Python Institute PCAD-31-02 valid 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: Sep 25, 2026
  • Q&As: 146 Questions and Answers

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Python Institute PCAD-31-02 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Data Acquisition and Preprocessing30%- Data collection, integration and storage
  • 1. Data collection methods and sources
  • 2. Data formats and storage systems
  • 3. Data integration and merging
- Data cleaning and validation
  • 1. Quality assurance and validation techniques
  • 2. Data standardization and transformation
  • 3. Handling missing, duplicate and invalid values
- Data preparation with Pandas and NumPy
  • 1. Data reshaping and aggregation
  • 2. Indexing, filtering, sorting and grouping
  • 3. Data structures: Series, DataFrame, ndarray
Topic 2: Data Exploration and Statistical Analysis25%- Exploratory data analysis
  • 1. Feature selection and dimensionality reduction basics
  • 2. Identifying patterns, trends and outliers
- Descriptive statistics
  • 1. Correlation and covariance analysis
  • 2. Frequency distributions and percentiles
  • 3. Measures of central tendency and dispersion
- Inferential statistics
  • 1. Statistical significance and interpretation
  • 2. Hypothesis testing and confidence intervals
  • 3. Probability concepts and distributions
Topic 3: 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. Classification: logistic regression, k-NN, decision trees
  • 2. Regression: linear, multiple, polynomial
  • 3. Model training, testing and evaluation
Topic 4: 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. Executing queries and retrieving results
  • 2. Connecting to SQLite, MySQL or PostgreSQL
  • 3. Error handling and best practices
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. Choosing appropriate chart types
  • 3. Color, layout and clarity

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

Question #1

Which practices contribute to better exception handling in data pipelines involving file I/O and numerical computations?
(Choose two)

  • A. Suppressing all error output using pass
  • B. Using specific exception types like FileNotFoundError or ZeroDivisionError
  • C. Logging error messages for later inspection during failures
  • D. Catching all exceptions using a generic except: clause without specifying the error
Reveal Solution  Discussion  0

Correct Answer: A,C  🗳️

Question #2

Which Pandas methods are commonly used to extract central tendency and dispersion insights from a DataFrame?
(Choose two)

  • A. median()
  • B. pivot_table()
  • C. sort_values()
  • D. var()
Reveal Solution  Discussion  0

Correct Answer: A,D  🗳️

Question #3

What is the primary purpose of using a requirements.txt file in Python-based data projects?

  • A. To list the packages and versions required for consistent environment setup
  • B. To store the current working directory of Python modules
  • C. To track script runtime dependencies during execution
  • D. To automatically compress source code for deployment
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

Question #4

Which actions are valid techniques for handling erroneous categorical values in a dataset?
(Choose two)

  • A. Converting all values to integers
  • B. Replacing inconsistent labels with a standardized value
  • C. Removing rows with invalid labels
  • D. Normalizing using min-max scaling
Reveal Solution  Discussion  0

Correct Answer: B,C  🗳️

Question #5

Why is min-max scaling applied before using algorithms like K-Nearest Neighbors or SVM?

  • A. To make all features interpretable as binary values
  • B. To compress all values into integers
  • C. To prevent features with larger scales from dominating distance calculations
  • D. To convert strings into numeric values
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

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