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What are all of the BigQuery operations that Google charges for?
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Question 275 - Professional Data Engineer discussion

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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 Storage. You are looking for a low code solution that can be used by your data science team to quickly cleanse and explore data within Cloud Storage. What should you do?

A.
Load 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.
Answers
A.
Load 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.
B.
Provide 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.
Answers
B.
Provide 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.
C.
Provide the data science team access to Dataprep to prepare, validate, and explore the data within Cloud Storage.
Answers
C.
Provide the data science team access to Dataprep to prepare, validate, and explore the data within Cloud Storage.
D.
Create 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 the raw data.
Answers
D.
Create 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 the raw data.
Suggested answer: C

Explanation:

Dataprep is a low code, serverless, and fully managed service that allows users to visually explore, cleanse, and validate data in Cloud Storage. It also provides features such as data profiling, data quality, data transformation, and data lineage. Dataprep is integrated with BigQuery, so users can easily export the prepared data to BigQuery for further analysis or modeling. Dataprep is a suitable solution for the data science team to quickly and easily work with the data in Cloud Storage, without having to write code or manage infrastructure. The other options are not as suitable as Dataprep for this use case, because they either require more coding, more infrastructure management, or more data movement. Loading the data into BigQuery, either directly or through Dataflow, would incur additional costs and latency, and may not provide the same level of data exploration and validation as Dataprep. Creating an external table in BigQuery would allow users to query the data in Cloud Storage, but would not provide the same level of data cleansing and transformation as Dataprep.Reference:

Dataprep overview

Dataprep features

Dataprep and BigQuery integration

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asked 18/09/2024
Baheilu Tekelu
38 questions
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