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: Aug 03, 2026   Q&As: 146 Questions and Answers

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Python Institute PCAD-31-02 Exam Syllabus Topics:
SectionWeightObjectives
SQL and Database Integration10%- SQL querying
  • 1. SELECT, WHERE, JOIN, GROUP BY, aggregate functions
  • 2. Subqueries and filtering
- Relational database concepts
  • 1. Tables, keys, relationships and normalization
- Python-database connectivity
  • 1. Connecting to SQLite, MySQL or PostgreSQL
  • 2. Error handling and best practices
  • 3. Executing queries and retrieving results
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 formats and storage systems
  • 2. Data collection methods and sources
  • 3. Data integration and merging
Data Exploration and Statistical Analysis25%- Descriptive statistics
  • 1. Correlation and covariance analysis
  • 2. Measures of central tendency and dispersion
  • 3. Frequency distributions and percentiles
- Inferential statistics
  • 1. Statistical significance and interpretation
  • 2. Probability concepts and distributions
  • 3. Hypothesis testing and confidence intervals
- Exploratory data analysis
  • 1. Feature selection and dimensionality reduction basics
  • 2. Identifying patterns, trends and outliers
Data Visualization and Communication15%- Visualization principles and best practices
  • 1. Choosing appropriate chart types
  • 2. Audience-focused presentation
  • 3. Color, layout and clarity
- Visualization with Matplotlib and Seaborn
  • 1. Line, bar, scatter, histogram, box plots
  • 2. Heatmaps, pair plots and correlation matrices
  • 3. Customization and styling
- Data storytelling and reporting
  • 1. Written and verbal presentation techniques
  • 2. Structuring insights and conclusions
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. Classification: logistic regression, k-NN, decision trees
  • 2. Regression: linear, multiple, polynomial
  • 3. Model training, testing and evaluation
Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions:

1. How would you extract the last three rows of a DataFrame df using position-based indexing?

A) df.tail(3)
B) df.iloc[-3:]
C) df[-3]
D) df.loc[-3:]


2. Which characteristics are typically associated with supervised learning algorithms?
(Choose two)

A) They work only with numerical features
B) They are useful for regression and classification tasks
C) They automatically detect outliers
D) They require labeled training data


3. Which operations are valid and commonly used on Python lists during data transformation tasks?
(Choose two)

A) Slicing
B) Index-based iteration
C) Key-value mapping
D) Append


4. Which command should a data analyst use to install the pandas library in a Python environment using the Python Package Index?

A) python install pandas
B) install pip pandas
C) python get pandas
D) pip install pandas


5. Which method is best suited to transform inconsistent entries such as "N/A", "missing", and empty strings into a standard missing value representation in a Pandas DataFrame?

A) df.dropna()
B) df.rename()
C) df.sort_values()
D) df.replace()


Solutions:

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

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