PCAD-31-02 Real Exam - PCAD-31-02 Torrent Vce & PCAD-31-02 Valid Questions

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Python Institute PCAD-31-02 Exam Syllabus Topics:

Section	Weight	Objectives
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 - Data storytelling and reporting 1. Written and verbal presentation techniques 2. Structuring insights and conclusions - Visualization principles and best practices 1. Color, layout and clarity 2. Choosing appropriate chart types 3. Audience-focused presentation
Data Exploration and Statistical Analysis	25%	- 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 - Inferential statistics 1. Hypothesis testing and confidence intervals 2. Probability concepts and distributions 3. Statistical significance and interpretation
Data Acquisition and Preprocessing	30%	- Data cleaning and validation 1. Quality assurance and validation techniques 2. Data standardization and transformation 3. Handling missing, duplicate and invalid values - Data preparation with Pandas and NumPy 1. Data structures: Series, DataFrame, ndarray 2. Indexing, filtering, sorting and grouping 3. Data reshaping and aggregation - Data collection, integration and storage 1. Data formats and storage systems 2. Data collection methods and sources 3. Data integration and merging
SQL and Database Integration	10%	- SQL querying 1. SELECT, WHERE, JOIN, GROUP BY, aggregate functions 2. Subqueries and filtering - 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
Data Modeling and Machine Learning Basics	20%	- Model performance and optimization 1. Accuracy, precision, recall, F1-score 2. Hyperparameter tuning basics 3. Overfitting, underfitting and generalization - Supervised learning fundamentals 1. Regression: linear, multiple, polynomial 2. Classification: logistic regression, k-NN, decision trees 3. Model training, testing and evaluation

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q76-Q81):

NEW QUESTION # 76
What is the key difference between the .iloc[] and .loc[] accessors when working with Series or DataFrames?

A. .loc[] can only be used on Series
B. .iloc[] allows label-based selection
C. .iloc[] is position-based, while .loc[] is label-based
D. .loc[] does not allow slicing

Answer: C

NEW QUESTION # 77
Which file format is most suitable for exchanging large tabular datasets with consistent column data types across systems?

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

Answer: C

NEW QUESTION # 78
Which Pandas methods are commonly used to extract central tendency and dispersion insights from a DataFrame?
(Choose two)

A. median()
B. sort_values()
C. pivot_table()
D. var()

Answer: A,D

NEW QUESTION # 79
Which features are commonly supported by both Matplotlib and Seaborn for customizing visualizations?
(Choose two)

A. Changing plot color palettes
B. Adding plot titles and axis labels
C. Inserting audio annotations
D. Automatic plot animations

Answer: A,B

NEW QUESTION # 80
Which transformation method adjusts the mean of the dataset to 0 and the standard deviation to 1?

A. One-hot encoding
B. Log transformation
C. Min-max normalization
D. Z-score normalization

Answer: D

NEW QUESTION # 81
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