Exam PCAD-31-02 Cram & PCAD-31-02 Exam Score

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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 - Data storytelling and reporting 1. Structuring insights and conclusions 2. Written and verbal presentation techniques - Visualization principles and best practices 1. Color, layout and clarity 2. Choosing appropriate chart types 3. Audience-focused presentation
Topic 2: Data Exploration and Statistical Analysis	25%	- Descriptive statistics 1. Correlation and covariance analysis 2. Frequency distributions and percentiles 3. Measures of central tendency and dispersion - Exploratory data analysis 1. Identifying patterns, trends and outliers 2. Feature selection and dimensionality reduction basics - Inferential statistics 1. Probability concepts and distributions 2. Statistical significance and interpretation 3. Hypothesis testing and confidence intervals
Topic 3: 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. Connecting to SQLite, MySQL or PostgreSQL 2. Error handling and best practices 3. Executing queries and retrieving results
Topic 4: 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. Regression: linear, multiple, polynomial 3. Classification: logistic regression, k-NN, decision trees
Topic 5: Data Acquisition and Preprocessing	30%	- 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 cleaning and validation 1. Data standardization and transformation 2. Quality assurance and validation techniques 3. Handling missing, duplicate and invalid values

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q93-Q98):

NEW QUESTION # 93
What is the main purpose of using the bootstrapping technique in inferential statistics?

A. To estimate the sampling distribution by resampling with replacement
B. To validate the syntax of SQL queries in Python
C. To filter outliers from a dataset
D. To increase the number of features in a model

Answer: A

NEW QUESTION # 94
Which of the following would best validate the uniqueness of a primary key in a Pandas DataFrame?

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

Answer: B

NEW QUESTION # 95
What is the primary role of the pandas library in the Python data science ecosystem?

A. Manipulating labeled data structures
B. Performing statistical tests
C. Visualizing large datasets
D. Reducing execution time of compiled code

Answer: A

NEW QUESTION # 96
Which operation would most efficiently apply element-wise multiplication to two NumPy arrays of equal size?

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

Answer: A

NEW QUESTION # 97
Which features are commonly supported by both Matplotlib and Seaborn for customizing visualizations?
(Choose two)

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

Answer: A,D

NEW QUESTION # 98
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