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Python Institute PCAD-31-02 Exam Syllabus Topics:

Section	Weight	Objectives
Data Exploration and Statistical Analysis	25%	- Exploratory data analysis 1. Feature selection and dimensionality reduction basics 2. Identifying patterns, trends and outliers - Descriptive statistics 1. Correlation and covariance analysis 2. Measures of central tendency and dispersion 3. Frequency distributions and percentiles - Inferential statistics 1. Probability concepts and distributions 2. Statistical significance and interpretation 3. Hypothesis testing and confidence intervals
SQL and Database Integration	10%	- SQL querying 1. Subqueries and filtering 2. SELECT, WHERE, JOIN, GROUP BY, aggregate functions - 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
Data Modeling and Machine Learning Basics	20%	- 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
Data Visualization and Communication	15%	- Data storytelling and reporting 1. Written and verbal presentation techniques 2. Structuring insights and conclusions - Visualization principles and best practices 1. Audience-focused presentation 2. Choosing appropriate chart types 3. Color, layout and clarity - Visualization with Matplotlib and Seaborn 1. Customization and styling 2. Line, bar, scatter, histogram, box plots 3. Heatmaps, pair plots and correlation matrices
Data Acquisition and Preprocessing	30%	- 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 formats and storage systems 3. Data collection methods and sources - Data cleaning and validation 1. Quality assurance and validation techniques 2. Handling missing, duplicate and invalid values 3. Data standardization and transformation

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q107-Q112):

NEW QUESTION # 107
What is one potential risk of dynamically constructing SQL queries using string concatenation in Python scripts?

A. Exposure to SQL injection attacks
B. Missing foreign key constraints
C. Slower database performance
D. Increased memory usage

Answer: A

NEW QUESTION # 108
Which technique would be most appropriate to handle missing numerical values in a dataset intended for machine learning?

A. Replacing with NULL
B. Imputation using mean or median
C. Filling with random values
D. Dropping all columns

Answer: B

NEW QUESTION # 109
Which conditions typically necessitate data normalization or scaling before analysis?
(choose two)

A. Presence of duplicate data
B. Features with different units and scales
C. Distance-based modeling algorithms
D. Boolean feature encoding

Answer: B,C

NEW QUESTION # 110
What is a key assumption of linear regression that distinguishes it from logistic regression?

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

Answer: C

NEW QUESTION # 111
Why is it important to adjust data presentations based on the audience's background?

A. To ensure the data is understood and supports actionable insights
B. To simplify all metrics to percentages only
C. To include as many technical terms as possible
D. To avoid using charts altogether

Answer: A

NEW QUESTION # 112
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