PCED-30-02 Exam Simulations | Study PCED-30-02 Reference

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| Section | Objectives |
|---|
| Data Handling and Processing with Python | - Working with data structures
- 1. Lists, tuples, dictionaries
- 2. Iterating and transformations
- Data manipulation libraries
- 1. NumPy basics
- 2. Pandas fundamentals
|
| Python Programming Fundamentals | - Basic syntax and data types
- 1. Variables and operators
- 2. Control flow basics
|
| Data Analysis and Visualization | - Data visualization
- 1. Plot interpretation
- 2. Matplotlib basics
- Data cleaning and preprocessing
- 1. Missing values handling
- 2. Data transformation techniques
|
| Basic Statistics for Data Analysis | - Descriptive statistics
- 1. Variance and standard deviation
- 2. Mean, median, mode
|
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Python Institute PCED - Certified Entry-Level Data Analyst with Python Sample Questions (Q32-Q37):
NEW QUESTION # 32
A Python script includes the expression not False and True. The developer is unsure how logical operators are evaluated in terms of precedence. What will be the final Boolean result of this expression?
- A. True
- B. Error
- C. None
- D. False
Answer: A
Explanation:
The not operator has higher precedence than and, so not False becomes True first. Then True and True is evaluated, resulting in True as the final Boolean value.
NEW QUESTION # 33
You are working with city names entered by users. These names may contain inconsistent capitalization and unwanted spaces.
To standardize the data, you want to:
- Remove any leading or trailing whitespace, and
- Capitalize the first letter of each word (e.g., convert "new york" to "New York").
For example:
" New york " → "New York"
"lOS ANGELES" → "Los Angeles"
You are given a variable citythat contains the raw input.
Which line of code correctly updates the value of cleaned_cityto apply the required transformation? Select the best answer.
- A. cleaned_city = city.strip().title()
- B. cleaned_city = city.upper().strip()
- C. cleaned_city = city.upper().replace(" ", "")
- D. cleaned_city = city.strip().capitalize()
Answer: A
Explanation:
Stripping removes leading and trailing whitespace, and applying title formatting capitalizes the first letter of each word while converting the remaining letters to lowercase, producing the correctly standardized city name.
NEW QUESTION # 34
You want to safely read the contents of a file named records.txtand store them in a list. Your requirements are:
- The file should be automatically closed after reading,
- The code should check whether the file exists before opening it, and
- It should handle missing file errors gracefully.
Which of the following is the most appropriate way to accomplish this task in Python? Select the best answer.
Answer: C
Explanation:
It uses a context manager to automatically close the file after reading and wraps the operation in a try-except block to gracefully handle a missing file error, assigning an empty list if the file is not found.
NEW QUESTION # 35
A loop includes a break statement when a condition is met. The programmer wants to understand how break affects loop execution. What happens when break is executed inside the loop?
- A. Skips one iteration
- B. Loop restarts
- C. Loop ends immediately
- D. Error
Answer: C
Explanation:
The break statement immediately terminates the loop, regardless of remaining iterations. Control is transferred to the first statement following the loop block.
NEW QUESTION # 36
Which task is traditionally or typically performed by a data analyst, and not by a data scientist or analytics specialist? Select the best answer.
- A. Recommending pricing strategies using predictive modeling and A/B testing.
- B. Creating summary tables and charts to describe patterns in historical data.
- C. Developing machine learning algorithms to predict user behavior.
- D. Designing a data pipeline to ingest and process real-time sensor data.
Answer: B
Explanation:
Creating summary tables and charts to describe patterns in historical data is a core responsibility of a data analyst, focusing on descriptive analytics and reporting rather than building predictive models, machine learning systems, or engineering data pipelines.
NEW QUESTION # 37
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