fix: 커리큘럼 감사 — 섹션 분류 제거 + 개념 역전 수정

1. 연습문제 섹션 분류(기초/응용/실전) 전체 제거 (98파일, ko+en)
   — 일부만 있어 일관성 위배, 문제 번호만으로 충분

2. 개념 역전 수정 (초급에서 미수강 개념 사용):
   - beginner-02-customers 문제5: OVER() → 서브쿼리로 대체
   - beginner-02-customers 문제17: CTE+OVER+LEFT JOIN → 단순 GROUP BY+서브쿼리
   - lectures/07-case 문제10: INNER JOIN 4테이블 → 단일 테이블 CASE

3. YAML 원본 + 컴파일 결과 모두 반영

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

Author: civilian7

docs/en/exercises/advanced-01-window-functions.md

@@ -12,8 +12,6 @@
 !!! abstract "Concepts"
     `ROW_NUMBER`, `RANK`, `DENSE_RANK`, `NTILE`, `LAG`, `LEAD`, `SUM/AVG/COUNT OVER`, `FIRST_VALUE`, `LAST_VALUE`, `ROWS BETWEEN`
 
-## Basic (1~5)
-
 Practice the basic syntax of window functions.
 
 ---
@@ -252,8 +250,6 @@ Payments completed in 2024 (`status = 'completed'`) are tallied by means, and qu
 
 ---
 
-## Applied (6~10)
-
 Utilizes analytical functions such as LAG/LEAD and moving average.
 
 ---
@@ -547,8 +543,6 @@ Only products with 3 or more reviews are eligible.
 
 ---
 
-## Practical (11~15)
-
 Solve practical analysis scenarios with complex window function combinations.
 
 ---

docs/en/exercises/advanced-02-cte.md

@@ -12,8 +12,6 @@
 !!! abstract "Concepts"
     `WITH`, Recursive CTE(`WITH RECURSIVE`), Multiple CTE Chaining, CTE + Aggregate/JOIN/Window Function
 
-## Basic (1~5)
-
 Practice single CTE and CTE + JOIN/aggregation.
 
 ---
@@ -299,8 +297,6 @@ Calculate sales by category as of 2024 and display cumulative proportions in des
 
 ---
 
-## Applied (6~10)
-
 Utilizes recursive CTEs and multiple CTEs.
 
 ---
@@ -614,8 +610,6 @@ We chain three CTEs: (1) RFM raw metrics, (2) NTILE scores, and (3) segment labe
 
 ---
 
-## Practical (11~15)
-
 We cover advanced patterns such as chaining more than 3 CTEs, leveraging recursion depth, and creating date sequences.
 
 ---

docs/en/exercises/advanced-03-exists-antipattern.md

@@ -14,8 +14,6 @@
 !!! abstract "Concepts"
     `EXISTS`, `NOT EXISTS`, Correlated subquery, Anti-join pattern, Universal Quantification
 
-## Basic (1~5)
-
 Practice the basic usage of EXISTS and NOT EXISTS.
 
 ---
@@ -261,8 +259,6 @@ Identify “blue-chip customers” who consistently purchase without making a cl
 
 ---
 
-## Applied (6~10)
-
 Covers complex EXISTS conditions combining correlated subqueries and aggregates.
 
 ---
@@ -522,8 +518,6 @@ Combines two EXISTS/NOT EXISTS conditions.
 
 ---
 
-## Practical (11~15)
-
 Covers advanced scenarios such as universal qualification, compound NOT EXISTS, and anti-patterns.
 
 ---

docs/en/exercises/advanced-04-db-objects.md

@@ -14,8 +14,6 @@
 !!! abstract "Concepts"
     `CREATE VIEW`, View query, `DROP VIEW`, `CREATE TRIGGER` (AFTER/BEFORE, INSERT/UPDATE/DELETE, WHEN, OLD/NEW), Audit Logging, Stored procedure concepts (MySQL/PG)
 
-## Views (1~7)
-
 Practice creating, using, and managing views.
 
 ---
@@ -773,8 +771,6 @@ Save statistics for each product as a table and implement an update pattern.
 
 ---
 
-## Trigger (8~14)
-
 Practice defining, creating, and using triggers.
 
 ---
@@ -1188,8 +1184,6 @@ Delete the existing `trg_stock_deduct` trigger and recreate it with an improved
 
 ---
 
-## Stored procedures (15~20)
-
 Stored procedures are not supported by SQLite. Provided in MySQL/PostgreSQL syntax.
 
 !!! warning "SQLite not supported"

docs/en/exercises/advanced-05-json.md

@@ -21,13 +21,11 @@ The `specs` column of the products table stores specification information for ea
 
 ---
 
-
 ### Problem 1. JSON Basic Extraction — Laptop CPU List
 
 Extract product names and CPU specifications from all active products in the Laptop category.
 Products with NULL CPU specifications are excluded.
 
-
 ??? tip "Hint"
     - Extract JSON internal values ​​with `json_extract(specs, '$.cpu')`
     - Filter categories by JOIN with `categories` table
@@ -50,16 +48,13 @@ Products with NULL CPU specifications are excluded.
     | (laptop product name) | Intel Core i7-13700H |
     | ... | ... |
 
-
 ---
 
-
 ### Problem 2. JSON Multiple Field Extraction — Notebook Spec Sheet
 
 Create a spec sheet by extracting the screen size, CPU, RAM (GB), storage capacity (GB), and battery (hours) of your laptop product at once.
 Sort by price descending.
 
