試験の準備方法-最新のPCAD-31-02試験関連赤本試験-高品質なPCAD-31-02関連日本語版問題集

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Python Institute PCAD-31-02 Exam Syllabus Topics:

Section	Weight	Objectives
Data Acquisition and Preprocessing	30%	- 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 collection methods and sources 3. Data formats and storage systems - Data cleaning and validation 1. Data standardization and transformation 2. Quality assurance and validation techniques 3. Handling missing, duplicate and invalid values
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 - Exploratory data analysis 1. Identifying patterns, trends and outliers 2. Feature selection and dimensionality reduction basics - Inferential statistics 1. Statistical significance and interpretation 2. Hypothesis testing and confidence intervals 3. Probability concepts and distributions
Data Modeling and Machine Learning Basics	20%	- Supervised learning fundamentals 1. Classification: logistic regression, k-NN, decision trees 2. Regression: linear, multiple, polynomial 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
Data Visualization and Communication	15%	- Visualization principles and best practices 1. Choosing appropriate chart types 2. Audience-focused presentation 3. Color, layout and clarity - Data storytelling and reporting 1. Structuring insights and conclusions 2. Written and verbal presentation techniques - Visualization with Matplotlib and Seaborn 1. Heatmaps, pair plots and correlation matrices 2. Customization and styling 3. Line, bar, scatter, histogram, box plots
SQL and Database Integration	10%	- Relational database concepts 1. Tables, keys, relationships and normalization - Python-database connectivity 1. Executing queries and retrieving results 2. Error handling and best practices 3. Connecting to SQLite, MySQL or PostgreSQL - SQL querying 1. SELECT, WHERE, JOIN, GROUP BY, aggregate functions 2. Subqueries and filtering

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) 認定 PCAD-31-02 試験問題 (Q57-Q62):

質問 # 57
Which practices are considered part of professional Python scripting standards?
(Choose two)

A. Using consistent indentation and naming conventions
B. Avoiding error handling to keep code clean
C. Avoiding use of modules and libraries
D. Including in-line comments and docstrings

正解：A、D

質問 # 58
Which term best describes the process of combining customer data from multiple systems into a single unified dataset?

A. Data normalization
B. Data integration
C. Data binning
D. Data warehousing

正解：B

質問 # 59
Which approaches are commonly used to extract structured data from APIs in Python?
(Choose two)

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

正解：B、D

質問 # 60
In a scatter plot showing the relationship between advertising spend and sales revenue, the points form a tight upward-sloping pattern. What conclusion can be drawn?

A. There is a strong positive correlation
B. Advertising has no effect on sales
C. The correlation is weak and likely negative
D. The data shows no apparent relationship

正解：A

質問 # 61
When working with relational databases in Python, what are key components required to establish and manage database connections?
(Choose all that apply)

A. Connection object to interface with the database
B. Python's random module for generating keys
C. Cursor object for executing queries
D. Database driver or module (e.g., sqlite3)

正解：A、D

質問 # 62
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