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Strengthen Your Preparation with the Newest Python Institute PCED-30-01 Dumps (V8.02) from DumpsBase

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You can strengthen your exam preparation with the newest Python Institute PCED-30-01 dumps (V8.02) from DumpsBase. The PCED-30-01 dumps are designed to mirror the actual exam format, reflecting the logic, depth, and complexity of what you'll encounter in the Certified Entry-Level Data Analyst with Python exam. By working through the newest questions and answers in the PCED-30-01 dumps, you'll strengthen your understanding of test objectives while identifying weak areas. Choose DumpsBase and pass your PCED-30-01 exam smoothly. #PCED-30-01

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DUMPS
BASE
EXAM DUMPS

PYTHON INSTITUTE
PCED-30-01
28% OFF Automatically For You

Certified Entry-Level Data Analyst with Python

,1.In the context of data acquisition, what does ETL stand for?
A. Extract, Transform, Load
B. Extract, Transfer, Load
C. Edit, Transform, Load
D. Enhance, Transform, Load
Answer: A


2.Which of the following is NOT a commonly used data acquisition method?
A. Web scraping
B. API integration
C. Data visualization
D. Manual data entry




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Answer: C




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3.What is the purpose of data pre-processing in a data analytics workflow?




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A. To make the data more difficult to analyze


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B. To remove noise and irrelevant information
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C. To increase the computational complexity
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D. To slow down the data analysis process
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Answer: B
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4.Which of the following techniques can be used in data pre-processing to handle
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B. Replacing missing values with the mean of the column
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C. Ignoring missing values altogether
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D. Filling missing values with zeros
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Answer: B
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5.What is outlier detection in the context of data pre-processing?
A. Identifying extreme values or anomalies in the data
B. Highlighting positive trends in the data
C. Analyzing data to predict future outcomes
D. Sorting data based on a predefined criteria
Answer: A


6.Which of the following is NOT an example of data cleaning in the pre-processing
stage?

, A. Removing duplicate entries
B. Converting categorical data to numerical values
C. Handling missing values
D. Aggregating data from multiple sources without validation
Answer: D


7.Why is data normalization important in data pre-processing?
A. It helps in increasing the size of the dataset
B. It ensures that all features have the same scale
C. It makes the data difficult to interpret
D. It slows down the data analysis process
Answer: B




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8.Which of the following techniques can be used in data pre-processing for feature




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A. Principal Component Analysis (PCA)


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B. Support Vector Machines (SVM)
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C. k-Nearest Neighbors (k-NN)
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D. Linear Regression
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Answer: A
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9.What does the "len()" function in Python do?
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A. Returns the sum of all elements in a list
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B. Returns the average of elements in a list
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C. Returns the number of elements in a list
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D. Returns the maximum value in a list
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Answer: C
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10.What is the correct way to comment a single line of code in Python?
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A. // This is a comment
B. /* This is a comment */
C. # This is a comment
D.
Answer: C


11.Which of the following is used to perform integer division in Python?
A. /
B. //
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