Day 30/30 SQL, Python, ETL, Data Modelling Challenge FREE Solutions

April 4th CHALLENGE – unlock solutions + reasoning

SQL Challenge – RANK vs DENSE_RANK

Challenge Recap:
❓ Which statement is TRUE about RANK() and DENSE_RANK()?

✅ Answer: B - RANK() skips ranks after ties, but DENSE_RANK() does not

Explanation:

• RANK() gives the same rank to tied rows and skips the next rank(s).

• DENSE_RANK() gives the same rank to tied rows but continues with the next consecutive rank.

Best Practices:

• Use RANK() when gaps in rank are important (e.g., competitions)

• Use DENSE_RANK() for continuous, gap-free ranks in reports

• Combine with PARTITION BY for group-wise ranking

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Python Challenge – List Comprehensions with Filtering

Challenge Recap:

nums = [1, 2, 3, 4, 5] 
evens = [x for x in nums if x % 2 == 0] 
print(evens)

✅ Answer: B - [2, 4]

Explanation:
This list comprehension filters even numbers from the list using x % 2 == 0.

Best Practices:

• Use inline conditions for clean, readable filtering

• Avoid complex expressions—offload to helper functions if needed

• Prefer comprehensions over loops for simple transformations

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ETL Challenge – Benefit of Incremental Loads

Challenge Recap:
❓ What is the primary benefit of incremental loading in ETL?

✅ Answer: C - It reduces data volume and speeds up processing

Explanation:
Incremental loads process only new or updated records, improving efficiency, reducing costs, and minimizing processing time.

Best Practices:

• Track changes using last_updated or CDC fields

• Store pipeline state (e.g., last run timestamp)

• Use idempotent merge/upsert logic to avoid duplication

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Data Modeling Challenge – OLAP Systems

Challenge Recap:
❓ Which of the following best describes OLAP systems?

✅ Answer: B - Focused on complex analytical queries and reporting

Explanation:
OLAP systems are designed for read-heavy, analytical workloads using dimensional models. They power dashboards, insights, and aggregated reporting.

Best Practices:

• Use star/snowflake schema for dimensional modeling

• Leverage OLAP for BI tools (Power BI, Tableau, Looker)

• Apply indexing, partitioning, and surrogate keys for performance

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