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

PCAD-31-02 real exams

Exam Code: PCAD-31-02

Exam Name: Python Institute Certified Associate Data Analyst with Python (PCAD-31-02)

Updated: Aug 02, 2026

Q & A: 146 Questions and Answers

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Python Institute PCAD-31-02 Exam Syllabus Topics:

SectionWeightObjectives
SQL and Database Integration10%- Relational database concepts
  • 1. Tables, keys, relationships and normalization
- 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
Data Modeling and Machine Learning Basics20%- Model performance and optimization
  • 1. Hyperparameter tuning basics
  • 2. Overfitting, underfitting and generalization
  • 3. Accuracy, precision, recall, F1-score
- Supervised learning fundamentals
  • 1. Regression: linear, multiple, polynomial
  • 2. Classification: logistic regression, k-NN, decision trees
  • 3. Model training, testing and evaluation
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 integration and merging
  • 2. Data formats and storage systems
  • 3. Data collection methods and sources
- Data preparation with Pandas and NumPy
  • 1. Indexing, filtering, sorting and grouping
  • 2. Data reshaping and aggregation
  • 3. Data structures: Series, DataFrame, ndarray
Data Visualization and Communication15%- Visualization with Matplotlib and Seaborn
  • 1. Heatmaps, pair plots and correlation matrices
  • 2. Customization and styling
  • 3. Line, bar, scatter, histogram, box plots
- Visualization principles and best practices
  • 1. Color, layout and clarity
  • 2. Choosing appropriate chart types
  • 3. Audience-focused presentation
- Data storytelling and reporting
  • 1. Structuring insights and conclusions
  • 2. Written and verbal presentation techniques
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. Probability concepts and distributions
  • 3. Hypothesis testing and confidence intervals
- 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. Which SQL commands are typically used to retrieve and manipulate data in a data analysis context?
(Choose all that apply)
Response:

A) INSERT
B) UPDATE
C) SELECT
D) ROLLBACK


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

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


3. Which data collection method is most appropriate when gathering opinions from a target audience at scale?

A) Sensor logs
B) Transactional data
C) Surveys
D) Web scraping


4. What may occur if a model is evaluated using the same data it was trained on?

A) Data leakage
B) Underfitting
C) Overgeneralization
D) Overfitting


5. Which type of regression is most appropriate when the response variable is categorical, such as predicting customer churn (Yes/No)?

A) Logistic regression
B) Linear regression
C) Decision tree regression
D) Polynomial regression


Solutions:

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

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