Social Feed Compliance Screening

Easy

A trust-and-safety pipeline scans social posts to flag records that violate publishing limits. Table: Tweets tweet_id: unique post identifier content: raw post text A post is considered invalid if it violates at least one rule: content length is strictly greater than 140 characters content contains more than 3 '@' characters content contains more than 3 '#' characters Task: Return all invalid post IDs. Output requirements: Return exactly one column in this order: tweet_id Sort output by tweet_id in ascending order. Supported submission environments: SQL (SQLite 3.27.2) Pandas (Python)

Example 1

Input

Tweets:
| tweet_id | content                                                          |
|----------|------------------------------------------------------------------|
| 1        | launching now @ops @infra @qa @support                           |
| 2        | daily digest #news #product #release #mobile                     |
| 3        | rollout complete @ops #stable                                    |

Output

[
  {"tweet_id": 1},
  {"tweet_id": 2}
]

Explanation

Tweet 1 has 4 mentions and tweet 2 has 4 hashtags. Tweet 3 satisfies all limits.

Example 2

Input

Tweets:
| tweet_id | content                                                         |
|----------|-----------------------------------------------------------------|
| 10       | short update @a @b #x                                           |
| 11       | exactly three mentions @a @b @c and three tags #a #b #c        |
| 12       | status ok                                                       |

Output

[]

Explanation

All tweets remain within the 140-character, mention, and hashtag limits.

Example 3

Input

Tweets:
| tweet_id | content                                                                 |
|----------|-------------------------------------------------------------------------|
| 21       | xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx |
| 22       | spike @u1 @u2 @u3 @u4 #urgent #audit #ops #incident                     |
| 23       | brief post #ok                                                          |

Output

[
  {"tweet_id": 21},
  {"tweet_id": 22}
]

Explanation

Tweet 21 exceeds the character limit. Tweet 22 violates both mention and hashtag limits.

Constraints

1 <= number of rows in Tweets <= 200000
1 <= tweet_id <= 2000000000
tweet_id is unique per test case
1 <= content length <= 300
A tweet is invalid if any one of the three policy checks fails
Character counting uses full string length in SQLite and Pandas
Mention counting is based on occurrences of '@'
Hashtag counting is based on occurrences of '#'
Return exactly one column: tweet_id
Sort final output by tweet_id ASC
SQL submissions run on SQLite 3.27.2
Pandas submissions must return a DataFrame with column [tweet_id]

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Social Feed Compliance Screening

Easy

A trust-and-safety pipeline scans social posts to flag records that violate publishing limits. Table: Tweets tweet_id: unique post identifier content: raw post text A post is considered invalid if it violates at least one rule: content length is strictly greater than 140 characters content contains more than 3 '@' characters content contains more than 3 '#' characters Task: Return all invalid post IDs. Output requirements: Return exactly one column in this order: tweet_id Sort output by tweet_id in ascending order. Supported submission environments: SQL (SQLite 3.27.2) Pandas (Python)

Example 1

Input

Tweets:
| tweet_id | content                                                          |
|----------|------------------------------------------------------------------|
| 1        | launching now @ops @infra @qa @support                           |
| 2        | daily digest #news #product #release #mobile                     |
| 3        | rollout complete @ops #stable                                    |

Output

[
  {"tweet_id": 1},
  {"tweet_id": 2}
]

Explanation

Tweet 1 has 4 mentions and tweet 2 has 4 hashtags. Tweet 3 satisfies all limits.

Example 2

Input

Tweets:
| tweet_id | content                                                         |
|----------|-----------------------------------------------------------------|
| 10       | short update @a @b #x                                           |
| 11       | exactly three mentions @a @b @c and three tags #a #b #c        |
| 12       | status ok                                                       |

Output

[]

Explanation

All tweets remain within the 140-character, mention, and hashtag limits.

Example 3

Input

Tweets:
| tweet_id | content                                                                 |
|----------|-------------------------------------------------------------------------|
| 21       | xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx |
| 22       | spike @u1 @u2 @u3 @u4 #urgent #audit #ops #incident                     |
| 23       | brief post #ok                                                          |

Output

[
  {"tweet_id": 21},
  {"tweet_id": 22}
]

Explanation

Tweet 21 exceeds the character limit. Tweet 22 violates both mention and hashtag limits.

Constraints

1 <= number of rows in Tweets <= 200000
1 <= tweet_id <= 2000000000
tweet_id is unique per test case
1 <= content length <= 300
A tweet is invalid if any one of the three policy checks fails
Character counting uses full string length in SQLite and Pandas
Mention counting is based on occurrences of '@'
Hashtag counting is based on occurrences of '#'
Return exactly one column: tweet_id
Sort final output by tweet_id ASC
SQL submissions run on SQLite 3.27.2
Pandas submissions must return a DataFrame with column [tweet_id]