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 17, 2026
  • Q & A: 146 Questions and Answers

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PCAD-31-02 exam dumps

Python Institute PCAD-31-02 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Data Exploration and Statistical Analysis25%- Exploratory data analysis
  • 1. Feature selection and dimensionality reduction basics
  • 2. Identifying patterns, trends and outliers
- 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. Probability concepts and distributions
  • 3. Statistical significance and interpretation
Topic 2: Data Modeling and Machine Learning Basics20%- Supervised learning fundamentals
  • 1. Model training, testing and evaluation
  • 2. Classification: logistic regression, k-NN, decision trees
  • 3. Regression: linear, multiple, polynomial
- Model performance and optimization
  • 1. Accuracy, precision, recall, F1-score
  • 2. Overfitting, underfitting and generalization
  • 3. Hyperparameter tuning basics
Topic 3: SQL and Database Integration10%- Relational database concepts
  • 1. Tables, keys, relationships and normalization
- Python-database connectivity
  • 1. Connecting to SQLite, MySQL or PostgreSQL
  • 2. Executing queries and retrieving results
  • 3. Error handling and best practices
- SQL querying
  • 1. SELECT, WHERE, JOIN, GROUP BY, aggregate functions
  • 2. Subqueries and filtering
Topic 4: Data Acquisition and Preprocessing30%- Data cleaning and validation
  • 1. Handling missing, duplicate and invalid values
  • 2. Quality assurance and validation techniques
  • 3. Data standardization and transformation
- Data preparation with Pandas and NumPy
  • 1. Indexing, filtering, sorting and grouping
  • 2. Data reshaping and aggregation
  • 3. Data structures: Series, DataFrame, ndarray
- Data collection, integration and storage
  • 1. Data formats and storage systems
  • 2. Data collection methods and sources
  • 3. Data integration and merging
Topic 5: 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. Audience-focused presentation
  • 2. Color, layout and clarity
  • 3. Choosing appropriate chart types

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

Question #1

Which methods can be used to validate data types and structure in a Pandas DataFrame?
(Choose two)

  • A. df.to_csv()
  • B. df.dtypes
  • C. df.memory_usage()
  • D. df.info()
Reveal Solution  Discussion  0

Correct Answer: B,D  🗳️

Question #2

Which approaches are commonly used to extract structured data from APIs in Python?
(Choose two)

  • A. Open Excel files using matplotlib
  • B. Use the requests module to retrieve JSON responses
  • C. Load CSV directly with re.compile()
  • D. Access data using requests.get() and parse it with .json()
Reveal Solution  Discussion  0

Correct Answer: B,D  🗳️

Question #3

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

  • A. TRUNCATE TABLE customers;
  • B. DELETE TABLE customers;
  • C. REMOVE TABLE customers;
  • D. DROP TABLE customers;
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

Question #4

When joining two tables in a relational database using SQL, which clause is used to specify the relationship between their columns?

  • A. GROUP BY
  • B. HAVING
  • C. JOIN ON
  • D. FROM
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

Question #5

Which features are commonly supported by both Matplotlib and Seaborn for customizing visualizations?
(Choose two)

  • A. Changing plot color palettes
  • B. Automatic plot animations
  • C. Adding plot titles and axis labels
  • D. Inserting audio annotations
Reveal Solution  Discussion  0

Correct Answer: A,C  🗳️

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