SOL-C01 Question #235: Real Exam Question with Answer & Explanation

Question

A data engineering team is tasked with building a data pipeline that ingests semi-structured data (JSON) from various sources into Snowflake. They want to optimize query performance on this data. Which of the following techniques would be MOST effective in improving query performance when querying the JSON data in Snowflake?

Options

• ALoad the entire JSON document as a single VARIANT column and query it directly using SQL
• BParse the JSON data during ingestion and flatten it into relational tables with appropriate data
• CStore the JSON data as a string and use regular expressions to extract the required information
• DCreate a separate virtual warehouse specifically for querying the JSON data, regardless of the
• EUse Snowflake's search optimization service only after understanding the most common search

Explanation

Option B provides the best performance. Flattening the JSON data into relational tables allows Snowflake's query optimizer to take advantage of indexes and statistics. Option A can be slow for complex queries due to the overhead of parsing JSON on the fly. Option C is inefficient due to the cost of regular expression processing. Option D doesn't address the underlying issue of data structure. Option E, using search optimization service, is helpful but it works after parsing and only on specific search patterns. Structuring the data beforehand is more efficient. The best solution will depend on the JSON Structure.

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