Companion to the Practice Set · Mark-by-mark walkthroughs · AP CSP-Feeder / US / ON / BC / AB styles练习题配套详解 · 逐分讲解 · AP CSP 衔接 / 美 / 安 / 卑 / 阿省考风格
A weather sensor records values with no labels. A program adds labels and displays an interpreted summary. Which statement best describes the relationship between the raw values and the final display?气象传感器记录了没有标签的值。程序添加标签并显示经解释的摘要。哪个陈述最准确地描述了原始值与最终显示之间的关系？
Eliminate each option: [1]逐一排除每个选项：[1]
A student wants to add a new log entry to log.txt without erasing existing entries. Which file-open mode?学生想向 log.txt 添加新日志条目而不删除现有条目。哪种文件打开模式？
"a" (append mode)答案：(C) - "a"（追加模式）Four Python file modes: [1]四种 Python 文件模式：[1]
"r" = read-only. The file cannot be written to. Incorrect."r" = 只读。无法写入文件。不正确。"w" = write mode. Opens the file and erases all existing content before writing. Incorrect for this task. [1]"w" = 写入模式。打开文件并在写入前删除所有现有内容。不适合此任务。[1]"a" = append mode. Opens the file and positions the write cursor at the end, so new content is added after existing content. Correct. [1]"a" = 追加模式。打开文件并将写入游标定位在末尾，因此新内容添加到现有内容之后。正确。[1]"x" = exclusive create. Creates a new file; raises an error if the file already exists. Incorrect."x" = 独占创建。创建新文件；如果文件已存在则引发错误。不正确。"a". If the question says "overwrite" or "start fresh," the answer is write mode "w". AB CSE2130 outcome 1.4.4 ("exporting data to a file") directly tests this choice.考试中的关键词是"不删除"或"添加到末尾"，两者都表示追加模式 "a"。如果题目说"覆盖"或"重新开始"，答案是写入模式 "w"。AB CSE2130 结果 1.4.4（"将数据导出到文件"）直接考查这个选择。CSV with a header row and three product records. Identify the header row and record count; explain a crash when computing price * 1.13.CSV 包含标题行和三条产品记录。指出标题行和记录数；解释计算 price * 1.13 时崩溃的原因。
product_id,name,price; 3 records. Part (b): CSV values are strings; fix with float(row["price"]) * 1.13.第 (a) 部分：标题行 = product_id,name,price；3 条记录。第 (b) 部分：CSV 值为字符串；修复：float(row["price"]) * 1.13。The header row is the first line of the CSV that names each column: product_id,name,price. [1] The three subsequent lines (101, 102, 103) are data rows (records), so the file contains 3 records. [1]标题行是 CSV 的第一行，命名每一列：product_id,name,price。[1]随后的三行（101、102、103）是数据行（记录），因此文件包含 3 条记录。[1]
The most likely cause: every value read from a CSV is a string. row["price"] holds the string "3.50". Python 3 raises a TypeError when multiplying a string by a float ("3.50" * 1.13 is not defined). [1]最可能的原因：从 CSV 读取的每个值都是字符串。row["price"] 保存字符串 "3.50"。Python 3 在字符串乘以浮点数时引发 TypeError（"3.50" * 1.13 未定义）。[1]
Fix (one line):修复（一行）：
float(row["price"]) * 1.13float(row["price"]) converts the string "3.50" to the floating-point number 3.50, which can then be multiplied. [1]float(row["price"]) 将字符串 "3.50" 转换为浮点数 3.50，然后可以进行乘法运算。[1]
int() for whole numbers, float() for decimals. The Study Guide's Exam Strategy section lists this as the top CSV pitfall. If the question says "the program crashes" on a CSV numeric operation, the answer is almost always a missing type conversion.这是最常见的 CSV 错误。在算术运算前始终转换数值型 CSV 字段：整数用 int()，小数用 float()。学习指南的考试策略部分将此列为 CSV 的首要陷阱。如果题目说程序在 CSV 数值操作上"崩溃"，答案几乎总是缺少类型转换。Python program reads scores from scores.txt, computes average, writes to summary.txt.Python 程序从 scores.txt 读取成绩，计算平均值，写入 summary.txt。
The scores are 87, 92, 74, 95, 88. Count = 5. Sum = 87+92+74+95+88 = 436. Average = 436/5 = 87.2. The file contains exactly:成绩为 87、92、74、95、88。Count = 5。Sum = 87+92+74+95+88 = 436。Average = 436/5 = 87.2。文件内容恰好为：
