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Updated: Aug 25, 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 Price:$195 USD
Exam Format:Single-select questions, Multiple-select questions
Passing Score:75%
Available Languages:English, Japanese, Spanish, German, French
Certificate Validity Period:6 years
Related Certifications:PCEP – Certified Entry-Level Python Programmer
PCAP – Certified Associate in Python Programming
PCED – Certified Associate in Python Engineering
Real Exam Qty:48
Exam Duration:60 minutes
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%- SQL querying
  • 1. SELECT, WHERE, JOIN, GROUP BY, aggregate functions
  • 2. Subqueries and filtering
- Python-database connectivity
  • 1. Error handling and best practices
  • 2. Executing queries and retrieving results
  • 3. Connecting to SQLite, MySQL or PostgreSQL
- Relational database concepts
  • 1. Tables, keys, relationships and normalization
Data Acquisition and Preprocessing30%- Data cleaning and validation
  • 1. Handling missing, duplicate and invalid values
  • 2. Data standardization and transformation
  • 3. Quality assurance and validation techniques
- 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 Visualization and Communication15%- Data storytelling and reporting
  • 1. Structuring insights and conclusions
  • 2. Written and verbal presentation techniques
- Visualization principles and best practices
  • 1. Audience-focused presentation
  • 2. Choosing appropriate chart types
  • 3. Color, layout and clarity
- Visualization with Matplotlib and Seaborn
  • 1. Heatmaps, pair plots and correlation matrices
  • 2. Line, bar, scatter, histogram, box plots
  • 3. Customization and styling
Data Modeling and Machine Learning Basics20%- Supervised learning fundamentals
  • 1. Regression: linear, multiple, polynomial
  • 2. Classification: logistic regression, k-NN, decision trees
  • 3. Model training, testing and evaluation
- Model performance and optimization
  • 1. Hyperparameter tuning basics
  • 2. Accuracy, precision, recall, F1-score
  • 3. Overfitting, underfitting and generalization
Data Exploration and Statistical Analysis25%- Exploratory data analysis
  • 1. Feature selection and dimensionality reduction basics
  • 2. Identifying patterns, trends and outliers
- Inferential statistics
  • 1. Statistical significance and interpretation
  • 2. Hypothesis testing and confidence intervals
  • 3. Probability concepts and distributions
- Descriptive statistics
  • 1. Correlation and covariance analysis
  • 2. Frequency distributions and percentiles
  • 3. Measures of central tendency and dispersion

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

1. What is the purpose of using df.loc[2:5, 'column1'] in a Pandas DataFrame?

A) Returns rows 2 to 4 in 'column1'
B) Returns a new DataFrame excluding 'column1'
C) Returns the entire DataFrame
D) Returns rows 2 through 5 in 'column1'


2. What is a key advantage of using the Parquet file format over CSV in large-scale data pipelines?

A) Parquet supports schema evolution and columnar storage
B) Parquet offers row-based compression
C) Parquet stores data in plain text, making it human-readable
D) Parquet files can only be used with Excel


3. Which techniques are most effective when designing reusable and testable functions in Python?
(Choose two)

A) Creating large monolithic functions
B) Returning print statements instead of values
C) Avoiding external state or side effects
D) Use of default parameters


4. What is the main purpose of using the bootstrapping technique in inferential statistics?

A) To filter outliers from a dataset
B) To increase the number of features in a model
C) To estimate the sampling distribution by resampling with replacement
D) To validate the syntax of SQL queries in Python


5. What is a best practice when organizing data in a spreadsheet for analysis?

A) Merge cells to reduce redundancy
B) Place charts in the middle of data tables
C) Apply conditional formatting to entire rows only
D) Use consistent column headers across all sheets


Solutions:

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

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