100% Pass Quiz 2026 PCAD-31-02: Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Updated Sample Questions Answers

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Python Institute PCAD-31-02 Exam Syllabus Topics:

SectionWeightObjectives
Data Exploration and Statistical Analysis25%- Inferential statistics
  • 1. Hypothesis testing and confidence intervals
  • 2. Statistical significance and interpretation
  • 3. Probability concepts and distributions
- Exploratory data analysis
  • 1. Feature selection and dimensionality reduction basics
  • 2. Identifying patterns, trends and outliers
- Descriptive statistics
  • 1. Frequency distributions and percentiles
  • 2. Correlation and covariance analysis
  • 3. Measures of central tendency and dispersion
Data Modeling and Machine Learning Basics20%- Model performance and optimization
  • 1. Hyperparameter tuning basics
  • 2. Overfitting, underfitting and generalization
  • 3. Accuracy, precision, recall, F1-score
- Supervised learning fundamentals
  • 1. Regression: linear, multiple, polynomial
  • 2. Model training, testing and evaluation
  • 3. Classification: logistic regression, k-NN, decision trees
SQL and Database Integration10%- Relational database concepts
  • 1. Tables, keys, relationships and normalization
- SQL querying
  • 1. Subqueries and filtering
  • 2. SELECT, WHERE, JOIN, GROUP BY, aggregate functions
- Python-database connectivity
  • 1. Error handling and best practices
  • 2. Connecting to SQLite, MySQL or PostgreSQL
  • 3. Executing queries and retrieving results
Data Visualization and Communication15%- Visualization principles and best practices
  • 1. Color, layout and clarity
  • 2. Audience-focused presentation
  • 3. Choosing appropriate chart types
- Data storytelling and reporting
  • 1. Written and verbal presentation techniques
  • 2. Structuring insights and conclusions
- Visualization with Matplotlib and Seaborn
  • 1. Line, bar, scatter, histogram, box plots
  • 2. Customization and styling
  • 3. Heatmaps, pair plots and correlation matrices
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 cleaning and validation
  • 1. Handling missing, duplicate and invalid values
  • 2. Data standardization and transformation
  • 3. Quality assurance and validation techniques
- Data collection, integration and storage
  • 1. Data collection methods and sources
  • 2. Data integration and merging
  • 3. Data formats and storage systems

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q91-Q96):

NEW QUESTION # 91
Which of the following exceptions would most likely be raised if a string is directly converted to an integer in Python?

Answer: D


NEW QUESTION # 92
When analyzing a dataset, which of the following functions would best help detect skewness or asymmetry in the data distribution?

Answer: D


NEW QUESTION # 93
Which actions are considered part of basic data validation tasks in Python using Pandas?
(Choose two)

Answer: C,D


NEW QUESTION # 94
What is the primary purpose of using a requirements.txt file in Python-based data projects?

Answer: B


NEW QUESTION # 95
Which methods are typically used to assess relationships between variables in exploratory data analysis?
(Choose two)

Answer: A,D


NEW QUESTION # 96
......

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