Count: 5
Average: 87.2The :.1f format specifier rounds to 1 decimal place. [1] Both lines end with \n (newline). [1]:.1f 格式说明符保留 1 位小数。[1]两行均以 \n（换行符）结尾。[1]
line.strip() removes leading and trailing whitespace, including the newline character (\n) that appears at the end of each line in the file. [1] Without it, int("87\n") would raise a ValueError because the newline makes the string invalid for integer conversion. [1]line.strip() 删除首尾空白字符，包括文件每行末尾的换行符（\n）。[1]如果没有它，int("87\n") 会引发 ValueError，因为换行符使字符串对整数转换无效。[1]
with open("summary.txt", "a") as f:
f.write("Max: 95\n")Mode "a" opens the existing file and positions the cursor at the end. [1] f.write("Max: 95\n") adds the line without touching existing content. [1]模式 "a" 打开现有文件并将游标定位在末尾。[1]f.write("Max: 95\n") 添加该行而不影响现有内容。[1]
The with statement is a context manager: when execution leaves the block (normally or via an exception), Python automatically calls the file's close method, flushing any buffered data and releasing the OS file handle. [1]with 语句是一个上下文管理器：当执行离开代码块时（正常或通过异常），Python 自动调用文件的关闭方法，刷新所有缓冲数据并释放操作系统文件句柄。[1]
with block automates the close step and is Python best practice. On a CSE2130 exam, always use the with pattern in your answers; manually calling f.close() at the end is also acceptable but the with approach is safer under errors.每道文件题考查的三个操作是打开-读取/写入-关闭。with 块自动化了关闭步骤，是 Python 最佳实践。在 CSE2130 考试中，答案中始终使用 with 模式；在末尾手动调用 f.close() 也可接受，但 with 方法在出错时更安全。Program reads students.csv with csv.DictReader and prints students who scored above 85.程序使用 csv.DictReader 读取 students.csv，打印成绩高于 85 的学生。
Check each student: Alice 92 > 85 (print), Bob 78 not > 85 (skip), Carol 95 > 85 (print), David 82 not > 85 (skip), Eve 88 > 85 (print). Exact output: [2]检查每位学生：Alice 92 > 85（打印），Bob 78 不 > 85（跳过），Carol 95 > 85（打印），David 82 不 > 85（跳过），Eve 88 > 85（打印）。精确输出：[2]
Alice 92
Carol 95
Eve 88csv.reader returns each row as a plain list (e.g., ["Alice", "11", "92"]); fields are accessed by numeric index (row[2]). [1] csv.DictReader reads the header row automatically and maps each subsequent row to a dictionary keyed by column name (e.g., {"name": "Alice", "grade": "11", "score": "92"}). Advantage: fields are accessed by name (row["score"]), making the code self-documenting and less prone to off-by-one index errors. [1]csv.reader 将每行作为普通列表返回（例如 ["Alice", "11", "92"]）；字段通过数字索引访问（row[2]）。[1]csv.DictReader 自动读取标题行，并将后续每行映射为以列名为键的字典（例如 {"name": "Alice", "grade": "11", "score": "92"}）。优点：通过名称访问字段（row["score"]），使代码自文档化，减少索引偏移错误。[1]
CSV values are always strings. row["score"] holds "92" (a string), not 92 (an integer). Comparing a string to an integer with > raises a TypeError in Python 3 ("92" > 85 is invalid). [1] int(row["score"]) converts "92" to 92 first, making the comparison valid. [1]CSV 值始终是字符串。row["score"] 保存 "92"（字符串），而不是 92（整数）。在 Python 3 中，用 > 比较字符串和整数会引发 TypeError（"92" > 85 无效）。[1]int(row["score"]) 先将 "92" 转换为 92，使比较有效。[1]
if int(row["score"]) > 85 and int(row["grade"]) == 12:Both score and grade must be converted to integers before numeric comparison. [1] Using and combines both conditions: score above 85 AND grade exactly 12. Only Carol (grade 12, score 95) meets both conditions. [1]在数值比较前，score 和 grade 都必须转换为整数。[1]使用 and 组合两个条件：成绩高于 85 且年级恰好为 12。只有 Carol（12 年级，成绩 95）同时满足两个条件。[1]
