You have a variety of files in Cloud Storage that your data science team wants to use in their models. Currently, users do not have a method to explore, cleanse, and validate the data in Cloud…

Question

Options

• AProvide the data science team access to Dataflow to create a pipeline to prepare and validate the raw data and load data into BigQuery for data exploration.
• BCreate an external table in BigQuery and use SQL to transform the data as necessary. Provide the data science team access to the external tables to explore
• CLoad the data into BigQuery and use SQL to transform the data as necessary. Provide the data science team access to staging tables to explore the raw data.
• DProvide the data science team access to Dataprep to prepare, validate, and explore the data within Cloud Storage.

• A
  3% (1)
• B
  5% (2)
• C
  11% (4)
• D
  81% (30)

Explanation

Dataprep (option D) is the correct choice because it is a low-code, visual data preparation tool built specifically for exploring, cleansing, and validating data directly from Cloud Storage - no coding or pipeline engineering required, making it ideal for data scientists who need to move quickly.

Option A is wrong because Dataflow is a code-heavy pipeline orchestration service (Apache Beam); while powerful, it requires significant engineering effort and is not a "low code" solution for ad-hoc exploration.

Option B is wrong because creating external BigQuery tables still requires SQL expertise and the step of structuring/querying data manually - it doesn't provide an interactive, visual cleansing workflow, and external tables add latency and don't fully "prepare" the data.

Option C is wrong because loading data into BigQuery first adds an unnecessary ingestion step, and using SQL for transformation is still a code-based approach rather than a low-code exploration tool.

Memory tip: Think of the keyword triggers - "low code + cleanse + validate + explore + Cloud Storage" = Dataprep. Whenever an exam question emphasizes low/no-code data preparation with a visual interface, Dataprep is almost always the answer over Dataflow (code-heavy pipelines) or BigQuery (SQL-based analysis).

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