PDF Version DemoPython Institute PCAD-31-02 Exam Overview:
| Certification Vendor: | Python Institute |
|---|---|
| Exam Name: | Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) |
| Exam Number: | PCAD-31-02 |
| Passing Score: | 70% |
| Exam Format: | Multiple Choice, Fill in the Blank, Code Completion, Multiple Select |
| Related Certifications: | PCAP - Certified Associate in Python Programming PCEP - Certified Entry-Level Python Programmer |
| Real Exam Qty: | 40 |
| Certificate Validity Period: | No expiration (lifetime validity) |
| Exam Price: | $295 USD |
| Exam Duration: | 65 minutes |
| Available Languages: | English |
| Sample Questions: | Python Institute PCAD-31-02 Sample Questions |
| Exam Way: | Online proctored exam through Pearson VUE testing centers |
| Pre Condition: | PCEP certification is recommended but not required. Basic Python programming knowledge is expected. |
| Official Syllabus URL: | https://pythoninstitute.org/certifications/pcad |
Python Institute PCAD-31-02 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Working with Data Using Python Libraries | 30% | - Pandas Library
|
| Topic 2: Applied Data Analysis Projects | 20% | - Exploratory Data Analysis (EDA)
|
| Topic 3: Data Analysis Fundamentals | 20% | - Introduction to Data Analysis
|
| Topic 4: Python Programming for Data Analysis | 30% | - File Operations
|
Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions:
Question #1
Which operation would most efficiently apply element-wise multiplication to two NumPy arrays of equal size?
A. numpy.outer()
B. array1.dot(array2)
C. array1 * array2
D. numpy.matrix()
Question #2
Which method is best suited to transform inconsistent entries such as "N/A", "missing", and empty strings into a standard missing value representation in a Pandas DataFrame?
A. df.replace()
B. df.rename()
C. df.dropna()
D. df.sort_values()
Question #3
Which practices are considered part of professional Python scripting standards?
(Choose two)
A. Avoiding use of modules and libraries
B. Using consistent indentation and naming conventions
C. Avoiding error handling to keep code clean
D. Including in-line comments and docstrings
Question #4
Which Python feature allows the reuse of logic in different data processing steps, reducing duplication and improving maintainability?
A. Global variables
B. Tuple unpacking
C. Custom functions
D. Lambda functions
Question #5
Which conditions typically necessitate data normalization or scaling before analysis?
(choose two)
A. Presence of duplicate data
B. Features with different units and scales
C. Distance-based modeling algorithms
D. Boolean feature encoding
Solutions:
| Question #1 Correct Answer: C | Question #2 Correct Answer: A | Question #3 Correct Answer: B,D | Question #4 Correct Answer: C | Question #5 Correct Answer: B,C |
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