>, <, and == comparisons against CSV-sourced values and ask: "Was this converted?"对多个 CSV 列进行过滤时，每个数值字段都需要自己的类型转换。常见考试陷阱是转换一个字段但忘记另一个。始终扫描所有与 CSV 来源值的 >、< 和 == 比较，并问："这个被转换了吗？"Library database with Books and Loans tables. Primary/foreign keys and normalisation.图书馆数据库，包含 Books 和 Loans 表。主键/外键和规范化。
Books table primary key: book_id. Each book has a unique ID (1, 2, 3 - no duplicates). [1]Books 表主键：book_id。每本书有唯一的 ID（1、2、3 - 没有重复）。[1]
Loans table primary key: loan_id. Each loan event has a unique ID (1, 2, 3). [1]Loans 表主键：loan_id。每条借阅记录有唯一的 ID（1、2、3）。[1]
A column qualifies as a primary key when every row has a unique, non-null value in that column and the value never repeats across rows. [1]当每行在该列中具有唯一的非空值且值不在行间重复时，该列可作为主键。[1]
The foreign key in Loans is book_id. [1] It references the book_id column in the Books table. [1] Linking via this foreign key allows a program to look up the full book details (title, author) for any loan: given a loan row with book_id = 2, the program can find the book "Data Science 101" in Books without repeating that title in every Loans row. [1]Loans 中的外键是 book_id。[1]它引用 Books 表中的 book_id 列。[1]通过此外键链接允许程序查找任何借阅记录的完整图书详情（书名、作者）：给定 book_id = 2 的借阅行，程序可以在 Books 中找到书目"Data Science 101"，而无需在每条 Loans 行中重复该书名。[1]
Storing the title once in Books and referencing it by book_id in Loans is better because: (1) if the title needs to be corrected, it is changed in one place only; repeating it in every Loans row means every copy must be updated, risking inconsistency. (2) It saves storage space. [1] This principle is called normalisation. [1]将书名在 Books 中存储一次并在 Loans 中通过 book_id 引用更好，因为：(1) 如果书名需要更正，只需在一个地方更改；在每条 Loans 行中重复意味着必须更新每个副本，有不一致的风险。(2) 节省存储空间。[1]这个原则称为规范化。[1]
SQL queries on the Books and Loans tables from Q6.对 Q6 的 Books 和 Loans 表执行 SQL 查询。
SELECT * FROM Books;SELECT * returns all columns; FROM Books names the table. [1]SELECT * 返回所有列；FROM Books 指定表名。[1]
SELECT title, author FROM Books WHERE book_id = 2;Result: Data Science 101 | Lee, C. [2] (1 mark for SELECT+FROM, 1 mark for WHERE clause)结果：Data Science 101 | Lee, C. [2]（1 分给 SELECT+FROM，1 分给 WHERE 子句）
SELECT * FROM Loans ORDER BY due_date ASC;The keyword for ascending sort order is ASC (it is also the default, so ORDER BY due_date without ASC also works). [2]升序排列的关键字是 ASC（它也是默认值，所以不带 ASC 的 ORDER BY due_date 也有效）。[2]
Error: SELECT title WHERE book_id = 1; is missing the FROM Books clause. SQL requires FROM to identify the table. [1]错误：SELECT title WHERE book_id = 1; 缺少 FROM Books 子句。SQL 需要 FROM 来标识表。[1]
SELECT title FROM Books WHERE book_id = 1;Result: Python Primer. [1]结果：Python Primer。[1]
SELECT → FROM → WHERE → ORDER BY. [1]SELECT → FROM → WHERE → ORDER BY。[1]
FROM is the most common SQL error on Ontario exams. A complete SELECT always needs at minimum SELECT + FROM. The WHERE and ORDER BY clauses are optional additions, but their order relative to each other is fixed: WHERE filters before ORDER BY sorts. Write the clauses in order and you will never place ORDER BY before WHERE.缺少 FROM 是安大略考试中最常见的 SQL 错误。完整的 SELECT 至少需要 SELECT + FROM。WHERE 和 ORDER BY 子句是可选的添加项，但它们相对彼此的顺序是固定的：WHERE 在 ORDER BY 排序之前过滤。按顺序写子句，就永远不会把 ORDER BY 放在 WHERE 之前。Music streaming service. Students (student_id, name, plan) and Plays (play_id, student_id, song_title, play_count).音乐流媒体服务。Students（student_id, name, plan）和 Plays（play_id, student_id, song_title, play_count）。
SELECT name, plan
