PCAD-31-02 Real Braindumps & PCAD-31-02 Real Testing Environment

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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. 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. Choosing appropriate chart types 3. Color, layout and clarity - Data storytelling and reporting 1. Written and verbal presentation techniques 2. Structuring insights and conclusions
Topic 2: Data Modeling and Machine Learning Basics	20%	- Supervised learning fundamentals 1. Regression: linear, multiple, polynomial 2. Model training, testing and evaluation 3. Classification: logistic regression, k-NN, decision trees - Model performance and optimization 1. Overfitting, underfitting and generalization 2. Hyperparameter tuning basics 3. Accuracy, precision, recall, F1-score
Topic 3: 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. Subqueries and filtering 2. SELECT, WHERE, JOIN, GROUP BY, aggregate functions
Topic 4: Data Exploration and Statistical Analysis	25%	- Descriptive statistics 1. Measures of central tendency and dispersion 2. Frequency distributions and percentiles 3. Correlation and covariance analysis - Exploratory data analysis 1. Feature selection and dimensionality reduction basics 2. Identifying patterns, trends and outliers - Inferential statistics 1. Probability concepts and distributions 2. Statistical significance and interpretation 3. Hypothesis testing and confidence intervals
Topic 5: Data Acquisition and Preprocessing	30%	- Data preparation with Pandas and NumPy 1. Data reshaping and aggregation 2. Data structures: Series, DataFrame, ndarray 3. Indexing, filtering, sorting and grouping - Data collection, integration and storage 1. Data integration and merging 2. Data collection methods and sources 3. Data formats and storage systems - Data cleaning and validation 1. Quality assurance and validation techniques 2. Handling missing, duplicate and invalid values 3. Data standardization and transformation

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q140-Q145):

NEW QUESTION # 140
What is a key advantage of using the Parquet file format over CSV in large-scale data pipelines?

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

Answer: D

NEW QUESTION # 141
When analyzing a sales dataset using Pandas, which of the following techniques help extract key insights by grouping and summarizing the data?
(Choose two)

A. Using rename() to change column headers
B. Applying agg() to calculate multiple metrics like mean and max
C. Using fillna() to replace missing product prices
D. Using groupby() to compute total sales per region

Answer: B,D

NEW QUESTION # 142
What is the main purpose of defining a class when working with complex data structures in a Python-based analysis project?

A. To define global variables that persist across files
B. To encapsulate related data and behavior for better organization
C. To avoid using loops and conditional logic
D. To ensure all values are stored as plain strings

Answer: B

NEW QUESTION # 143
Which metric is most appropriate for evaluating the accuracy of a classification model?

A. R-squared
B. Confusion matrix
C. Mean squared error
D. Precision

Answer: D

NEW QUESTION # 144
What is a major challenge in aggregating data from multiple sources?
Response:

A. Overfitting in predictive models
B. Excessive disk space usage
C. Increased algorithmic complexity
D. Duplicate records and format inconsistencies

Answer: D

NEW QUESTION # 145
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