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Python Institute PCAD-31-02 Exam Syllabus Topics:

Section	Weight	Objectives
Data Exploration and Statistical Analysis	25%	- Inferential statistics 1. Hypothesis testing and confidence intervals 2. Statistical significance and interpretation 3. Probability concepts and distributions - Exploratory data analysis 1. Feature selection and dimensionality reduction basics 2. Identifying patterns, trends and outliers - Descriptive statistics 1. Frequency distributions and percentiles 2. Measures of central tendency and dispersion 3. Correlation and covariance analysis
Data Modeling and Machine Learning Basics	20%	- Supervised learning fundamentals 1. Model training, testing and evaluation 2. Regression: linear, multiple, polynomial 3. Classification: logistic regression, k-NN, decision trees - Model performance and optimization 1. Hyperparameter tuning basics 2. Overfitting, underfitting and generalization 3. Accuracy, precision, recall, F1-score
Data Visualization and Communication	15%	- Visualization with Matplotlib and Seaborn 1. Customization and styling 2. Line, bar, scatter, histogram, box plots 3. Heatmaps, pair plots and correlation matrices - 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
Data Acquisition and Preprocessing	30%	- Data preparation with Pandas and NumPy 1. Indexing, filtering, sorting and grouping 2. Data reshaping and aggregation 3. Data structures: Series, DataFrame, ndarray - Data cleaning and validation 1. Data standardization and transformation 2. Quality assurance and validation techniques 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
SQL and Database Integration	10%	- SQL querying 1. SELECT, WHERE, JOIN, GROUP BY, aggregate functions 2. Subqueries and filtering - Python-database connectivity 1. Executing queries and retrieving results 2. Error handling and best practices 3. Connecting to SQLite, MySQL or PostgreSQL - Relational database concepts 1. Tables, keys, relationships and normalization

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q90-Q95):

NEW QUESTION # 90
Which technique is considered a primary defense against SQL injection when executing database queries from Python?

A. Encrypting the database connection
B. Escaping quotes manually in SQL strings
C. Using parameterized queries
D. Closing the database cursor after each query

Answer: C

NEW QUESTION # 91
Which method is typically used in Pandas to check if a column contains only values within an expected range?

A. df.sort_values()
B. df.isin()
C. df.clip()
D. df.sample()

Answer: B

NEW QUESTION # 92
Why is it important to adjust data presentations based on the audience's background?

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

Answer: C

NEW QUESTION # 93
What may occur if a model is evaluated using the same data it was trained on?

A. Overfitting
B. Underfitting
C. Overgeneralization
D. Data leakage

Answer: A

NEW QUESTION # 94
Which of the following statements best describes the structural relationship between Pandas Series and DataFrames?

A. A DataFrame is a one-dimensional array of Series
B. A DataFrame is a two-dimensional container of Series objects
C. A Series contains multiple DataFrames
D. A Series can only be created from a DataFrame

Answer: B

NEW QUESTION # 95
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