Python Institute Certification PCAD-31-02 pdf braindumps & PCAD-31-02 practice exam test

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

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q68-Q73):

NEW QUESTION # 68
Which of the following best describes the purpose of the plt.subplot() function in Matplotlib?

A. It links Matplotlib with Seaborn's grid styling
B. It overlays multiple plots in the same figure without axes
C. It creates a 3D surface plot using pandas
D. It configures multiple plots in a grid layout within a single figure

Answer: D

NEW QUESTION # 69
Which practices enhance both readability and professionalism in spreadsheet formatting?
(Choose two)

A. Applying consistent font and cell styling
B. Centering all data regardless of type
C. Aligning column headers to the left
D. Using uniform date formats across columns

Answer: A,D

NEW QUESTION # 70
Which of the following correctly demonstrates the use of parameterized queries using Python's sqlite3 module?

A. cursor.execute("SELECT * FROM users WHERE id = ?", (user_id,))
B. cursor.execute("SELECT * FROM users WHERE id = ?", user_id)
C. cursor.execute("SELECT * FROM users WHERE id = ':id'", {'id': user_id})
D. cursor.execute("SELECT * FROM users WHERE id = " + user_id)

Answer: A

NEW QUESTION # 71
Which features are commonly supported by both Matplotlib and Seaborn for customizing visualizations?
(Choose two)

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

Answer: A,C

NEW QUESTION # 72
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'].isnull().sum()
D. df['id'].fillna()

Answer: B

NEW QUESTION # 73
......

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