Day 89: FULL MOCK: Weakest Topic

Day 89 — FULL MOCK: Weakest Topic

Coding problem

Solution outline (coding)

• From prior logs, pick the single weakest topic (e.g. DP, graphs, design).
• Do one targeted problem set only in that topic.
• Re-assess with one verification problem tomorrow.

Time complexity: Varies.

Space complexity: Varies.

class ReviewDay:
  """Practice / review: FULL MOCK: Weakest Topic."""

  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.
Weakest-topic mock: tag each prior SQL question by topic (window, partitioning, UDF, performance) and write a query to surface your weakest area from logged scores.

Tables implied by the prompt:

• topic(window, partitioning, UDF, performance)

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).
• Filter time first: predicate on DATE(ts) / partition column before heavy joins; state the window in plain English.
• Windows: align PARTITION BY / ORDER BY with the business rule; use QUALIFY in BigQuery when you need top-N per group.
• Arrays / UDFs: UNNEST and offsets for index; say when logic belongs in SQL vs a UDF.
• 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 topic, AVG(score) AS avg_score FROM sql_topic_scores GROUP BY topic ORDER BY avg_score ASC LIMIT 3;