Python Institute PCAD-31-02 exam dumps : 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: Jul 31, 2026     Q & A: 146 Questions and Answers

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Python Institute PCAD-31-02 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Data Acquisition and Preprocessing30%- 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 collection methods and sources
  • 3. Data formats and storage systems
- Data cleaning and validation
  • 1. Quality assurance and validation techniques
  • 2. Data standardization and transformation
  • 3. Handling missing, duplicate and invalid values
Topic 2: Data Modeling and Machine Learning Basics20%- Model performance and optimization
  • 1. Accuracy, precision, recall, F1-score
  • 2. Overfitting, underfitting and generalization
  • 3. Hyperparameter tuning basics
- Supervised learning fundamentals
  • 1. Model training, testing and evaluation
  • 2. Regression: linear, multiple, polynomial
  • 3. Classification: logistic regression, k-NN, decision trees
Topic 3: SQL and Database Integration10%- SQL querying
  • 1. SELECT, WHERE, JOIN, GROUP BY, aggregate functions
  • 2. Subqueries and filtering
- Python-database connectivity
  • 1. Executing queries and retrieving results
  • 2. Error handling and best practices
  • 3. Connecting to SQLite, MySQL or PostgreSQL
- Relational database concepts
  • 1. Tables, keys, relationships and normalization
Topic 4: Data Exploration and Statistical Analysis25%- Inferential statistics
  • 1. Probability concepts and distributions
  • 2. Hypothesis testing and confidence intervals
  • 3. Statistical significance and interpretation
- 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
Topic 5: Data Visualization and Communication15%- 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
- Visualization principles and best practices
  • 1. Choosing appropriate chart types
  • 2. Color, layout and clarity
  • 3. Audience-focused presentation

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

1. Which operations are recommended when organizing messy tabular data in Pandas for further transformation and statistical modeling?
(choose two)

A) Converting the DataFrame to a Series
B) Resetting the index after filtering
C) Dropping column headers
D) Ensuring consistent data types


2. What is the primary difference between the == operator and the is operator in Python when used in a data pipeline?

A) is compares dictionary keys, while == compares keys and values
B) is checks value equality, while == checks for data type compatibility
C) == compares values for equality, while is checks for object identity
D) == checks identity, while is compares memory addresses


3. What does the standard deviation of a dataset represent in the context of statistical analysis?

A) The most frequently occurring value
B) The average squared distance from the mean
C) The sum of all values in the dataset
D) The number of outliers in the dataset


4. You have a list of product prices, and you need to remove duplicate values while maintaining the fastest performance. Which core data structure should you use?

A) set
B) list
C) tuple
D) dictionary


5. Which operations are valid when working with Pandas Series?
(Choose two)

A) Applying NumPy universal functions
B) Arithmetic vector operations
C) Using .columns to rename
D) Merging by index using merge()


Solutions:

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

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