-
 ??? tip "Hint"
     - Call `json_extract` multiple times to extract each field into a separate column
     - `$.screen_size`, `$.cpu`, `$.ram_gb`, `$.storage_gb`, `$.battery_hours`
@@ -88,16 +83,13 @@ Sort by price descending.
     | (laptop name) | 2500000 | 16 inch | Intel Core i9-13900H | 32 | 1024 | 12 |
     | ... | ... | ... | ... | ... | ... | ... |
 
-
 ---
 
-
 ### Problem 3. Identifying products with NULL specs
 
 Check the distribution by category of products without specs information (NULL).
 The goal is to determine which categories are missing specification information.
 
-
 ??? tip "Hint"
     - `WHERE p.specs IS NULL` condition
     - Aggregated by `GROUP BY` Category name
@@ -120,16 +112,13 @@ The goal is to determine which categories are missing specification information.
     | (peripheral category) | 45 |
     | ... | ... |
 
-
 ---
 
-
 ### Problem 4. List JSON keys — utilizing json_each
 
 Pick a laptop product and list all the keys included in the specs JSON.
 `json_each` uses table-valued functions.
 
-
 ??? tip "Hint"
     - `json_each(specs)` returns each key-value pair in a JSON object as a row
     - Return columns: `key`, `value`, `type`
@@ -161,16 +150,13 @@ Pick a laptop product and list all the keys included in the specs JSON.
     | storage_gb | 512 | integer |
     | battery_hours | 10 | integer |
 
-
 ---
 
-
 ### Problem 5. JSON Condition Filtering — Finding High-Performance Laptops
 
 Look for a laptop with at least 32GB of RAM and at least 1024GB of storage.
 Displays product name, price, RAM, and storage capacity.
 
-
 ??? tip "Hint"
     - Use JSON value as condition in WHERE clause like `json_extract(specs, '$.ram_gb') >= 32`
     - Values ​​extracted from JSON are automatically converted to the appropriate type.
@@ -198,16 +184,13 @@ Displays product name, price, RAM, and storage capacity.
     | (high-spec laptop) | 3200000 | 32 | 1024 | Intel Core i9-13900H |
     | ... | ... | ... | ... | ... |
 
-
 ---
 
-
 ### Problem 6. JSON-based group aggregation — average price by CPU
 
 Find the average price and number of products by CPU specification for laptops and desktops.
 Shows only CPUs with 3 or more products.
 
-
 ??? tip "Hint"
     - Use `json_extract(specs, '$.cpu')` for `GROUP BY`
     - Exclude minority groups with `HAVING COUNT(*) >= 3`
@@ -236,16 +219,13 @@ Shows only CPUs with 3 or more products.
     | AMD Ryzen 9 7950X | 4 | 2500000 | 2000000 | 3000000 |
     | ... | ... | ... | ... | ... |
 
-
 ---
 
-
 ### Problem 7. JSON-based statistics — Analysis by monitor panel type
 
 In the monitor category, tally the number of products, average price, and average refresh rate by panel type (IPS/VA/OLED).
 Also check the resolution distribution of each panel type.
 
-
 ??? tip "Hint"
     - Use `json_extract(specs, '$.panel')`, `json_extract(specs, '$.refresh_rate')`
     - Resolution distribution is processed as a separate query or conditional aggregation (`CASE WHEN`)
@@ -275,16 +255,13 @@ Also check the resolution distribution of each panel type.
     | IPS | 25 | 450000 | 120 | 8 | 10 | 7 |
     | VA | 12 | 380000 | 100 | 5 | 5 | 2 |
 
-
 ---
 
-
 ### Problem 8. Modifying JSON value with json_set (within SELECT)
 
 Use SQLite's `json_set` function to check the result of adding the `"warranty_years": 3` field to the laptop product's specs.
 Preview the converted JSON from a SELECT, not an actual UPDATE.
 
-
 ??? tip "Hint"
     - `json_set(specs, '$.warranty_years', 3)` adds a new key to an existing JSON
     - If the key already exists, the value is overwritten.
@@ -307,16 +284,13 @@ Preview the converted JSON from a SELECT, not an actual UPDATE.
     |---|---|---|
     | (laptop name) | {"screen_size":"15.6 inch",...} | {"screen_size":"15.6 inch",...,"warranty_years":3} |
 
-
 ---
 
-
 ### Problem 9. Removing a JSON key with json_remove (within a SELECT)
 
 Compare the original with the result of removing the `tdp_watts` key from the GPU product's specs.
 Use the `json_remove` function.
 
-
 ??? tip "Hint"
     - `json_remove(specs, '$.tdp_watts')` returns JSON with the specified key removed
     - Display `specs` and `json_remove(...)` results side by side for comparison with the original
@@ -338,16 +312,13 @@ Use the `json_remove` function.
     |---|---|---|
     | (GPU name) | {"vram":"16GB","clock_mhz":2100,"tdp_watts":300} | {"vram":"16GB","clock_mhz":2100} |
 
-
 ---
 
-
 ### Problem 10. Comprehensive JSON analysis — Specification comparison report by category
 
 Analyze product specs for all categories to find a list of unique keys included in JSON for each category and the occurrence rate of each key.
 This report allows you to see at a glance what specification information exists in which category.
 
-
 ??? tip "Hint"
     - Spread all keys into rows with `json_each(specs)`, then GROUP BY with category + key combination
     - Appearance rate = Number of products with that key / Total number of products in the category