Day 83: FULL MOCK: Google

Day 83 — FULL MOCK: Google

Coding problem

Solution outline (coding)

• Same structure as Meta mock: verbalize thought process continuously.
• Prioritize correctness on the first problem before speed on the second.
• Note Google-flavored emphasis: scale, testing, and clear abstractions.

Time complexity: Varies.

Space complexity: Varies.

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

  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: Google

Google: SQL screens usually assume BigQuery, Ads / Search / YouTube-style fact tables, and talking through bytes processed and partition pruning.

What you are asked to write (SQL prompt):

Frame this as metrics work for **Google**-scale surfaces (ads, product, or engagement — as the tables suggest).
Google-style mock: one BigQuery query for tree/graph analysis and one for product metrics, recording time/bytes and reasoning commentary.

Tables implied by the prompt:

• Infer schemas from the prompt and state them before coding.

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).
• Cost: selective columns, partition pruning, avoid SELECT * when tables are huge.
• Structure: CTEs (WITH) — one step per CTE; validate on a tiny slice (counts, nulls, duplicates).

SELECT 'tree' AS section, COUNT(*) FROM tree_stats
UNION ALL SELECT 'metrics', COUNT(*) FROM product_metrics;