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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. Hyperparameter tuning basics 2. Accuracy, precision, recall, F1-score 3. Overfitting, underfitting and generalization - Supervised learning fundamentals 1. Model training, testing and evaluation 2. Regression: linear, multiple, polynomial 3. Classification: logistic regression, k-NN, decision trees
Topic 2: 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 preparation with Pandas and NumPy 1. Indexing, filtering, sorting and grouping 2. Data reshaping and aggregation 3. Data structures: Series, DataFrame, ndarray - Data collection, integration and storage 1. Data collection methods and sources 2. Data integration and merging 3. Data formats and storage systems
Topic 3: Data Exploration and Statistical Analysis	25%	- Descriptive statistics 1. Measures of central tendency and dispersion 2. Frequency distributions and percentiles 3. Correlation and covariance analysis - Inferential statistics 1. Statistical significance and interpretation 2. Hypothesis testing and confidence intervals 3. Probability concepts and distributions - Exploratory data analysis 1. Feature selection and dimensionality reduction basics 2. Identifying patterns, trends and outliers
Topic 4: SQL and Database Integration	10%	- Relational database concepts 1. Tables, keys, relationships and normalization - Python-database connectivity 1. Connecting to SQLite, MySQL or PostgreSQL 2. Executing queries and retrieving results 3. Error handling and best practices - SQL querying 1. Subqueries and filtering 2. SELECT, WHERE, JOIN, GROUP BY, aggregate functions
Topic 5: Data Visualization and Communication	15%	- Visualization with Matplotlib and Seaborn 1. Heatmaps, pair plots and correlation matrices 2. Customization and styling 3. Line, bar, scatter, histogram, box plots - Data storytelling and reporting 1. Structuring insights and conclusions 2. Written and verbal presentation techniques - Visualization principles and best practices 1. Choosing appropriate chart types 2. Color, layout and clarity 3. Audience-focused presentation

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q27-Q32):

NEW QUESTION # 27
Which features of object-oriented patterns improve clarity and reusability in Python analysis workflows?
(Choose all that apply)

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

Answer: B,D

NEW QUESTION # 28
Which chart types are commonly used to represent relationships between two numerical variables in Python-based visualizations?
(choose two)
Response:

A. Histogram
B. Heatmap
C. Scatter plot
D. Line chart

Answer: B,C

NEW QUESTION # 29
Which Python library is most commonly used to establish a connection to a SQLite database and perform SQL operations?

A. psycopg2
B. pymysql
C. sqlalchemy
D. sqlite3

Answer: D

NEW QUESTION # 30
Which SQL clause is most appropriate when you need to filter records that meet a specific condition during data retrieval in an analytics pipeline?

A. WHERE
B. GROUP BY
C. HAVING
D. ORDER BY

Answer: A

NEW QUESTION # 31
What is the most efficient way to handle missing values in a Pandas DataFrame when preparing a dataset for analysis that must maintain original index structure and avoid row deletions?

A. Use dropna() with axis=1
B. Use drop() to remove null rows
C. Use fillna() with a constant value
D. Apply interpolate() method

Answer: C

NEW QUESTION # 32
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