple, polynomial
  • 2. Classification: logistic regression, k-NN, decision trees
  • 3. Model training, testing and evaluation
Topic 3: Data Acquisition and Preprocessing30%- Data cleaning and validation
  • 1. Quality assurance and validation techniques
  • 2. Data standardization and transformation
  • 3. Handling missing, duplicate and invalid values
- Data collection, integration and storage
  • 1. Data collection methods and sources
  • 2. Data integration and merging
  • 3. Data formats and storage systems
- Data preparation with Pandas and NumPy
  • 1. Data structures: Series, DataFrame, ndarray
  • 2. Indexing, filtering, sorting and grouping
  • 3. Data reshaping and aggregation
Topic 4: 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. Color, layout and clarity
  • 2. Choosing appropriate chart types
  • 3. Audience-focused presentation
- Visualization with Matplotlib and Seaborn
  • 1. Heatmaps, pair plots and correlation matrices
  • 2. Customization and styling
  • 3. Line, bar, scatter, histogram, box plots
Topic 5: SQL and Database Integration10%- SQL querying
  • 1. Subqueries and filtering
  • 2. SELECT, WHERE, JOIN, GROUP BY, aggregate functions
- Python-database connectivity
  • 1. Error handling and best practices
  • 2. Connecting to SQLite, MySQL or PostgreSQL
  • 3. Executing queries and retrieving results
- Relational database concepts
  • 1. Tables, keys, relationships and normalization

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

Question 1

Which of the following statements correctly describe best practices when managing Python modules and packages in data analytics workflows?
(Choose two)

A. Always install packages system-wide for accessibility
B. Use PIP to install and uninstall third-party libraries as needed
C. Use virtual environments to isolate project dependencies
D. Regularly update all packages using pip update all


Question 2

Which of the following would best validate the uniqueness of a primary key in a Pandas DataFrame?

A. df['id'].duplicated().any()
B. df['id'].fillna()
C. df['id'].isnull().sum()
D. df['id'].nunique()


Question 3

What object-oriented principle allows a child class to reuse and extend the behavior of a parent class in a Python data modeling scenario?

A. Abstraction
B. Encapsulation
C. Inheritance
D. Composition


Question 4

Which method is best suited to transform inconsistent entries such as "N/A", "missing", and empty strings into a standard missing value representation in a Pandas DataFrame?

A. df.dropna()
B. df.replace()
C. df.rename()
D. df.sort_values()


Question 5

Which method is appropriate for combining two DataFrames based on a shared key column where non-matching rows should be excluded from the result?

A. append()
B. merge(..., how='inner')
C. concat()
D. join(..., how='outer')


Solutions:

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

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