PCAD-31-02 Exam Tutorial - Latest PCAD-31-02 Test Prep

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Python Institute PCAD-31-02 Exam Syllabus Topics:

Section	Weight	Objectives
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. SELECT, WHERE, JOIN, GROUP BY, aggregate functions 2. Subqueries and filtering
Data Acquisition and Preprocessing	30%	- Data cleaning and validation 1. Data standardization and transformation 2. Handling missing, duplicate and invalid values 3. Quality assurance and validation techniques - Data collection, integration and storage 1. Data integration and merging 2. Data formats and storage systems 3. Data collection methods and sources - Data preparation with Pandas and NumPy 1. Indexing, filtering, sorting and grouping 2. Data structures: Series, DataFrame, ndarray 3. Data reshaping and aggregation
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. Frequency distributions and percentiles 2. Measures of central tendency and dispersion 3. Correlation and covariance analysis - Exploratory data analysis 1. Identifying patterns, trends and outliers 2. Feature selection and dimensionality reduction basics
Data Visualization and Communication	15%	- Visualization with Matplotlib and Seaborn 1. Heatmaps, pair plots and correlation matrices 2. Line, bar, scatter, histogram, box plots 3. Customization and styling - Visualization principles and best practices 1. Audience-focused presentation 2. Color, layout and clarity 3. Choosing appropriate chart types - Data storytelling and reporting 1. Structuring insights and conclusions 2. Written and verbal presentation techniques
Data Modeling and Machine Learning Basics	20%	- Model performance and optimization 1. Overfitting, underfitting and generalization 2. Hyperparameter tuning basics 3. Accuracy, precision, recall, F1-score - Supervised learning fundamentals 1. Regression: linear, multiple, polynomial 2. Model training, testing and evaluation 3. Classification: logistic regression, k-NN, decision trees

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q136-Q141):

NEW QUESTION # 136
Which of the following improves the readability of a spreadsheet used for collaborative data analysis?

A. Removing gridlines from all sheets
B. Using colored fonts to highlight every cell
C. Converting numeric values into images
D. Adding freeze panes to lock header rows

Answer: D

NEW QUESTION # 137
What is the correct sequence to connect to a database and execute a query using the sqlite3 module in Python?

A. Connect → Commit → Execute → Close
B. Connect → Execute → Commit → Close
C. Execute → Connect → Commit → Close
D. Commit → Connect → Execute → Close

Answer: B

NEW QUESTION # 138
Which file format is most suitable for exchanging large tabular datasets with consistent column data types across systems?

A. .txt
B. .json
C. .xml
D. .csv

Answer: D

NEW QUESTION # 139
Which of the following practices improves clarity when combining visualizations and textual explanations in a report?

A. Use visualizations alone without titles or captions
B. Include concise textual summaries that highlight the chart's message
C. Repeat all numerical data in both the chart and text
D. Avoid summarizing the data verbally

Answer: B

NEW QUESTION # 140
Which method in a Python class is responsible for initializing the object's attributes at the time of creation?

A. __main__()
B. __init__()
C. __str__()
D. __getattr__()

Answer: B

NEW QUESTION # 141
......

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