# Quiz: Taxonomy and Data Formats > Test your understanding of taxonomy categorization, vis-network JSON format, Dublin Core metadata, and Python processing scripts with these questions. - Skill: `tools-only/quiz-taxonomy-and-data-formats` (Agent Skill, multi-file: 3 files) - Install (CLI): `npx skillmds@latest add tools-only/quiz-taxonomy-and-data-formats` - Raw SKILL.md: https://api.skillmd.com/api/skills/tools-only/quiz-taxonomy-and-data-formats/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: tools-only (https://skillmd.com/u/tools-only) - Updated: 2026-09-29 - Page: https://skillmd.com/skills/tools-only/quiz-taxonomy-and-data-formats --- # Quiz: Taxonomy and Data Formats Test your understanding of taxonomy categorization, vis-network JSON format, Dublin Core metadata, and Python processing scripts with these questions. --- #### 1. What is the recommended length for TaxonomyID abbreviations in learning graph CSV files?
1. 1-2 letters for brevity 2. 3-5 letters for balance 3. 6-10 letters for clarity 4. 15+ letters for full descriptiveness
??? question "Show Answer" The correct answer is **B**. TaxonomyID abbreviations should be 3-5 letters, balancing compactness in CSV files and visualizations with sufficient distinctiveness and mnemonics. Option A is too short to be distinctive, while options C and D defeat the purpose of abbreviation and would clutter visualizations. **Concept Tested:** TaxonomyID Abbreviations **See:** [TaxonomyID Abbreviations](../../glossary.md#taxonomyid-abbreviations) --- #### 2. In the vis-network JSON format, which section defines visual styling like background color and node shape for each taxonomy category?
1. metadata section 2. groups section 3. nodes section 4. edges section
??? question "Show Answer" The correct answer is **B**. The groups section defines visual styling (color, font, shape) for each TaxonomyID category, enabling consistent color-coded visualization. The metadata section (option A) contains descriptive information about the graph, the nodes section (option C) contains concept objects, and the edges section (option D) contains dependency relationships. **Concept Tested:** Groups Section in JSON **See:** [Groups Section in JSON](../../glossary.md#groups-section-in-json) --- #### 3. What are the four primary sections of the vis-network JSON format for learning graphs?
1. header, concepts, relationships, footer 2. metadata, groups, nodes, edges 3. title, categories, vertices, links 4. description, taxonomy, elements, connections
??? question "Show Answer" The correct answer is **B**. The vis-network JSON format organizes learning graph data into four sections: metadata (information about the graph), groups (visual styling), nodes (concept objects), and edges (dependency relationships). Options A, C, and D use incorrect terminology that doesn't match the vis-network specification. **Concept Tested:** vis-network JSON Format **See:** [vis-network JSON Format](../../glossary.md#vis-network-json-format) --- #### 4. In the nodes section of vis-network JSON, what three required properties must each node object contain?
1. name, color, size 2. id, label, group 3. number, title, category 4. key, value, type
??? question "Show Answer" The correct answer is **B**. Each node object requires three properties: id (numeric identifier matching ConceptID), label (human-readable concept name), and group (TaxonomyID category for styling). Options A, C, and D use incorrect property names that don't conform to the vis-network schema. **Concept Tested:** Nodes Section in JSON **See:** [Nodes Section in JSON](../../glossary.md#nodes-section-in-json) --- #### 5. You are converting a learning graph CSV row with ConceptID=10 and Dependencies="3|7|9". How many edge objects will be created in the vis-network JSON?
1. 1 edge object (one concept, one entry) 2. 2 edge objects (pipe creates pairs) 3. 3 edge objects (one for each dependency) 4. 4 edge objects (including the concept itself)
??? question "Show Answer" The correct answer is **C**. The Dependencies field "3|7|9" indicates three prerequisites, so three edge objects must be created: {from: 3, to: 10}, {from: 7, to: 10}, and {from: 9, to: 10}. Each dependency creates one edge pointing from the prerequisite to the dependent concept. Options A, B, and D misunderstand the one-to-one mapping of dependencies to edges. **Concept Tested:** Edges Section in JSON **See:** [Edges Section in JSON](../../glossary.md#edges-section-in-json) --- #### 6. Which Dublin Core metadata field should use ISO 8601 format (YYYY-MM-DD)?
