Your business users need a way to clean and prepare data before using the data for analysis. Your business users are less technically savvy and prefer to work with graphical user interfaces to…

Question

Options

• AUse Dataprep to clean the data, and write the results to BigQuery. Analyze the data by using Connected Sheets.
• BUse Dataprep to clean the data, and write the results to BigQuery. Analyze the data by using Looker Studio.
• CUse Dataflow to clean the data, and write the results to BigQuery. Analyze the data by using Connected Sheets.
• DUse Dataflow to clean the data, and write the results to BigQuery. Analyze the data by using Looker Studio.

• A
  84% (36)
• B
  2% (1)
• C
  5% (2)
• D
  9% (4)

Explanation

Option A is correct because Dataprep is a GUI-driven, no-code data wrangling tool built for non-technical users - it lets them visually define transformations without writing code - and Connected Sheets embeds BigQuery data directly inside Google Sheets, satisfying the requirement to analyze in a spreadsheet.

Why the distractors fail:

• B gets the transformation tool right (Dataprep) but swaps the analysis tool for Looker Studio, which is a BI dashboarding tool, not a spreadsheet.
• C uses Connected Sheets correctly but chooses Dataflow for transformation, which requires writing Apache Beam pipelines in Java or Python - not suitable for less technical users.
• D is wrong on both counts: Dataflow requires coding, and Looker Studio is not a spreadsheet.

Memory tip: Match the user persona to the tool - "Dataprep = prepared for non-techies (GUI), Connected Sheets = it's literally Sheets." Whenever you see "non-technical + GUI + spreadsheet," the answer is always Dataprep → BigQuery → Connected Sheets.

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