FROM Students
WHERE plan = 'premium'
ORDER BY name ASC;[1] SELECT names the correct columns. [1] WHERE filters for plan = 'premium' (string comparisons in SQL use single quotes). [1] ORDER BY name ASC sorts alphabetically.[1] SELECT 命名正确的列。[1] WHERE 过滤 plan = 'premium'（SQL 中字符串比较使用单引号）。[1] ORDER BY name ASC 按字母排序。
SELECT song_title
FROM Plays
WHERE play_count > 100
ORDER BY play_count DESC;[1] Correct column (song_title) and table (Plays). [1] WHERE play_count > 100 filters correctly (numeric, no quotes). [1] ORDER BY play_count DESC sorts highest first (DESC = descending).[1] 正确的列（song_title）和表（Plays）。[1] WHERE play_count > 100 正确过滤（数值，不加引号）。[1] ORDER BY play_count DESC 最高在前（DESC = 降序）。
Flat table: simpler to set up and query (one table, no joins); however, the student name is repeated in every Plays row, wasting storage and risking inconsistency if the name changes. [1]平面表：设置和查询更简单（一个表，无需连接）；但是，学生姓名在每条 Plays 行中重复，如果姓名更改则浪费存储空间且有不一致风险。[1]
When student names might change, the two-table (linked) design is better: the name is stored once in Students; only the student_id foreign key appears in Plays. Updating the name requires changing only one row in Students, not every Plays row. [1]当学生姓名可能更改时，两表（关联）设计更好：姓名在 Students 中只存储一次；Plays 中只有 student_id 外键。更新姓名只需更改 Students 中的一行，而不是每条 Plays 行。[1]
WHERE plan = 'premium' not double quotes. Numeric literals have no quotes: WHERE play_count > 100. Mixing these up is a common syntax error. Also note: DESC and ASC apply to the column in the ORDER BY clause, not to the whole query.SQL 字符串字面量始终使用单引号：WHERE plan = 'premium' 而不是双引号。数值字面量不加引号：WHERE play_count > 100。混淆这两者是常见的语法错误。另外：DESC 和 ASC 应用于 ORDER BY 子句中的列，而不是整个查询。Scores table (score_id, student_id, subject, score). SQL COUNT, AND filter, and CSV-vs-database tradeoff.Scores 表（score_id、student_id、subject、score）。SQL COUNT、AND 过滤以及 CSV 与数据库的权衡。
SELECT COUNT(*) FROM Scores WHERE subject = 'CS';COUNT(*) counts matching rows. [1] From the table: rows with CS are score_id 2 (student 1, CS, 88) and score_id 4 (student 3, CS, 95). Expected result: 2. [1]COUNT(*) 统计匹配行数。[1]从表中：CS 行为 score_id 2（学生 1，CS，88）和 score_id 4（学生 3，CS，95）。预期结果：2。[1]
SELECT student_id, score
FROM Scores
WHERE subject = 'Math' AND score >= 85;Checking the table: Math rows are (student_id=1, score=92) and (student_id=2, score=78). Only student 1 has Math score >= 85. [1] The AND operator requires both conditions to be true simultaneously. [1] Result: student_id = 1, score = 92.检查表：Math 行为（student_id=1, score=92）和（student_id=2, score=78）。只有学生 1 的 Math 成绩 >= 85。[1]AND 运算符要求两个条件同时为真。[1]结果：student_id = 1，score = 92。
CSV advantage: simple to create, edit, and share; no database software required; any spreadsheet or text editor can read it. [1]CSV 优点：创建、编辑和共享简单；不需要数据库软件；任何电子表格或文本编辑器都可以读取。[1]
Relational database advantage: supports complex queries (WHERE, JOIN, COUNT, ORDER BY) across large datasets; enforces data integrity through primary/foreign keys; avoids data duplication through normalisation. [1]关系数据库优点：支持跨大型数据集的复杂查询（WHERE、JOIN、COUNT、ORDER BY）；通过主键/外键强制数据完整性；通过规范化避免数据重复。[1]
Recommendation: a relational database for a school with 500 students and 10 subjects. With 500 students each having up to 10 subject scores, a single CSV would have up to 5000 rows with repeated student names and be difficult to query (e.g., "find all students with Math score above 80"). A database allows efficient SQL queries, avoids duplication, and scales cleanly. [1]建议：对于有 500 名学生和 10 门课程的学校，推荐使用关系数据库。500 名学生每人最多有 10 门课程成绩，单个 CSV 最多有 5000 行，学生姓名重复且难以查询（例如"找出所有数学成绩高于 80 的学生"）。数据库允许高效的 SQL 查询，避免重复，并能整洁地扩展。[1]
