Day 21: Review + Phase 1 Assessment

Day 21 — Review + Phase 1 Assessment

Coding problem

Solution outline (coding)

• Skim Weeks 1–2 problem list; pick 1 easy + 1 medium you would not ace cold.
• Do a timed run: 15–20 min coding + 5 min complexity explanation.
• Grade against rubric: correctness, speed, communication, edge cases (empty, single node, overflow).

Time complexity: Varies — assessment simulation.

Space complexity: Varies.

class ReviewDay:
  """Practice / review: Review + Phase 1 Assessment."""

  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: Review / mixed (see weekly theme)

This is a review / mixed day. Expect SQL that blends data quality, funnels, and metric definitions—the same mix you see across consumer tech and ads analytics.

What you are asked to write (SQL prompt):

Review / mixed week — use the same tables and deliverables as in a standard onsite SQL round.
Using all Phase 1 tables, design a BigQuery job health dashboard: job run counts, failure rates, average runtime, and data freshness for each critical table.

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 Review to SQL: say the relational equivalent (e.g. hash map → GROUP BY + key; two pointers → ordered window + filter).
• Rates: SAFE_DIVIDE or NULLIF; define numerator and denominator.
• Structure: CTEs (WITH) — one step per CTE; validate on a tiny slice (counts, nulls, duplicates).

WITH jobs AS (
  SELECT job_id, table_name, run_ts, status, runtime_sec
  FROM job_runs
)
SELECT table_name,
  COUNT(*) AS runs,
  AVG(runtime_sec) AS avg_runtime,
  COUNTIF(status != 'SUCCESS') / COUNT(*) AS failure_rate
FROM jobs
GROUP BY table_name;