Valid PCAD-31-02 Exam Papers - PCAD-31-02 Valid Test Guide

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Python Institute PCAD-31-02 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Visualization and Communication	15%	- Visualization with Matplotlib and Seaborn 1. Customization and styling 2. Line, bar, scatter, histogram, box plots 3. Heatmaps, pair plots and correlation matrices - Visualization principles and best practices 1. Color, layout and clarity 2. Audience-focused presentation 3. Choosing appropriate chart types - Data storytelling and reporting 1. Structuring insights and conclusions 2. Written and verbal presentation techniques
Topic 2: 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. Data standardization and transformation 2. Quality assurance and validation techniques 3. Handling missing, duplicate and invalid values - Data collection, integration and storage 1. Data integration and merging 2. Data collection methods and sources 3. Data formats and storage systems
Topic 3: Data Modeling and Machine Learning Basics	20%	- Model performance and optimization 1. Accuracy, precision, recall, F1-score 2. Overfitting, underfitting and generalization 3. Hyperparameter tuning basics - Supervised learning fundamentals 1. Regression: linear, multiple, polynomial 2. Model training, testing and evaluation 3. Classification: logistic regression, k-NN, decision trees
Topic 4: SQL and Database Integration	10%	- SQL querying 1. SELECT, WHERE, JOIN, GROUP BY, aggregate functions 2. Subqueries and filtering - Python-database connectivity 1. Error handling and best practices 2. Connecting to SQLite, MySQL or PostgreSQL 3. Executing queries and retrieving results - Relational database concepts 1. Tables, keys, relationships and normalization
Topic 5: Data Exploration and Statistical Analysis	25%	- Inferential statistics 1. Probability concepts and distributions 2. Hypothesis testing and confidence intervals 3. Statistical significance and interpretation - Exploratory data analysis 1. Feature selection and dimensionality reduction basics 2. Identifying patterns, trends and outliers - Descriptive statistics 1. Correlation and covariance analysis 2. Frequency distributions and percentiles 3. Measures of central tendency and dispersion

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q45-Q50):

NEW QUESTION # 45
Why is min-max scaling applied before using algorithms like K-Nearest Neighbors or SVM?

A. To convert strings into numeric values
B. To compress all values into integers
C. To prevent features with larger scales from dominating distance calculations
D. To make all features interpretable as binary values

Answer: C

NEW QUESTION # 46
What is the primary reason to use .head() and .tail() methods in pandas when exploring datasets?

A. To delete missing values
B. To create visualizations
C. To rename columns
D. To preview the first and last few rows of data

Answer: D

NEW QUESTION # 47
Which characteristics are typically associated with unstructured data formats?
(Choose two)

A. Stored using fixed-width fields and records
B. Lacks predefined data schema
C. Requires parsing or transformation before analysis
D. Highly tabular and query-friendly

Answer: B,C

NEW QUESTION # 48
Which technique is considered a primary defense against SQL injection when executing database queries from Python?

A. Using parameterized queries
B. Escaping quotes manually in SQL strings
C. Closing the database cursor after each query
D. Encrypting the database connection

Answer: A

NEW QUESTION # 49
Which operation would most efficiently apply element-wise multiplication to two NumPy arrays of equal size?

A. array1.dot(array2)
B. numpy.matrix()
C. array1 * array2
D. numpy.outer()

Answer: C

NEW QUESTION # 50
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