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Python Institute PCAD-31-02 Exam Syllabus Topics:

Section	Weight	Objectives
Working with Data Using Python Libraries	30%	- Pandas Library 1. DataFrame operations (merge, join, concat) 2. Handling missing data 3. GroupBy operations 4. Data selection and filtering 5. Series and DataFrame structures - Data Visualization 1. Seaborn introduction 2. Creating basic charts (line, bar, scatter, histogram) 3. Customizing plots 4. Matplotlib basics - NumPy Fundamentals 1. Vectorized operations 2. Basic statistical functions 3. Array indexing and slicing 4. NumPy arrays and operations
Applied Data Analysis Projects	20%	- Exploratory Data Analysis (EDA) 1. Correlation analysis 2. Pattern identification 3. Data distribution analysis 4. Descriptive statistics computation - Data Analysis Workflow 1. Problem definition 2. Analysis and modeling 3. Results interpretation and presentation 4. Data exploration and cleaning
Data Analysis Fundamentals	20%	- Introduction to Data Analysis 1. Data analysis concepts and terminology 2. Data analysis process lifecycle 3. Types of data (structured, unstructured, semi-structured) - Data Collection and Preparation 1. Data cleaning and preprocessing basics 2. Data import/export operations 3. Data sources and acquisition methods
Python Programming for Data Analysis	30%	- Control Flow and Functions 1. Function definitions and parameters 2. Return values and scope 3. Conditional statements (if, elif, else) 4. Loops (for, while) - Python Data Types and Structures 1. Lists, tuples, dictionaries, sets 2. Numbers, strings, booleans 3. Data type conversions - File Operations 1. Writing to files 2. Reading from files (text, CSV) 3. Context managers (with statement)

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q103-Q108):

NEW QUESTION # 103
Which refinements are typically used to enhance clarity and presentation quality in visualizations?
(Choose two)

A. Avoiding color entirely
B. Adding descriptive axis labels
C. Customizing tick labels
D. Disabling grid lines in all cases

Answer: B,C

NEW QUESTION # 104
What is the best approach when comparing SQL NULL values with Python data during conditional checks?

A. Convert all NULL to string before comparison
B. Replace NULL with zero before importing
C. Use Python's None and SQL's IS NULL clause
D. Use == None directly in SQL queries

Answer: C

NEW QUESTION # 105
Which Python constructs are useful for comparing and managing object identity in a data pipeline?
(Choose all that apply)

A. str() function to convert data formats
B. id() function to retrieve unique object identifiers
C. is operator for memory-level comparison
D. == operator for checking value equivalence

Answer: B,C

NEW QUESTION # 106
What is the primary purpose of using a requirements.txt file in Python-based data projects?

A. To track script runtime dependencies during execution
B. To automatically compress source code for deployment
C. To list the packages and versions required for consistent environment setup
D. To store the current working directory of Python modules

Answer: C

NEW QUESTION # 107
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.sort_values() to sort both columns before merging
B. Use .isin() to compare the foreign key column against the parent key column
C. Use df.dropna() on the foreign key column
D. Use df.merge() with how='outer'

Answer: B

NEW QUESTION # 108
......

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