PCAD-31-02 Interactive Practice Exam & PCAD-31-02 Practical Information

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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. Choosing appropriate chart types 2. Color, layout and clarity 3. Audience-focused presentation - Visualization with Matplotlib and Seaborn 1. Customization and styling 2. Line, bar, scatter, histogram, box plots 3. Heatmaps, pair plots and correlation matrices
Topic 2: SQL and Database Integration	10%	- SQL querying 1. Subqueries and filtering 2. SELECT, WHERE, JOIN, GROUP BY, aggregate functions - Python-database connectivity 1. Connecting to SQLite, MySQL or PostgreSQL 2. Executing queries and retrieving results 3. Error handling and best practices - Relational database concepts 1. Tables, keys, relationships and normalization
Topic 3: Data Acquisition and Preprocessing	30%	- Data cleaning and validation 1. Handling missing, duplicate and invalid values 2. Data standardization and transformation 3. Quality assurance and validation techniques - Data collection, integration and storage 1. Data integration and merging 2. Data collection methods and sources 3. Data formats and storage systems - Data preparation with Pandas and NumPy 1. Data structures: Series, DataFrame, ndarray 2. Indexing, filtering, sorting and grouping 3. Data reshaping and aggregation
Topic 4: Data Exploration and Statistical Analysis	25%	- Descriptive statistics 1. Measures of central tendency and dispersion 2. Correlation and covariance analysis 3. Frequency distributions and percentiles - Exploratory data analysis 1. Feature selection and dimensionality reduction basics 2. Identifying patterns, trends and outliers - Inferential statistics 1. Hypothesis testing and confidence intervals 2. Probability concepts and distributions 3. Statistical significance and interpretation
Topic 5: Data Modeling and Machine Learning Basics	20%	- Model performance and optimization 1. Hyperparameter tuning basics 2. Overfitting, underfitting and generalization 3. Accuracy, precision, recall, F1-score - Supervised learning fundamentals 1. Model training, testing and evaluation 2. Classification: logistic regression, k-NN, decision trees 3. Regression: linear, multiple, polynomial

>> PCAD-31-02 Interactive Practice Exam <<

Free PDF Quiz Python Institute - PCAD-31-02 –Valid Interactive Practice Exam

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q40-Q45):

NEW QUESTION # 40
What is the primary reason for converting all categorical labels to lowercase during the data cleaning process?

A. To make string comparison operations slower
B. To improve data visualization aesthetics
C. To avoid treating the same category as different due to case differences
D. To reduce memory usage in the dataset

Answer: C

NEW QUESTION # 41
What is the outcome of the following code?
data = [5, 10, 15]
result = [x**2 for x in data if x > 5]
print(result)

A. [25, 100]
B. [100, 225]
C. [25, 100, 225]
D. [10, 15]

Answer: B

NEW QUESTION # 42
What is the primary difference between the == operator and the is operator in Python when used in a data pipeline?

A. is checks value equality, while == checks for data type compatibility
B. == compares values for equality, while is checks for object identity
C. == checks identity, while is compares memory addresses
D. is compares dictionary keys, while == compares keys and values

Answer: B

NEW QUESTION # 43
What is the main reason for using try-except blocks in a data analysis script that reads multiple files?

A. To enforce strict type annotations
B. To automatically restart the script after execution
C. To gracefully handle runtime errors without crashing the program
D. To ensure functions always return None

Answer: C

NEW QUESTION # 44
Which statements correctly describe differences between linear and logistic regression?
(Choose two)

A. Logistic regression uses the logit function to model relationships
B. Linear regression requires target variables to be binary
C. Logistic regression outputs probabilities, while linear regression predicts continuous values
D. Linear regression applies only to classification tasks

Answer: A,C

NEW QUESTION # 45
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