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

  • Exam Code: PCAD-31-02
  • Exam Name: Python Institute Certified Associate Data Analyst with Python (PCAD-31-02)
  • Updated: Sep 18, 2026     Q & A: 146 Questions and Answers

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Python Institute PCAD-31-02 Exam Syllabus Topics:

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

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

Question #1

Which refinements are typically used to enhance clarity and presentation quality in visualizations?
(Choose two)

  • A. Adding descriptive axis labels
  • B. Avoiding color entirely
  • C. Customizing tick labels
  • D. Disabling grid lines in all cases
Reveal Solution  Discussion  0

Correct Answer: A,C  🗳️

Question #2

Which of the following statements best describes the structural relationship between Pandas Series and DataFrames?

  • A. A DataFrame is a one-dimensional array of Series
  • B. A Series contains multiple DataFrames
  • C. A Series can only be created from a DataFrame
  • D. A DataFrame is a two-dimensional container of Series objects
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

Question #3

Which file formats are commonly used for hierarchical or nested data structures in data acquisition?
(Choose two)

  • A. TSV
  • B. CSV
  • C. JSON
  • D. XML
Reveal Solution  Discussion  0

Correct Answer: C,D  🗳️

Question #4

When merging two Pandas DataFrames using a common key, which parameters are essential to ensure accurate and conflict-free results?
(Choose two)

  • A. on
  • B. ignore_index
  • C. dropna
  • D. suffixes
Reveal Solution  Discussion  0

Correct Answer: A,D  🗳️

Question #5

When creating a line chart with Matplotlib, which function is most appropriate to add a text label at a specific data point?

  • A. plt.annotate()
  • B. plt.title()
  • C. plt.legend()
  • D. plt.xlabel()
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

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