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Python Institute PCAD-31-02 Exam Syllabus Topics:

Section	Weight	Objectives
Data Modeling and Machine Learning Basics	20%	- Model performance and optimization 1. Accuracy, precision, recall, F1-score 2. Hyperparameter tuning basics 3. Overfitting, underfitting and generalization - Supervised learning fundamentals 1. Regression: linear, multiple, polynomial 2. Model training, testing and evaluation 3. Classification: logistic regression, k-NN, decision trees
Data Exploration and Statistical Analysis	25%	- Inferential statistics 1. Probability concepts and distributions 2. Hypothesis testing and confidence intervals 3. Statistical significance and interpretation - 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
SQL and Database Integration	10%	- SQL querying 1. SELECT, WHERE, JOIN, GROUP BY, aggregate functions 2. Subqueries and filtering - 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
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. Customization and styling 2. Heatmaps, pair plots and correlation matrices 3. Line, bar, scatter, histogram, box plots
Data Acquisition and Preprocessing	30%	- 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 collection methods and sources 3. Data formats and storage systems - Data preparation with Pandas and NumPy 1. Data reshaping and aggregation 2. Data structures: Series, DataFrame, ndarray 3. Indexing, filtering, sorting and grouping

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q10-Q15):

NEW QUESTION # 10
What distinguishes structured data from unstructured data in the context of data modeling and storage?

A. Unstructured data contains only numeric values
B. Structured data cannot be stored in databases
C. Unstructured data cannot be stored or analyzed
D. Structured data follows a consistent schema or format

Answer: D

NEW QUESTION # 11
Which of the following best describes the use of relational databases in analytics?

A. Used only for image processing
B. Cannot enforce schema
C. Best for real-time log ingestion
D. Suitable for managing tabular structured data

Answer: D

NEW QUESTION # 12
Which SQL commands are typically used to retrieve and manipulate data in a data analysis context?
(Choose all that apply)
Response:

A. ROLLBACK
B. SELECT
C. INSERT
D. UPDATE

Answer: B,C

NEW QUESTION # 13
Which actions are considered best practices when managing datasets in Python using pandas?
(Choose two)

A. Use .describe() for summary statistics
B. Always convert all columns to strings
C. Replace missing values with hardcoded zeros without analysis
D. Use .info() to understand dataset structure

Answer: A,D

NEW QUESTION # 14
What is the main purpose of using the bootstrapping technique in inferential statistics?

A. To increase the number of features in a model
B. To filter outliers from a dataset
C. To estimate the sampling distribution by resampling with replacement
D. To validate the syntax of SQL queries in Python

Answer: C

NEW QUESTION # 15
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