High Python Institute PCAD-31-02 Quality & Free PCAD-31-02 Dumps

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Python Institute PCAD-31-02 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: 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 - Inferential statistics 1. Statistical significance and interpretation 2. Probability concepts and distributions 3. Hypothesis testing and confidence intervals - Exploratory data analysis 1. Identifying patterns, trends and outliers 2. Feature selection and dimensionality reduction basics
Topic 2: 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. Model training, testing and evaluation 3. Classification: logistic regression, k-NN, decision trees
Topic 3: 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. Audience-focused presentation 3. Color, layout and clarity - Visualization with Matplotlib and Seaborn 1. Customization and styling 2. Heatmaps, pair plots and correlation matrices 3. Line, bar, scatter, histogram, box plots
Topic 4: SQL and Database Integration	10%	- Python-database connectivity 1. Executing queries and retrieving results 2. Connecting to SQLite, MySQL or PostgreSQL 3. Error handling and best practices - SQL querying 1. SELECT, WHERE, JOIN, GROUP BY, aggregate functions 2. Subqueries and filtering - Relational database concepts 1. Tables, keys, relationships and normalization
Topic 5: Data Acquisition and Preprocessing	30%	- Data preparation with Pandas and NumPy 1. Data structures: Series, DataFrame, ndarray 2. Data reshaping and aggregation 3. Indexing, filtering, sorting and grouping - Data collection, integration and storage 1. Data integration and merging 2. Data formats and storage systems 3. Data collection methods and sources - Data cleaning and validation 1. Data standardization and transformation 2. Handling missing, duplicate and invalid values 3. Quality assurance and validation techniques

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q43-Q48):

NEW QUESTION # 43
What is the most appropriate way to ensure that a column used as a foreign key contains only valid references to a parent DataFrame in Pandas?

A. Use .isin() to compare the foreign key column against the parent key column
B. Use df.sort_values() to sort both columns before merging
C. Use df.merge() with how='outer'
D. Use df.dropna() on the foreign key column

Answer: A

NEW QUESTION # 44
Which SQL command should a data analyst use to modify existing records in a database table based on specific conditions?

A. SELECT
B. INSERT
C. UPDATE
D. CREATE

Answer: C

NEW QUESTION # 45
Which approaches are commonly used to extract structured data from APIs in Python?
(Choose two)

A. Load CSV directly with re.compile()
B. Use the requests module to retrieve JSON responses
C. Access data using requests.get() and parse it with .json()
D. Open Excel files using matplotlib

Answer: B,C

NEW QUESTION # 46
What distinguishes structured data from unstructured data in the context of data modeling and storage?

A. Structured data follows a consistent schema or format
B. Structured data cannot be stored in databases
C. Unstructured data contains only numeric values
D. Unstructured data cannot be stored or analyzed

Answer: A

NEW QUESTION # 47
What is the primary reason for converting all categorical labels to lowercase during the data cleaning process?

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

Answer: A

NEW QUESTION # 48
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