Python Institute Entry-Level Data Analyst with Python (PCED) Certification Sample Questions

Entry-Level Data Analyst with Python Dumps, PCED Dumps, PCED PDF, Entry-Level Data Analyst with Python VCE, Python Institute PCED VCE, Python Institute PCED-30-01 PDFThe purpose of this Sample Question Set is to provide you with information about the Python Institute Certified Entry-Level Data Analyst with Python exam. These sample questions will make you very familiar with both the type and the difficulty level of the questions on the PCED certification test. To get familiar with real exam environment, we suggest you try our Sample Python Institute Entry-Level Data Analyst with Python Certification Practice Exam. This sample practice exam gives you the feeling of reality and is a clue to the questions asked in the actual Python Institute Certified Entry-Level Data Analyst with Python (PCED) certification exam.

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Python Institute PCED Sample Questions:

01. You have a dataset where the standard deviation is very high. What does this indicate about the data?
a) The data is normally distributed.
b) The data points are widely spread out.
c) There are no outliers in the dataset.
d) The dataset has many duplicate values.
 
02. Which are differences between DataFrame and Series in Pandas?
(Select three.)
a) A DataFrame has multiple columns; a Series has one.
b) A Series is one-dimensional; a DataFrame is two-dimensional.
c) A Series can store only integers.
d) A DataFrame uses row and column indices; a Series uses a single index.
e) A Series cannot store strings.
 
03. Which library is best suited for creating advanced statistical visualizations in Python?
a) NumPy
b) Matplotlib
c) Seaborn
d) Pandas
 
04. A client requests a summary of product sales grouped by category. Which Pandas function should you use?
a) aggregate()
b) groupby()
c) sort_values()
d) pivot_table()
 
05. You need to calculate the sum of all even numbers in a list numbers. Which code snippet accomplishes this?
a) Edit
total = sum([num for num in numbers if num % 2 == 0])
b) Edit
total = sum(num for num in numbers if num % 2 != 0)
c) Edit
total = sum([num for num in numbers if num > 2])
d) Edit
total = sum(numbers)
 
06. What is the primary purpose of data normalization in data analysis?
a) To identify outliers in the dataset.
b) To ensure consistent file formatting.
c) To remove missing values from the dataset.
d) To scale variables for easier comparison.
 
07. Your Python program frequently fails when connecting to an external API. What can you do to improve reliability?
a) Use exception handling to retry the connection.
b) Increase the API response timeout.
c) Remove error handling to identify the root cause.
d) Execute API calls in a loop without exceptions.
 
08. Which data storage solution is ideal for managing large volumes of unstructured data?
a) Data warehouse
b) Data lake
c) Relational database
d) Spreadsheet
 
09. Which of the following are NumPy aggregation functions?
​(Select three.)
a) np.sum()
b) np.mean()
c) np.max()
d) np.merge()
e) np.split()
 
10. What does a positive Pearson’s correlation coefficient (r) indicate?
a) No relationship between variables
b) A weak negative correlation
c) A strong positive correlation
d) An inverse relationship

Answers:

Question: 01
Answer: b		 Question: 02
Answer: a, b, d		 Question: 03
Answer: c		 Question: 04
Answer: b		 Question: 05
Answer: a
Question: 06
Answer: d		 Question: 07
Answer: a		 Question: 08
Answer: b		 Question: 09
Answer: a, b, c		 Question: 10
Answer: c

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