The correct answer is C: SELECT SUBSTRING_INDEX(product_description, ' ', 1) as product_name FROM dataset.

Why C is the best option

• SUBSTRING_INDEX(str, delim, count) is a built-in Spark SQL function (supported in Azure Databricks / Spark) specifically designed for this exact use case.
• When count = 1 and delim = ' ', it efficiently returns everything before the first occurrence of the space — i.e., precisely the first word.
• It is scalar (operates directly on the string without creating intermediate collections), making it lightweight and performant on large datasets.
• No extra array handling or indexing is required.

Example behavior:

• 'Wireless Headphones with Noise Cancellation' → 'Wireless'
• 'Laptop' (no space) → 'Laptop'
• Handles leading/trailing spaces reasonably well for this simple requirement.

Why not the other options?

• A: SELECT SPLIT(product_description, ' ')[0] as product_name FROM dataset
  This also works (SPLIT returns an array, and [0] grabs the first element). It is a common and valid pattern in Spark SQL. However, it creates a full array for every row even when you only need the first element. This makes it slightly less efficient than SUBSTRING_INDEX, especially on very wide tables or extremely large datasets. For most cases the difference is negligible, but the question emphasizes "efficiency and optimal performance" on a large dataset, so C edges it out.

• B: SELECT product_description FROM dataset WHERE product_description LIKE '%product_name%'
  Completely incorrect.

  • It doesn't extract anything — it just filters rows.
  • The literal string 'product_name' in the LIKE clause has no relation to the actual product name.
  • This would return the full description (or nothing), not the extracted first word.

• D: SELECT product_description as product_name FROM dataset LIMIT 1
  Wrong on multiple levels.

  • It aliases the entire description column (no extraction).
  • LIMIT 1 returns only one arbitrary row instead of processing the whole dataset.
  • Useless for transforming every row.

Performance & Best Practices (for the exam)

• For simple "first word" extraction on large data → prefer SUBSTRING_INDEX when available (it stops at the first delimiter).
• If you need more advanced parsing (multiple spaces, punctuation, etc.) → consider regexp_extract(product_description, '^(\\S+)', 1) or a combination with TRIM.
• Both SPLIT and SUBSTRING_INDEX are vectorized and run well in Spark's Catalyst optimizer, but SUBSTRING_INDEX avoids array creation overhead.
• Always test with realistic data volumes in Databricks (use the Spark UI to compare query plans and shuffle/CPU metrics if performance is critical).

Recommended final query (clean & robust):

SELECT 
  SUBSTRING_INDEX(TRIM(product_description), ' ', 1) AS product_name
FROM dataset;

SELECT 
  SUBSTRING_INDEX(TRIM(product_description), ' ', 1) AS product_name
FROM dataset;

This trims any leading/trailing whitespace first for extra safety.