AGENT VIEW · Mokabla · Question · statistics-and-sql-screen
AgentGate · request ag_ee2d8d5d4795

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declared   ClaudeBot · Anthropic   · not verified

The user agent is ClaudeBot (Anthropic), which identifies itself as an AI agent.

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This page

page
question
human URL
https://www.mokabla.com/en/c/huawei/statistics-and-sql-screen

State

question
object · slug, title, difficulty, tags, is_free, locked, frequency, last_reported_at, url, agent, company, body_md, follow_ups_md
paid
false
access
Question bodies are paid content: a person buys 30 days of access per company (200 EGP or 200 points) in the human app. Agents get titles, tags and difficulty; bodies only for free questions.
{
  "question": {
    "slug": "statistics-and-sql-screen",
    "title": "Statistics and SQL Screen",
    "difficulty": "medium",
    "tags": [
      "sql",
      "data",
      "technical",
      "data-sci"
    ],
    "is_free": true,
    "locked": false,
    "frequency": 0.25,
    "last_reported_at": "2025-07-13T10:12:56.714334Z",
    "url": "https://www.mokabla.com/en/c/huawei/statistics-and-sql-screen",
    "agent": {
      "page": "https://www.mokabla.com/agent/en/c/huawei/statistics-and-sql-screen",
      "question": "https://www.mokabla.com/agent/_call/get_question?company=huawei&question=statistics-and-sql-screen"
    },
    "company": {
      "slug": "huawei",
      "name_en": "Huawei Egypt"
    },
    "body_md": "## What they ask\nFor data roles in the Cairo office the technical round leans almost entirely on two things: statistics fundamentals and SQL. The interviewer alternates between short conceptual questions and small query exercises you talk through out loud.\n\nQuestions in this area, for example:\n\n**Statistics**\n- When is the median a better summary than the mean, and give a telecom example.\n- Explain a p-value to a non-statistician without saying \"probability of the null\".\n- You have KPI samples from two groups of cell sites; which test would you use to compare them and what assumptions are you making?\n\n**SQL**, on a table `alarms(site_id, severity, opened_at, closed_at)`:\n1. Sites that raised more than five `critical` alarms last month.\n2. Average time to close, per severity, for alarms that were actually closed.\n3. For each site, the most recent alarm (one row per site).\n\n## Example\nFor item 3 the expected answer is a window function (`ROW_NUMBER() OVER (PARTITION BY site_id ORDER BY opened_at DESC)`) filtered to row 1; a `GROUP BY` with `MAX(opened_at)` alone loses the other columns, and they will point that out.\n\n## What they look for\n- Comfortable, unhurried explanations of basic statistics; they care that you can reason about data, not recite formulas.\n- Correct handling of `NULL` in `closed_at`, date arithmetic and grouping.\n- Whether you ask about the size of the table before proposing a query, which signals real-world experience.",
    "follow_ups_md": "- How would you detect sites whose alarm rate is anomalous compared with their own history?\n- What changes in your queries if `alarms` has a billion rows?"
  },
  "paid": false,
  "access": "Question bodies are paid content: a person buys 30 days of access per company (200 EGP or 200 points) in the human app. Agents get titles, tags and difficulty; bodies only for free questions."
}

Actions you may take here

actioninputpermission
get_question
One question with its body. Bodies are paid content unless the question is free.
company: string, question: stringAllowedcall →
list_questions
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Notices

This classification is final for the session: it came from a strong signal, not a guess. The human pages are not served to agents.
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