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

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q50-Q55):

NEW QUESTION # 50
Which Python control structures are commonly used for data filtering and iterative operations in real-world analytics tasks?
(Choose two)

A. decorators
B. list comprehensions
C. while loops
D. match-case statements

Answer: B,C

NEW QUESTION # 51
Which storage system is best suited for storing and retrieving large volumes of unstructured data, such as images or logs?

A. Data cubes
B. In-memory databases
C. Object storage systems
D. Relational databases

Answer: C

NEW QUESTION # 52
Which practices are considered part of professional Python scripting standards?
(Choose two)

A. Avoiding error handling to keep code clean
B. Avoiding use of modules and libraries
C. Using consistent indentation and naming conventions
D. Including in-line comments and docstrings

Answer: C,D

NEW QUESTION # 53
Which technique would be most appropriate to handle missing numerical values in a dataset intended for machine learning?

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

Answer: D

NEW QUESTION # 54
Which statements correctly describe differences between linear and logistic regression?
(Choose two)

A. Linear regression applies only to classification tasks
B. Logistic regression uses the logit function to model relationships
C. Linear regression requires target variables to be binary
D. Logistic regression outputs probabilities, while linear regression predicts continuous values

Answer: B,D

NEW QUESTION # 55
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