PCAD-31-02 Exam Guide
This PCAD-31-02 exam focuses on practical knowledge and real-world application scenarios related to the subject area. It evaluates your ability to understand core concepts, apply best practices, and make informed decisions in realistic situations rather than relying solely on memorization.
This page provides a structured exam guide, including exam focus areas, skills measured, preparation recommendations, and practice questions with explanations to support effective learning.
Exam Overview
The PCAD-31-02 exam typically emphasizes how concepts are used in professional environments, testing both theoretical understanding and practical problem-solving skills.
Skills Measured
- Understanding of core concepts and terminology
- Ability to apply knowledge to practical scenarios
- Analysis and evaluation of solution options
- Identification of best practices and common use cases
Preparation Tips
Successful candidates combine conceptual understanding with hands-on practice. Reviewing measured skills and working through scenario-based questions is strongly recommended.
Practice Questions for PCAD-31-02 Exam
The following practice questions are designed to reinforce key PCAD-31-02 exam concepts and reflect common scenario-based decision points tested in the certification.
Question#1
What result will the following Pandas expression return: df['Age'].notnull().all()?
A. Returns True if all values in 'Age' are NULL
B. Returns True if at least one value in 'Age' is not NULL
C. Returns True if all values in 'Age' are not NULL
D. Returns the number of NULL values in 'Age'
Question#2
Why is it important to adjust data presentations based on the audience's background?
A. To avoid using charts altogether
B. To simplify all metrics to percentages only
C. To ensure the data is understood and supports actionable insights
D. To include as many technical terms as possible
Question#4
Which approaches are commonly used to extract structured data from APIs in Python? (Choose two)
A. Use the requests module to retrieve JSON responses
B. Open Excel files using matplotlib
C. Access data using requests.get() and parse it with .json()
D. Load CSV directly with re.compile()
Disclaimer
This page is for educational and exam preparation reference only. It is not affiliated with Python Institute, Data Science, or the official exam provider. Candidates should refer to official documentation and training for authoritative information.
