Mock PCAD-31-02 Exams - PCAD-31-02 Real Brain Dumps

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Python Institute PCAD-31-02 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: SQL and Database Integration	10%	- Relational database concepts 1. Tables, keys, relationships and normalization - SQL querying 1. Subqueries and filtering 2. SELECT, WHERE, JOIN, GROUP BY, aggregate functions - Python-database connectivity 1. Executing queries and retrieving results 2. Connecting to SQLite, MySQL or PostgreSQL 3. Error handling and best practices
Topic 2: Data Visualization and Communication	15%	- Visualization with Matplotlib and Seaborn 1. Customization and styling 2. Line, bar, scatter, histogram, box plots 3. Heatmaps, pair plots and correlation matrices - 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. Audience-focused presentation 3. Choosing appropriate chart types
Topic 3: Data Modeling and Machine Learning Basics	20%	- Model performance and optimization 1. Hyperparameter tuning basics 2. Accuracy, precision, recall, F1-score 3. Overfitting, underfitting and generalization - Supervised learning fundamentals 1. Model training, testing and evaluation 2. Regression: linear, multiple, polynomial 3. Classification: logistic regression, k-NN, decision trees
Topic 4: Data Acquisition and Preprocessing	30%	- Data cleaning and validation 1. Quality assurance and validation techniques 2. Handling missing, duplicate and invalid values 3. Data standardization and transformation - 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
Topic 5: Data Exploration and Statistical Analysis	25%	- Exploratory data analysis 1. Feature selection and dimensionality reduction basics 2. Identifying patterns, trends and outliers - Inferential statistics 1. Statistical significance and interpretation 2. Hypothesis testing and confidence intervals 3. Probability concepts and distributions - Descriptive statistics 1. Frequency distributions and percentiles 2. Correlation and covariance analysis 3. Measures of central tendency and dispersion

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q63-Q68):

NEW QUESTION # 63
Which data collection method is most appropriate when gathering opinions from a target audience at scale?

A. Surveys
B. Web scraping
C. Transactional data
D. Sensor logs

Answer: A

NEW QUESTION # 64
Which of the following techniques are commonly used in the standardization of textual or categorical data during preprocessing?
(Choose two)

A. Applying logarithmic transformation to skewed data
B. Trimming whitespace from string values
C. Scaling numeric features to a fixed range
D. Replacing misspellings with a canonical category label

Answer: B,D

NEW QUESTION # 65
What is the function of the melt() operation in Pandas when reshaping wide-format data?

A. Converts column values into rows
B. Sorts the DataFrame based on a column
C. Transposes the DataFrame
D. Combines multiple DataFrames

Answer: A

NEW QUESTION # 66
Which approaches are commonly used to extract structured data from APIs in Python?
(Choose two)

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

Answer: A,B

NEW QUESTION # 67
Which Python library is most commonly used to establish a connection to a SQLite database and perform SQL operations?

A. sqlite3
B. pymysql
C. psycopg2
D. sqlalchemy

Answer: A

NEW QUESTION # 68
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