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Python Institute PCAD-31-02

PCAD-31-02

Exam Code: PCAD-31-02

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

Updated: Aug 01, 2026

Q & A: 146 Questions and Answers

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Python Institute PCAD-31-02 Exam Syllabus Topics:

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

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

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

A) A Series contains multiple DataFrames
B) A DataFrame is a two-dimensional container of Series objects
C) A DataFrame is a one-dimensional array of Series
D) A Series can only be created from a DataFrame


2. When analyzing a dataset, which of the following functions would best help detect skewness or asymmetry in the data distribution?

A) skew()
B) sum()
C) mean()
D) std()


3. What is the primary advantage of using web scraping as a data collection method in business analytics?

A) It guarantees real-time data accuracy
B) It ensures compliance with privacy laws
C) It allows automation of data collection from public sources
D) It eliminates the need for data cleaning


4. What is the purpose of using the groupby() function in a Pandas DataFrame when analyzing a dataset with multiple categories and numerical values?

A) To split the data into subgroups for aggregation
B) To sort the DataFrame by a specific column
C) To filter rows based on index values
D) To append new rows to the DataFrame


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()


Solutions:

Question # 1
Answer: B
Question # 2
Answer: A
Question # 3
Answer: C
Question # 4
Answer: A
Question # 5
Answer: A

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