HTML with errors; student builds a library catalogue page.包含错误的 HTML；学生构建图书馆目录页面。
Error 1: <title>Library Catalogue<title> - the closing tag is missing the slash. It should be </title>. A closing tag requires a forward slash before the tag name. [2]错误 1：<title>Library Catalogue<title> - 结束标签缺少斜杠。应为 </title>。结束标签在标签名前需要正斜杠。[2]
Error 2: <a href="books.html">Browse Books</p> - the anchor tag <a> is opened but never closed; instead a </p> appears. The correct closing is </a></p> (close the anchor inside the paragraph) or more commonly <p><a href="books.html">Browse Books</a></p>. Also, the <li>Data Science 101 item is missing its closing </li> tag (also acceptable as a second error). [2]错误 2：<a href="books.html">Browse Books</p> - 锚点标签 <a> 已打开但从未关闭；取而代之的是 </p>。正确的关闭方式是 </a></p>（在段落内关闭锚点），或更常见地 <p><a href="books.html">Browse Books</a></p>。另外，<li>Data Science 101 项缺少结束 </li> 标签（也可作为第二个错误）。[2]
<table>
<tr>
<th>Title</th><th>Author</th>
</tr>
<tr>
<td>Python Primer</td><td>Smith, J.</td>
</tr>
<tr>
<td>Web Basics</td><td>Patel, R.</td>
</tr>
</table>Key tags: <table> wraps the table; <tr> = table row; <th> = header cell (bold, centred by default); <td> = data cell. [1] for correct table structure (table/tr); [1] for correct header row with th; [1] for both data rows with td.关键标签：<table> 包裹表格；<tr> = 表格行；<th> = 表头单元格（默认粗体居中）；<td> = 数据单元格。[1]正确的表格结构（table/tr）；[1]带 th 的正确标题行；[1]两个带 td 的数据行。
The <body> section contains the page's visible content (headings, paragraphs, tables, links). [1] An example of something in the <head> section: <title> (sets the browser tab title, not visible on the page itself), <meta charset="UTF-8"> (character encoding), or a CSS link.<body> 部分包含页面的可见内容（标题、段落、表格、链接）。[1]属于 <head> 部分的例子：<title>（设置浏览器标签标题，不在页面上显示）、<meta charset="UTF-8">（字符编码）或 CSS 链接。
<title> instead of </title>), and (2) a mismatched closing tag (closing the wrong element). Always check that each opening tag has a matching closing tag with a slash. Self-closing tags like <br> and <img> are the exceptions.在"找出 HTML 错误"题中，两种最常见的预设错误是：(1) 缺少结束标签斜杠（例如 <title> 代替 </title>），以及 (2) 不匹配的结束标签（关闭了错误的元素）。始终检查每个开始标签是否有带斜杠的匹配结束标签。<br> 和 <img> 等自闭合标签是例外。Weather API JSON response. Python parses and prints city, tomorrow forecast, and first alert.天气接口 JSON 响应。Python 解析并打印城市、明日预报和第一条警告。
Trace: city = "Vancouver", tomorrow = "sunny", first_alert = "fog warning" (index 0 of the alerts list). The f-string produces: [2]追踪：city = "Vancouver"，tomorrow = "sunny"，first_alert = "fog warning"（alerts 列表的索引 0）。f-string 产生：[2]
Vancouver: tomorrow will be sunny. Alert: fog warningStep 1: data["forecast"] accesses the value associated with the key "forecast" in the top-level dictionary. That value is itself a dictionary: {"tomorrow": "sunny", "temp_high": 22.0}. [1]步骤 1：data["forecast"] 访问顶级字典中与键 "forecast" 关联的值。该值本身是一个字典：{"tomorrow": "sunny", "temp_high": 22.0}。[1]
Step 2: ["tomorrow"] accesses the "tomorrow" key in that nested dictionary, returning the string "sunny". [1]步骤 2：["tomorrow"] 访问该嵌套字典中的 "tomorrow" 键，返回字符串 "sunny"。[1]
data["alerts"][1]Python lists are 0-indexed: index 0 = "fog warning", index 1 = "wind advisory". [1]Python 列表从 0 开始索引：索引 0 = "fog warning"，索引 1 = "wind advisory"。[1]