1. Title 2. Creator 3. Date 4. License
??? question "Show Answer" The correct answer is **C**. The Date metadata field should use ISO 8601 format (YYYY-MM-DD) for unambiguous, machine-parseable dates like "2024-09-15". Title (option A) is a descriptive string, Creator (option B) contains author information, and License (option D) uses license identifiers like "CC-BY-4.0". **Concept Tested:** Date Metadata Field **See:** [Date Metadata Field](../../glossary.md#date-metadata-field) --- #### 7. In semantic versioning for learning graphs, what does incrementing the MINOR version number indicate?
1. Incompatible changes like restructuring categories 2. Backwards-compatible additions like new concepts 3. Bug fixes like correcting typos 4. Complete rewrite of the learning graph
??? question "Show Answer" The correct answer is **B**. In semantic versioning (MAJOR.MINOR.PATCH), incrementing MINOR indicates backwards-compatible additions such as adding new concepts or refining dependencies. MAJOR increments (option A) indicate breaking changes, PATCH increments (option C) indicate corrections, and option D would be a MAJOR version change, not MINOR. **Concept Tested:** Version Metadata Field **See:** [Version Metadata Field](../../glossary.md#version-metadata-field) --- #### 8. According to WCAG accessibility guidelines, what is the minimum contrast ratio required for normal text?
1. 2:1 contrast ratio 2. 3:1 contrast ratio 3. 4.5:1 contrast ratio 4. 7:1 contrast ratio
??? question "Show Answer" The correct answer is **C**. WCAG AA level requires a minimum 4.5:1 contrast ratio for normal text to ensure readability for users with visual impairments. Option B (3:1) is the requirement for large text, option A is insufficient, and option D (7:1) is the enhanced AAA level for normal text, exceeding the minimum. **Concept Tested:** Font Colors for Readability **See:** [Font Colors for Readability](../../glossary.md#font-colors-for-readability) --- #### 9. What is the recommended approach when a single taxonomy category contains 35% of all concepts in your learning graph?
1. Accept it as natural emphasis on an important topic 2. Review for over-representation and rebalance categories 3. Delete all concepts in the over-represented category 4. Change all concepts to use the same category
??? question "Show Answer" The correct answer is **B**. When a category exceeds 30% (the over-representation threshold), you should review it to identify concepts that could be consolidated, expand under-represented categories, or reclassify borderline concepts to achieve better balance. Option A ignores a quality issue, option C is unnecessarily destructive, and option D would eliminate the benefits of categorization. **Concept Tested:** Category Distribution **See:** [Category Distribution Analysis](../../glossary.md#category-distribution) --- #### 10. Which script should you run to analyze whether your learning graph has balanced representation across taxonomy categories?
1. analyze-graph.py 2. csv-to-json.py 3. taxonomy-distribution.py 4. balance-categories.py
??? question "Show Answer" The correct answer is **C**. The taxonomy-distribution.py script analyzes the distribution of concepts across taxonomy categories, calculating percentages and identifying over- or under-represented categories. The analyze-graph.py script (option A) performs structural validation and quality scoring, csv-to-json.py (option B) converts formats, and option D is not a real script in the toolkit. **Concept Tested:** Python Scripts for Processing **See:** [Python Scripts for Processing](../../glossary.md#python-scripts-for-processing) --- ## Quiz Statistics - **Total Questions:** 10 - **Bloom's Taxonomy Distribution:** - Remember: 3 questions (30%) - Understand: 3 questions (30%) - Apply: 3 questions (30%) - Analyze: 1 question (10%) - **Concepts Covered:** 10 of 22 chapter concepts (45%)