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Python Institute PCAD-31-02 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Modeling and Machine Learning Basics	20%	- Supervised learning fundamentals 1. Classification: logistic regression, k-NN, decision trees 2. Model training, testing and evaluation 3. Regression: linear, multiple, polynomial - Model performance and optimization 1. Accuracy, precision, recall, F1-score 2. Hyperparameter tuning basics 3. Overfitting, underfitting and generalization
Topic 2: Data Visualization and Communication	15%	- Visualization principles and best practices 1. Choosing appropriate chart types 2. Color, layout and clarity 3. Audience-focused presentation - 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. Structuring insights and conclusions 2. Written and verbal presentation techniques
Topic 3: SQL and Database Integration	10%	- Relational database concepts 1. Tables, keys, relationships and normalization - Python-database connectivity 1. Executing queries and retrieving results 2. Error handling and best practices 3. Connecting to SQLite, MySQL or PostgreSQL - SQL querying 1. Subqueries and filtering 2. SELECT, WHERE, JOIN, GROUP BY, aggregate functions
Topic 4: Data Acquisition and Preprocessing	30%	- Data preparation with Pandas and NumPy 1. Indexing, filtering, sorting and grouping 2. Data structures: Series, DataFrame, ndarray 3. Data reshaping and aggregation - Data cleaning and validation 1. Quality assurance and validation techniques 2. Data standardization and transformation 3. Handling missing, duplicate and invalid values - Data collection, integration and storage 1. Data integration and merging 2. Data formats and storage systems 3. Data collection methods and sources
Topic 5: Data Exploration and Statistical Analysis	25%	- Descriptive statistics 1. Frequency distributions and percentiles 2. Measures of central tendency and dispersion 3. Correlation and covariance analysis - Inferential statistics 1. Hypothesis testing and confidence intervals 2. Statistical significance and interpretation 3. Probability concepts and distributions - Exploratory data analysis 1. Identifying patterns, trends and outliers 2. Feature selection and dimensionality reduction basics

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q52-Q57):

NEW QUESTION # 52
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 target variable is unordered categorical
D. The relationship between predictors and the outcome is linear

Answer: D

NEW QUESTION # 53
What is the primary difference between the == operator and the is operator in Python when used in a data pipeline?

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

Answer: A

NEW QUESTION # 54
What is the main reason to remove duplicate records before performing analysis?

A. It ensures training data is intentionally overfitted
B. It helps increase sample size
C. It avoids converting data to JSON format
D. It reduces redundancy that can skew statistical metrics

Answer: D

NEW QUESTION # 55
What is the primary purpose of using annotations in a data visualization?

A. To enhance interactivity in static plots
B. To highlight important data points and provide contextual information
C. To ensure faster rendering in large datasets
D. To replace the need for axes

Answer: B

NEW QUESTION # 56
Why is it important to manage object identity correctly in complex data analysis pipelines?

A. It prevents reloading modules unnecessarily
B. It avoids redundant copies and preserves shared state when required
C. It ensures type casting is consistently applied
D. It guarantees consistent hashing of string values

Answer: B

NEW QUESTION # 57
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