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Python Institute PCAD-31-02 Exam Syllabus Topics:

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

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q69-Q74):

NEW QUESTION # 69
Which operations are valid when working with Pandas Series?
(Choose two)

A. Applying NumPy universal functions
B. Merging by index using merge()
C. Using .columns to rename
D. Arithmetic vector operations

Answer: A,D

NEW QUESTION # 70
When creating a line chart with Matplotlib, which function is most appropriate to add a text label at a specific data point?

A. plt.annotate()
B. plt.xlabel()
C. plt.legend()
D. plt.title()

Answer: A

NEW QUESTION # 71
What is a key advantage of using the Parquet file format over CSV in large-scale data pipelines?

A. Parquet offers row-based compression
B. Parquet supports schema evolution and columnar storage
C. Parquet stores data in plain text, making it human-readable
D. Parquet files can only be used with Excel

Answer: B

NEW QUESTION # 72
Which SQL command should a data analyst use to modify existing records in a database table based on specific conditions?

A. SELECT
B. INSERT
C. CREATE
D. UPDATE

Answer: D

NEW QUESTION # 73
Which of the following are key differences between Pandas and NumPy arrays?
(Choose two)

A. Pandas Series are more memory efficient than NumPy arrays
B. Pandas handles labeled data; NumPy does not
C. Pandas integrates better with tabular data and missing values
D. NumPy supports string indexing; Pandas does not

Answer: B,C

NEW QUESTION # 74
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