Python Institute PCAD-31-02 Q&A - in .pdf

  • PCAD-31-02 pdf
  • Exam Code: PCAD-31-02
  • Exam Name: Python Institute Certified Associate Data Analyst with Python (PCAD-31-02)
  • Updated: Aug 24, 2026
  • Q & A: 146 Questions and Answers
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  • Exam Code: PCAD-31-02
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Python Institute PCAD-31-02 Exam Syllabus Topics:

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

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

1. What is the correct SQL statement to permanently remove a table named customers from a database?

A) DELETE TABLE customers;
B) TRUNCATE TABLE customers;
C) REMOVE TABLE customers;
D) DROP TABLE customers;


2. Which characteristics are typically associated with unstructured data formats?
(Choose two)

A) Stored using fixed-width fields and records
B) Highly tabular and query-friendly
C) Requires parsing or transformation before analysis
D) Lacks predefined data schema


3. What is the benefit of using the __name__ == "__main__" construct in Python scripts?

A) It defines the main function in Python
B) It allows access to all global variables
C) It prevents modules from being imported
D) It allows selective execution of code when run as a script


4. You have a list of product prices, and you need to remove duplicate values while maintaining the fastest performance. Which core data structure should you use?

A) dictionary
B) tuple
C) list
D) set


5. Which Python library is most commonly used to establish a connection to a SQLite database and perform SQL operations?

A) sqlalchemy
B) sqlite3
C) pymysql
D) psycopg2


Solutions:

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

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