Quiz 2026 Python Institute PCAD-31-02: Fantastic Test Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Guide

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Python Institute PCAD-31-02 Exam Syllabus Topics:

Section	Weight	Objectives
Data Modeling and Machine Learning Basics	20%	- Supervised learning fundamentals 1. Regression: linear, multiple, polynomial 2. Classification: logistic regression, k-NN, decision trees 3. Model training, testing and evaluation - Model performance and optimization 1. Hyperparameter tuning basics 2. Overfitting, underfitting and generalization 3. Accuracy, precision, recall, F1-score
SQL and Database Integration	10%	- 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. Connecting to SQLite, MySQL or PostgreSQL 2. Error handling and best practices 3. Executing queries and retrieving results
Data Exploration and Statistical Analysis	25%	- Inferential statistics 1. Hypothesis testing and confidence intervals 2. Statistical significance and interpretation 3. Probability concepts and distributions - Descriptive statistics 1. Correlation and covariance analysis 2. Frequency distributions and percentiles 3. Measures of central tendency and dispersion - Exploratory data analysis 1. Identifying patterns, trends and outliers 2. Feature selection and dimensionality reduction basics
Data Visualization and Communication	15%	- Data storytelling and reporting 1. Structuring insights and conclusions 2. Written and verbal presentation techniques - Visualization principles and best practices 1. Color, layout and clarity 2. Audience-focused presentation 3. Choosing appropriate chart types - Visualization with Matplotlib and Seaborn 1. Heatmaps, pair plots and correlation matrices 2. Customization and styling 3. Line, bar, scatter, histogram, box plots
Data Acquisition and Preprocessing	30%	- Data cleaning and validation 1. Handling missing, duplicate and invalid values 2. Data standardization and transformation 3. Quality assurance and validation techniques - Data preparation with Pandas and NumPy 1. Data reshaping and aggregation 2. Data structures: Series, DataFrame, ndarray 3. Indexing, filtering, sorting and grouping - Data collection, integration and storage 1. Data integration and merging 2. Data collection methods and sources 3. Data formats and storage systems

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q57-Q62):

NEW QUESTION # 57
What is the recommended Python library to connect and extract tabular data directly from an Excel file?

A. matplotlib
B. csv
C. sqlite3
D. openpyxl

Answer: D

NEW QUESTION # 58
How would you extract the last three rows of a DataFrame df using position-based indexing?

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

Answer: D

NEW QUESTION # 59
What is a key assumption of linear regression that distinguishes it from logistic regression?

A. Output values must be binary
B. Predictors must be independent
C. The relationship between predictors and the outcome is linear
D. The target variable is unordered categorical

Answer: C

NEW QUESTION # 60
What is the benefit of using the __name__ == "__main__" construct in Python scripts?

A. It prevents modules from being imported
B. It defines the main function in Python
C. It allows selective execution of code when run as a script
D. It allows access to all global variables

Answer: C

NEW QUESTION # 61
Which of the following best describes the purpose of the plt.subplot() function in Matplotlib?

A. It links Matplotlib with Seaborn's grid styling
B. It configures multiple plots in a grid layout within a single figure
C. It creates a 3D surface plot using pandas
D. It overlays multiple plots in the same figure without axes

Answer: B

NEW QUESTION # 62
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