PCAD-31-02 Test Cram - PCAD-31-02 Free Learning Cram

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Python Institute PCAD-31-02 Exam Syllabus Topics:

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

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q138-Q143):

NEW QUESTION # 138
Which type of chart is most appropriate when comparing the distribution of a numerical variable across multiple categories?

A. Box plot
B. Line chart
C. Pie chart
D. Scatter plot

Answer: A

NEW QUESTION # 139
What is a major challenge in aggregating data from multiple sources?
Response:

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

Answer: B

NEW QUESTION # 140
When working with relational databases in Python, what are key components required to establish and manage database connections?
(Choose all that apply)

A. Cursor object for executing queries
B. Database driver or module (e.g., sqlite3)
C. Python's random module for generating keys
D. Connection object to interface with the database

Answer: B,D

NEW QUESTION # 141
Which method is commonly used to handle missing numerical data in a dataset?

A. Replacing with text labels
B. Dropping all columns
C. Multiplying all missing values by zero
D. Filling with the mean or median

Answer: D

NEW QUESTION # 142
Which method is used after grouping a DataFrame to compute the average value for each group in a specific column?

A. groupby().count()
B. groupby().aggregate()
C. groupby().max()
D. groupby().mean()

Answer: D

NEW QUESTION # 143
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