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Python Institute PCAD-31-02 Exam Syllabus Topics:

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

>> PCAD-31-02 Visual Cert Exam <<

New PCAD-31-02 Visual Cert Exam | Pass-Sure PCAD-31-02 Study Test: Python Institute Certified Associate Data Analyst with Python (PCAD-31-02)

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q105-Q110):

NEW QUESTION # 105
Which methods can be used to validate data types and structure in a Pandas DataFrame?
(Choose two)

A. df.to_csv()
B. df.memory_usage()
C. df.info()
D. df.dtypes

Answer: C,D

NEW QUESTION # 106
Which actions are considered part of basic data validation tasks in Python using Pandas?
(Choose two)

A. Confirming numeric columns contain only integers
B. Sorting the data frame by index
C. Replacing NULL values with placeholders
D. Checking for duplicate rows

Answer: A,D

NEW QUESTION # 107
When working with relational databases in Python, what are key components required to establish and manage database connections?
(Choose all that apply)

A. Database driver or module (e.g., sqlite3)
B. Connection object to interface with the database
C. Python's random module for generating keys
D. Cursor object for executing queries

Answer: A,B

NEW QUESTION # 108
Which actions are considered best practices when managing datasets in Python using pandas?
(Choose two)

A. Replace missing values with hardcoded zeros without analysis
B. Use .describe() for summary statistics
C. Always convert all columns to strings
D. Use .info() to understand dataset structure

Answer: B,D

NEW QUESTION # 109
Which of the following would best validate the uniqueness of a primary key in a Pandas DataFrame?

A. df['id'].duplicated().any()
B. df['id'].fillna()
C. df['id'].isnull().sum()
D. df['id'].nunique()

Answer: A

NEW QUESTION # 110
......

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