Python Institute PCED-30-01 問題練習

Question No : 1
How do you comment out a single line of code in Python?

• A.// This is a comment
• B./* This is a comment */
• C.# This is a comment

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Question No : 2
What does the 'split()' method do when called on a string in Python?

• A.Joins multiple strings together
• B.Splits a string into a list based on a specified delimiter
• C.Reverses the characters in a string
• D.Removes all spaces from a string

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Question No : 3
In Python, how would you access the value '42' in a dictionary that has the key 'answer'?

• A.dictionary['42']
• B.dictionary.get('answer')
• C.dictionary.answer
• D.dictionary['answer']

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Question No : 4
What does the 'range()' function in Python do?

• A.Generates a random number
• B.Creates a list of integers starting from 0 up to a specified number
• C.Checks if a condition is satisfied
• D.Converts a string to a list

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Question No : 5
Which of the following is NOT a valid data type in Python?

• A.String
• B.Boolean
• C.Character
• D.Float

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Question No : 6
Which of the following techniques can be used for outlier detection in data pre-processing?

• A.Data imputation
• B.Standard deviation method
• C.Mean replacement
• D.Data normalization

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Question No : 7
What is the significance of data profiling in data pre-processing?

• A.Evaluating data quality and completeness
• B.Performing advanced statistical analysis
• C.Converting data types
• D.Visualizing data patterns

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Question No : 8
Which of the following is a typical challenge faced during data acquisition?

• A.Data cleaning
• B.Data visualization
• C.Data modeling
• D.Data inconsistency

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Question No : 9
What is the purpose of data integration in the data acquisition process?

• A.Removing irrelevant data
• B.Combining data from multiple sources
• C.Sorting data into categories
• D.Performing statistical analysis on the data

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Question No : 10
Which of the following is an example of data transformation in pre-processing?

• A.Converting categorical variables into numerical ones
• B.Removing outliers from the dataset
• C.Calculating the mean and standard deviation
• D.Visualizing correlation between variables

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Question No : 11
In data pre-processing, what is the purpose of data cleaning?

• A.Removing missing values and duplicates
• B.Transforming data into a usable format
• C.Calculating summary statistics
• D.Visualizing data patterns

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Question No : 12
Which of the following is NOT a common method for data acquisition?

• A.Web scraping
• B.Data preprocessing
• C.API integration
• D.Database querying

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Question No : 13
What is the purpose of data acquisition in the context of data analytics?

• A.Collecting data from various sources
• B.Preparing data for visualization
• C.Analyzing data patterns
• D.Presenting data findings

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Question No : 14
Which of the following color schemes is recommended for data visualization to ensure accurate interpretation of data?

• A.Vibrant and random color combinations
• B.A monochromatic color scheme
• C.Neon colors for emphasis
• D.A chaotic mix of colors

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Question No : 15
Which of the following is a key advantage of using dashboards in data visualization?

• A.Dashboards allow for complex data analysis
• B.Dashboards provide real-time data updates
• C.Dashboards help in creating interactive visualizations
• D.Dashboards simplify the presentation of multiple visualizations in one place

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