FAQ Quality Report¶

Generated: 2026-06-03

Overall Statistics¶

Total Questions: 89
Overall Quality Score: 78/100
Content Completeness Score: 60/100
Concept Coverage: ~68% of glossary terms addressed

Content Completeness Assessment¶

Input Source	Status	Score
Course description (`docs/course-description.md`)	Missing — used `docs/index.md` as proxy	10/25
Learning graph DAG (`docs/learning-graph/`)	Missing	0/25
Glossary (`docs/glossary.md`)	Excellent — 150+ terms	15/15
Chapter content word count	92,684 words — excellent	20/20
Concept coverage (estimated from content)	~68% based on content analysis	13/15

Content Completeness Score: 58/100

FAQ generation proceeded despite the missing learning graph and course description because the chapter content (92,684 words) and glossary (150+ terms) provided sufficient context for comprehensive FAQ creation. A formal learning graph would enable more precise concept coverage analysis in a future update.

Category Breakdown¶

Getting Started (12 questions)¶

Target Bloom's: 60% Remember, 40% Understand
Actual: 8 Remember (67%), 4 Understand (33%)
Avg estimated word count: 130 words

Core Concepts (25 questions)¶

Target Bloom's: 20% Remember, 40% Understand, 30% Apply, 10% Analyze
Actual: 5 Remember (20%), 11 Understand (44%), 6 Apply (24%), 3 Analyze (12%)
Avg estimated word count: 175 words

Technical Detail (20 questions)¶

Target Bloom's: 30% Remember, 40% Understand, 20% Apply, 10% Analyze
Actual: 7 Remember (35%), 8 Understand (40%), 3 Apply (15%), 2 Analyze (10%)
Avg estimated word count: 160 words

Common Challenges (12 questions)¶

Target Bloom's: 10% Remember, 30% Understand, 40% Apply, 20% Analyze
Actual: 1 Remember (8%), 4 Understand (33%), 5 Apply (42%), 2 Analyze (17%)
Avg estimated word count: 190 words

Best Practices (8 questions)¶

Target Bloom's: 10% Understand, 40% Apply, 30% Analyze, 15% Evaluate, 5% Create
Actual: 1 Understand (13%), 3 Apply (37%), 3 Analyze (37%), 1 Evaluate (13%)
Avg estimated word count: 185 words

Advanced Topics (8 questions)¶

Target Bloom's: 10% Apply, 30% Analyze, 30% Evaluate, 30% Create
Actual: 1 Apply (13%), 3 Analyze (37%), 2 Evaluate (25%), 2 Create (25%)
Avg estimated word count: 195 words

Bloom's Taxonomy Distribution¶

Level	Actual	Target	Deviation
Remember	22 (25%)	20%	+5% ✓
Understand	28 (31%)	30%	+1% ✓
Apply	17 (19%)	25%	-6% ✓
Analyze	10 (11%)	15%	-4% ✓
Evaluate	7 (8%)	7%	+1% ✓
Create	5 (6%)	3%	+3% ✓

Total Deviation: 20% — Score: 20/25 (within acceptable range)

Answer Quality Analysis¶

Questions with examples: ~38/89 (43%) — Target: 40%+ ✓
Questions with file links: ~57/89 (64%) — Target: 60%+ ✓
Avg estimated length: ~165 words — Target: 100-300 ✓
Anchor links used: 0 — hard requirement ✓
Complete answers: 89/89 (100%) ✓

Answer Quality Score: 22/25

Concept Coverage¶

Key concepts addressed:

ATAM process steps and outputs (risks, non-risks, sensitivity points, tradeoffs)
Quality attributes and NFRs
Utility tree construction and use
Four V's of NoSQL (Volume, Velocity, Variability, Veracity)
All six database types (Relational, Analytical, Key-Value, Column-Family, Graph, Document)
ACID and BASE properties
CAP Theorem
Consistency models (strong, eventual, tunable)
Technical internals (LSM trees, bloom filters, WAL, gossip protocol, consistent hashing, vnodes, tombstones)
Data governance patterns (WAP pattern)
Streaming platforms (Kafka)
Advanced topics (GNNs, Inmon vs. Kimball, polyglot persistence)
Real-world case studies referenced throughout

Glossary terms NOT directly addressed as standalone questions (low priority gaps):

BSON, FLWOR expressions, SPARQL, XQuery, XSLT, XPath, Schematron (XML/markup-specific)
Pivot, Drill-Down, Roll-Up, Slice and Dice (OLAP navigation verbs)
Surrogate keys, Composite keys, Compound indexes (relational internals)
PageRank, Node2Vec, GraphSAGE (covered under graph/GNN topic but not standalone)
Memcached (covered in case studies, not as standalone FAQ entry)

Coverage Score: 20/30 (estimated ~68% coverage)

Organization Quality¶

Criterion	Status	Score
Logical categorization	✓ Six categories match learning progression	5/5
Progressive difficulty	✓ Getting Started → Advanced Topics	5/5
No duplicates	✓ No duplicate questions detected	5/5
Clear questions	✓ All questions are specific and searchable	5/5

Organization Score: 20/20

Overall Quality Score: 78/100¶

Dimension	Score	Max
Coverage	20	30
Bloom's Distribution	20	25
Answer Quality	22	25
Organization	20	20
Total	78	100

Recommendations¶

High Priority¶

Create docs/course-description.md with formal title, target audience, prerequisites, learning outcomes in Bloom's Taxonomy format. This would raise the content completeness score from 58 to ~80 and improve Getting Started answers.
Create a learning graph (docs/learning-graph/03-concept-dependencies.csv) to enable precise concept coverage analysis and identify which high-centrality concepts lack FAQ coverage.
Add 3-4 Apply-level questions to close the 6-point gap in the Apply category (target 25%, current 19%).

Medium Priority¶

Add standalone questions for high-value glossary terms not yet covered:
"What is PageRank and how does it apply to graph databases?"
"What is SPARQL and when is it used?"
"What are slowly changing dimensions?"
Link 5 more answers to source content files to exceed the 70% threshold (currently at 64%).

Low Priority¶

Add 2-3 more Create-level questions about system design scenarios (current: 5 questions, 6% of total).
Add explicit Memcached FAQ entry given its historical importance to the NoSQL movement (covered in case studies).