You are designing storage for 20 TB of text files as part of deploying a data pipeline on Google Cloud. Your input data is in CSV format. You want to minimize the cost of querying aggregate values…

Question

Options

• AUse Cloud Bigtable for storage. Install the HBase shell on a Compute Engine instance to query the Cloud Bigtable data.
• BUse Cloud Bigtable for storage. Link as permanent tables in BigQuery for query.
• CUse Cloud Storage for storage. Link as permanent tables in BigQuery for query.
• DUse Cloud Storage for storage. Link as temporary tables in BigQuery for query.

• A
  5% (2)
• B
  2% (1)
• C
  84% (37)
• D
  9% (4)

Explanation

Option C - Cloud Storage with BigQuery permanent tables - is correct because Cloud Storage is the most cost-effective solution for storing large volumes of CSV/text files at 20 TB scale, and linking them as permanent external tables in BigQuery allows multiple users and engines to query the data without duplicating it, minimizing both storage and query costs.

Why the distractors fail:

• A & B (Cloud Bigtable): Bigtable is a NoSQL wide-column store optimized for low-latency reads/writes on structured key-value data - not designed for aggregate analytical queries over large text/CSV files, and it's significantly more expensive than Cloud Storage for this use case.
• B specifically: BigQuery cannot link Bigtable as permanent external tables in the way described; Bigtable and BigQuery integration works differently and doesn't suit this scenario.
• D (temporary tables): Temporary tables exist only for the duration of a session or query job, so multiple users cannot share them persistently - this defeats the multi-user requirement and adds overhead recreating them repeatedly.

Memory tip: Think "CSV + aggregate queries + multiple users + low cost = Cloud Storage + BigQuery permanent tables." The word permanent is the key differentiator from D - if data needs to be reused across users and sessions, the table linkage must be permanent.

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