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WGU D522 CSV, JSON & Data-Validation Quick Reference| With complete solution| Updated RATED A+

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WGU D522 CSV, JSON & Data-Validation Quick Reference| With complete solution| Updated RATED A+ WGU D522 CSV, JSON & Data-Validation Quick Reference| With complete solution| Updated RATED A+ WGU D522 CSV, JSON & Data-Validation Quick Reference| With complete solution| Updated RATED A+ WGU D522 CSV, JSON & Data-Validation Quick Reference| With complete solution| Updated RATED A+

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WGU D522 CSV, JSON & Data-Validation Quick Reference
Safe Parsing, Validation, Troubleshooting & Submission Checklist — 2026/2027 Study Reference


1. CSV QUICK REFERENCE
CSV stands for Comma-Separated Values. It represents tabular data using rows and fields.
StudentID,Name,Program,Score
1001,John Smith,Data Analytics,87
1002,Mary Jones,Cybersecurity,92

Key components: header row, record, field, delimiter, quotation marks, and line breaks.

Important rules: keep column order consistent; quote fields containing delimiters; handle missing values according to
requirements; keep data types consistent.

2. COMMON CSV PROBLEMS
Incorrect column count, incorrect delimiter, unescaped delimiters, missing required values, and inconsistent data
types are common causes of parsing or validation failures.
101,"Smith, John",90

Expected: ID,Name,Score
Problem: 101,John,90,Pass


3. JSON QUICK REFERENCE
JSON (JavaScript Object Notation) is a structured text format commonly used to exchange data between
applications.
{
"student_id": 1001,
"name": "John Smith",
"program": "Data Analytics",
"score": 87
}


4. JSON DATA TYPES
String: "name": "John" Number: "score": 87 Boolean: true/false Null: null. Objects use { }; arrays
use [ ].

5. JSON SYNTAX RULES
Keys normally use double quotation marks. Use commas between properties. Avoid unnecessary trailing commas.
Match every opening brace/bracket with its closing counterpart.

6. NESTED JSON
{
"student": {
"id": 1001,
"name": "John"
},
"courses": ["D522", "D523"]
}


7. CSV VS. JSON
Feature CSV JSON

Structure Tabular Hierarchical

Best suited for Rows/columns Nested/structured data

Nested data Limited Excellent

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