Difference 1: JSON keys must be double-quoted strings. Python dictionary keys can be unquoted (if they are string literals, single quotes work too). Example: JSON requires "city": "Vancouver"; Python allows 'city': 'Vancouver' or city: 'Vancouver' (as a variable). [1]差异 1：JSON 键必须是双引号字符串。Python 字典键可以不带引号（如果是字符串字面量，单引号也可以）。示例：JSON 要求 "city": "Vancouver"；Python 允许 'city': 'Vancouver' 或 city: 'Vancouver'（作为变量）。[1]
Difference 2: JSON uses lowercase true/false/null for booleans and null; Python uses capitalised True/False/None. Example: JSON {"active": true} is invalid as Python dict syntax; Python is {"active": True}. [1]差异 2：JSON 对布尔值和 null 使用小写 true/false/null；Python 使用首字母大写的 True/False/None。示例：JSON {"active": true} 作为 Python 字典语法是无效的；Python 是 {"active": True}。[1]
json.loads(text) parses a JSON-formatted string and converts it into the equivalent Python data structure. [1] When the input is a JSON object (surrounded by {}), it returns a Python dict. When the input is a JSON array ([]), it returns a Python list. [1]json.loads(text) 解析 JSON 格式的字符串并将其转换为等效的 Python 数据结构。[1]当输入是 JSON 对象（被 {} 包围）时，返回 Python dict。当输入是 JSON 数组（[]）时，返回 Python list。[1]
json.loads is a mnemonic: "loads" = "load string." Its counterpart json.dumps = "dump string" (converts Python back to a JSON string). For nested JSON, the access pattern chains index operations: outer key first, then inner key. Each [] descends one level into the nested structure.json.loads 的名称是助记符："loads" = "load string"（加载字符串）。其对应的 json.dumps = "dump string"（转储字符串，将 Python 转换回 JSON 字符串）。对于嵌套 JSON，访问模式链接索引操作：先外层键，再内层键。每个 [] 向嵌套结构下降一层。Data pipeline: API returns JSON list of student scores; program generates HTML table.数据管线：接口以 JSON 列表返回学生成绩；程序生成 HTML 表格。
json.loads(api_response) returns a Python list. [1] You can tell because the JSON string starts with [ and ends with ]; square brackets denote a JSON array, which maps to a Python list. Each element of the array is a JSON object ({...}), which maps to a Python dict. [1]json.loads(api_response) 返回 Python list。[1]可以从 JSON 字符串以 [ 开始、以 ] 结束判断，方括号表示 JSON 数组，映射到 Python 列表。数组的每个元素是 JSON 对象（{...}），映射到 Python 字典。[1]
The loop iterates over three student dicts: Alice/92, Bob/78, Carol/95. [1] for header row; [1] for first two data rows; [1] for Carol row and closing tag.循环遍历三个学生字典：Alice/92、Bob/78、Carol/95。[1]表头行；[1]前两个数据行；[1]Carol 行和结束标签。
<table>
<tr><th>Name</th><th>Score</th></tr>
<tr><td>Alice</td><td>92</td></tr>
<tr><td>Bob</td><td>78</td></tr>
<tr><td>Carol</td><td>95</td></tr>
</table>for s in students:
if s['score'] >= 90:
html += f" <tr class='high'><td>{s['name']}</td><td>{s['score']}</td></tr>\n"
else:
html += f" <tr><td>{s['name']}</td><td>{s['score']}</td></tr>\n"Alice (92 >= 90) and Carol (95 >= 90) get class='high'; Bob (78 < 90) does not. [1] The if/else inside the loop selects the correct tag string for each student. [1]Alice（92 >= 90）和 Carol（95 >= 90）得到 class='high'；Bob（78 < 90）不得到。[1]循环内的 if/else 为每个学生选择正确的标签字符串。[1]
The raw JSON score values (e.g., 92, 78, 95) are data with no visual presentation or context; the program transforms them into a labelled, formatted HTML table that a user can read and understand, converting raw data into information. [1]原始 JSON 成绩值（如 92、78、95）是没有视觉呈现或上下文的数据；程序将其转换为用户可以阅读和理解的带标签的格式化 HTML 表格，将原始数据转换为信息。[1]