Day 76: FULL MOCK: Meta

Day 76 — FULL MOCK: Meta

Coding problem

Solution outline (coding)

• Follow the mock packet: read constraints, state brute force, then optimize.
• For each problem: 5 min plan, 20 min code, 5 min tests and complexity.
• Log misses: communication, edge cases, or slow pattern recognition.

Time complexity: Varies — mock setting.

Space complexity: Varies.

class ReviewDay:
  """Practice / review: FULL MOCK: Meta."""

  def practice_plan(self):
    return [
      "Pick 2–3 problems from this phase; re-solve timed without notes.",
      "For each: pattern name, time/space complexity, one alternative approach.",
    ]


# Input:  (your choice of problems from this week or phase)
# Output: a short list of gaps to drill before the next session

SQL interview practice

1. Interview question

Companies / track: Meta

Meta: expect event logging, ads / integrity, and social product analytics—deduping, sessionization, and window functions are frequent themes.

What you are asked to write (SQL prompt):

Frame this as metrics work for **Meta**-scale surfaces (ads, product, or engagement — as the tables suggest).
Design a Meta-style mock in BigQuery: one product-analytics query and one reliability query (e.g., error spike detection), emphasizing readability and explainability.

Tables implied by the prompt:

• query(e.g., error spike detection)

Engine: BigQuery — use its date, array, and approximate functions as documented.

2. Solution outline

• Clarify out loud: result grain (one row per what?), join keys, time zone, and any ORDER BY / LIMIT / tie-breakers.
• Map Mock to SQL: say the relational equivalent (e.g. hash map → GROUP BY + key; two pointers → ordered window + filter).
• Structure: CTEs (WITH) — one step per CTE; validate on a tiny slice (counts, nulls, duplicates).

SELECT DATE(ts) AS d, COUNT(*) AS events, COUNTIF(error) / COUNT(*) AS error_rate
FROM product_events
GROUP BY d;