有効的なPython Institute PCAD-31-02模擬試験問題集 &プロフェッショナルXhs1991 -資格試験におけるリーダーオファー

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Python Institute PCAD-31-02 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Exploration and Statistical Analysis	25%	- 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 - Exploratory data analysis 1. Identifying patterns, trends and outliers 2. Feature selection and dimensionality reduction basics
Topic 2: SQL and Database Integration	10%	- Python-database connectivity 1. Error handling and best practices 2. Executing queries and retrieving results 3. Connecting to SQLite, MySQL or PostgreSQL - Relational database concepts 1. Tables, keys, relationships and normalization - SQL querying 1. SELECT, WHERE, JOIN, GROUP BY, aggregate functions 2. Subqueries and filtering
Topic 3: 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. Hyperparameter tuning basics 2. Accuracy, precision, recall, F1-score 3. Overfitting, underfitting and generalization
Topic 4: Data Visualization and Communication	15%	- Data storytelling and reporting 1. Written and verbal presentation techniques 2. Structuring insights and conclusions - Visualization with Matplotlib and Seaborn 1. Heatmaps, pair plots and correlation matrices 2. Line, bar, scatter, histogram, box plots 3. Customization and styling - Visualization principles and best practices 1. Choosing appropriate chart types 2. Color, layout and clarity 3. Audience-focused presentation
Topic 5: Data Acquisition and Preprocessing	30%	- Data cleaning and validation 1. Data standardization and transformation 2. Quality assurance and validation techniques 3. Handling missing, duplicate and invalid values - Data collection, integration and storage 1. Data integration and merging 2. Data formats and storage systems 3. Data collection methods and sources - Data preparation with Pandas and NumPy 1. Data reshaping and aggregation 2. Indexing, filtering, sorting and grouping 3. Data structures: Series, DataFrame, ndarray

>> PCAD-31-02模擬試験問題集 <<

PCAD-31-02トレーニング、PCAD-31-02最新資料

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) 認定 PCAD-31-02 試験問題 (Q47-Q52):

質問 # 47
Which actions are valid techniques for handling erroneous categorical values in a dataset?
(Choose two)

A. Converting all values to integers
B. Replacing inconsistent labels with a standardized value
C. Normalizing using min-max scaling
D. Removing rows with invalid labels

正解：B、D

質問 # 48
In supervised learning, what is the primary goal when training a model on labeled data?

A. To cluster data points into similar groups
B. To learn a function that maps inputs to known outputs
C. To analyze unstructured data formats like text
D. To discover hidden patterns without outputs

正解：B

質問 # 49
Which of the following is the most suitable data structure in Python for representing a tabular row with column names?

A. list
B. tuple
C. dictionary
D. set

正解：C

質問 # 50
Which features are commonly supported by both Matplotlib and Seaborn for customizing visualizations?
(Choose two)

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

正解：A、C

質問 # 51
Which Python feature allows the reuse of logic in different data processing steps, reducing duplication and improving maintainability?

A. Lambda functions
B. Global variables
C. Tuple unpacking
D. Custom functions

正解：D

質問 # 52
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