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Python Institute PCAD-31-02 Exam Syllabus Topics:

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

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q18-Q23):

NEW QUESTION # 18
Which Python construct allows you to iterate over a list of dictionary values and apply conditional logic to accumulate numerical values that meet a certain threshold?

A. filter() function with map
B. for loop with if condition
C. while loop with try block
D. lambda expression with reduce

Answer: B

NEW QUESTION # 19
What is the recommended Python library to connect and extract tabular data directly from an Excel file?

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

Answer: B

NEW QUESTION # 20
Which methods are typically used to assess relationships between variables in exploratory data analysis?
(Choose two)

A. Scatter plots
B. Histogram analysis
C. Correlation matrix
D. Box plots

Answer: A,C

NEW QUESTION # 21
Which features of object-oriented patterns improve clarity and reusability in Python analysis workflows?
(Choose all that apply)

A. Using global variables instead of object properties
B. Replacing all functions with lambda expressions
C. Encapsulating behavior in class methods
D. Applying inheritance to avoid redundant code

Answer: C,D

NEW QUESTION # 22
What is one potential risk of dynamically constructing SQL queries using string concatenation in Python scripts?

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

Answer: B

NEW QUESTION # 23
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