Enterprise User Email Policy Filter

Easy

A platform trust team is auditing account emails against a strict signup policy. Table: users user_id: unique account identifier name: account display name mail: submitted email address An email is considered policy-compliant when all rules hold: The domain is exactly '@example.test' (case-sensitive). The prefix before the domain starts with a letter (A-Z or a-z). Every character in the prefix is one of: letters (A-Z, a-z) digits (0-9) underscore (_) period (.) dash (-) Task: Return all rows from users whose mail is policy-compliant. Output requirements: Return exactly three columns in this order: user_id name mail Result rows may be returned in any order. Supported submission environments: SQL (SQLite 3.27.2) Pandas (Python)

Example 1

Input

users:
| user_id | name      | mail                      |
|---------|-----------|---------------------------|
| 1       | Winston   | winston@example.test      |
| 2       | Jonathan  | jonathanisgreat           |
| 3       | Annabelle | bella-@example.test       |
| 4       | Sally     | sally.come@example.test   |
| 5       | Marwan    | quarz#2020@example.test   |
| 6       | David     | david69@example.net         |
| 7       | Shapiro   | .shapo@example.test       |

Output

[
  {"user_id":1,"name":"Winston","mail":"winston@example.test"},
  {"user_id":3,"name":"Annabelle","mail":"bella-@example.test"},
  {"user_id":4,"name":"Sally","mail":"sally.come@example.test"}
]

Explanation

Rows 1, 3, and 4 satisfy all policy rules. Other rows fail due to missing domain, invalid characters, wrong domain, or invalid starting character.

Example 2

Input

users:
| user_id | name    | mail                     |
|---------|---------|--------------------------|
| 10      | Asha    | A_1-2.x@example.test     |
| 11      | Ben     | 0ben@example.test        |
| 12      | Cia     | cia@Example.test         |
| 13      | Dori    | dori@example.test        |
| 14      | Erin    | erin__@example.test      |

Output

[
  {"user_id":10,"name":"Asha","mail":"A_1-2.x@example.test"},
  {"user_id":13,"name":"Dori","mail":"dori@example.test"},
  {"user_id":14,"name":"Erin","mail":"erin__@example.test"}
]

Explanation

User 11 fails because the prefix starts with a digit. User 12 fails because the domain must match exact lowercase '@example.test'.

Example 3

Input

users:
| user_id | name   | mail                   |
|---------|--------|------------------------|
| 20      | Nila   | @example.test          |
| 21      | Omar   | -omar@example.test     |
| 22      | Pia    | pia@example.tes        |
| 23      | Quinn  | q+in@example.test      |

Output

[]

Explanation

All rows violate at least one rule: empty prefix, non-letter prefix start, wrong domain, or disallowed '+' in prefix.

Constraints

0 <= number of rows in users <= 200000
1 <= user_id <= 1000000000
user_id is unique
name and mail are non-null text values
mail length is at least 1 and at most 320 characters
Domain check must be exact and case-sensitive: @example.test
Prefix must start with [A-Za-z]
Allowed prefix characters: [A-Za-z0-9_.-]
Return exactly columns: user_id, name, mail
Rows may be returned in any order
For SQL submissions, use SQLite 3.27.2 semantics
For Pandas submissions, return a DataFrame with columns: user_id, name, mail

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Enterprise User Email Policy Filter

Easy

A platform trust team is auditing account emails against a strict signup policy. Table: users user_id: unique account identifier name: account display name mail: submitted email address An email is considered policy-compliant when all rules hold: The domain is exactly '@example.test' (case-sensitive). The prefix before the domain starts with a letter (A-Z or a-z). Every character in the prefix is one of: letters (A-Z, a-z) digits (0-9) underscore (_) period (.) dash (-) Task: Return all rows from users whose mail is policy-compliant. Output requirements: Return exactly three columns in this order: user_id name mail Result rows may be returned in any order. Supported submission environments: SQL (SQLite 3.27.2) Pandas (Python)

Example 1

Input

users:
| user_id | name      | mail                      |
|---------|-----------|---------------------------|
| 1       | Winston   | winston@example.test      |
| 2       | Jonathan  | jonathanisgreat           |
| 3       | Annabelle | bella-@example.test       |
| 4       | Sally     | sally.come@example.test   |
| 5       | Marwan    | quarz#2020@example.test   |
| 6       | David     | david69@example.net         |
| 7       | Shapiro   | .shapo@example.test       |

Output

[
  {"user_id":1,"name":"Winston","mail":"winston@example.test"},
  {"user_id":3,"name":"Annabelle","mail":"bella-@example.test"},
  {"user_id":4,"name":"Sally","mail":"sally.come@example.test"}
]

Explanation

Rows 1, 3, and 4 satisfy all policy rules. Other rows fail due to missing domain, invalid characters, wrong domain, or invalid starting character.

Example 2

Input

users:
| user_id | name    | mail                     |
|---------|---------|--------------------------|
| 10      | Asha    | A_1-2.x@example.test     |
| 11      | Ben     | 0ben@example.test        |
| 12      | Cia     | cia@Example.test         |
| 13      | Dori    | dori@example.test        |
| 14      | Erin    | erin__@example.test      |

Output

[
  {"user_id":10,"name":"Asha","mail":"A_1-2.x@example.test"},
  {"user_id":13,"name":"Dori","mail":"dori@example.test"},
  {"user_id":14,"name":"Erin","mail":"erin__@example.test"}
]

Explanation

User 11 fails because the prefix starts with a digit. User 12 fails because the domain must match exact lowercase '@example.test'.

Example 3

Input

users:
| user_id | name   | mail                   |
|---------|--------|------------------------|
| 20      | Nila   | @example.test          |
| 21      | Omar   | -omar@example.test     |
| 22      | Pia    | pia@example.tes        |
| 23      | Quinn  | q+in@example.test      |

Output

[]

Explanation

All rows violate at least one rule: empty prefix, non-letter prefix start, wrong domain, or disallowed '+' in prefix.

Constraints

0 <= number of rows in users <= 200000
1 <= user_id <= 1000000000
user_id is unique
name and mail are non-null text values
mail length is at least 1 and at most 320 characters
Domain check must be exact and case-sensitive: @example.test
Prefix must start with [A-Za-z]
Allowed prefix characters: [A-Za-z0-9_.-]
Return exactly columns: user_id, name, mail
Rows may be returned in any order
For SQL submissions, use SQLite 3.27.2 semantics
For Pandas submissions, return a DataFrame with columns: user_id, name, mail