Quiz PCAD-31-02 - Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Useful Valid Braindumps

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Python Institute PCAD-31-02 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Visualization and Communication	15%	- Visualization with Matplotlib and Seaborn 1. Line, bar, scatter, histogram, box plots 2. Customization and styling 3. Heatmaps, pair plots and correlation matrices - Visualization principles and best practices 1. Choosing appropriate chart types 2. Color, layout and clarity 3. Audience-focused presentation - Data storytelling and reporting 1. Structuring insights and conclusions 2. Written and verbal presentation techniques
Topic 2: 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. Overfitting, underfitting and generalization 2. Accuracy, precision, recall, F1-score 3. Hyperparameter tuning basics
Topic 3: SQL and Database Integration	10%	- Relational database concepts 1. Tables, keys, relationships and normalization - 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. Subqueries and filtering 2. SELECT, WHERE, JOIN, GROUP BY, aggregate functions
Topic 4: Data Acquisition and Preprocessing	30%	- Data collection, integration and storage 1. Data formats and storage systems 2. Data integration and merging 3. Data collection methods and sources - Data preparation with Pandas and NumPy 1. Indexing, filtering, sorting and grouping 2. Data reshaping and aggregation 3. Data structures: Series, DataFrame, ndarray - Data cleaning and validation 1. Data standardization and transformation 2. Handling missing, duplicate and invalid values 3. Quality assurance and validation techniques
Topic 5: Data Exploration and Statistical Analysis	25%	- Descriptive statistics 1. Correlation and covariance analysis 2. Measures of central tendency and dispersion 3. Frequency distributions and percentiles - Exploratory data analysis 1. Identifying patterns, trends and outliers 2. Feature selection and dimensionality reduction basics - Inferential statistics 1. Probability concepts and distributions 2. Statistical significance and interpretation 3. Hypothesis testing and confidence intervals

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q17-Q22):

NEW QUESTION # 17
Why is it important to use a test dataset separate from the training dataset when evaluating a machine learning model?

A. To evaluate the model's performance on unseen data
B. To ensure the loss function is minimized
C. To optimize the model's training accuracy
D. To reduce the number of iterations during training

Answer: A

NEW QUESTION # 18
Which operations are recommended when organizing messy tabular data in Pandas for further transformation and statistical modeling?
(choose two)

A. Resetting the index after filtering
B. Dropping column headers
C. Ensuring consistent data types
D. Converting the DataFrame to a Series

Answer: A,C

NEW QUESTION # 19
Which refinements are typically used to enhance clarity and presentation quality in visualizations?
(Choose two)

A. Disabling grid lines in all cases
B. Adding descriptive axis labels
C. Customizing tick labels
D. Avoiding color entirely

Answer: B,C

NEW QUESTION # 20
Which characteristics are typically associated with supervised learning algorithms?
(Choose two)

A. They work only with numerical features
B. They are useful for regression and classification tasks
C. They require labeled training data
D. They automatically detect outliers

Answer: B,C

NEW QUESTION # 21
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: B

NEW QUESTION # 22
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