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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. Probability concepts and distributions 3. Statistical significance and interpretation - Descriptive statistics 1. Measures of central tendency and dispersion 2. Correlation and covariance analysis 3. Frequency distributions and percentiles - Exploratory data analysis 1. Feature selection and dimensionality reduction basics 2. Identifying patterns, trends and outliers
Data Visualization and Communication	15%	- Visualization principles and best practices 1. Color, layout and clarity 2. Choosing appropriate chart types 3. Audience-focused presentation - 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. Written and verbal presentation techniques 2. Structuring insights and conclusions
SQL and Database Integration	10%	- Python-database connectivity 1. Connecting to SQLite, MySQL or PostgreSQL 2. Error handling and best practices 3. Executing queries and retrieving results - Relational database concepts 1. Tables, keys, relationships and normalization - SQL querying 1. Subqueries and filtering 2. SELECT, WHERE, JOIN, GROUP BY, aggregate functions
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 preparation with Pandas and NumPy 1. Data structures: Series, DataFrame, ndarray 2. Indexing, filtering, sorting and grouping 3. Data reshaping and aggregation - Data collection, integration and storage 1. Data formats and storage systems 2. Data integration and merging 3. Data collection methods and sources
Data Modeling and Machine Learning Basics	20%	- Supervised learning fundamentals 1. Classification: logistic regression, k-NN, decision trees 2. Model training, testing and evaluation 3. Regression: linear, multiple, polynomial - Model performance and optimization 1. Hyperparameter tuning basics 2. Overfitting, underfitting and generalization 3. Accuracy, precision, recall, F1-score

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q71-Q76):

NEW QUESTION # 71
Which transformation method adjusts the mean of the dataset to 0 and the standard deviation to 1?

A. Log transformation
B. Z-score normalization
C. One-hot encoding
D. Min-max normalization

Answer: B

NEW QUESTION # 72
Which Python control structures are commonly used for data filtering and iterative operations in real-world analytics tasks?
(Choose two)

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

Answer: B,D

NEW QUESTION # 73
What is the most appropriate way to ensure that a column used as a foreign key contains only valid references to a parent DataFrame in Pandas?

A. Use df.merge() with how='outer'
B. Use df.dropna() on the foreign key column
C. Use .isin() to compare the foreign key column against the parent key column
D. Use df.sort_values() to sort both columns before merging

Answer: C

NEW QUESTION # 74
Which Python feature allows the reuse of logic in different data processing steps, reducing duplication and improving maintainability?

A. Lambda functions
B. Tuple unpacking
C. Custom functions
D. Global variables

Answer: C

NEW QUESTION # 75
Which best practices help enhance database security when building Python data analysis pipelines?
(Choose two)

A. Store credentials securely using environment variables
B. Limit database user permissions to only necessary operations
C. Sanitize input by replacing dangerous characters with asterisks
D. Construct queries using f-strings for readability

Answer: A,B

NEW QUESTION # 76
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