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ASSOCIATE-CLOUD-ENGINEER · Question #287

Your company has a large quantity of unstructured data in different file formats. You want to perform ETL transformations on the data. You need to make the data accessible on Google Cloud so it can…

The correct answer is B. Upload the data to Cloud Storage using the gcloud storage command. To make a large quantity of unstructured data in various file formats accessible for processing by a Dataflow job on Google Cloud, the data should be uploaded to Cloud Storage, which serves as a scalable and flexible storage solution for raw data.

Submitted by jian89· Mar 30, 2026Deploying and implementing a cloud solution

Question

Your company has a large quantity of unstructured data in different file formats. You want to perform ETL transformations on the data. You need to make the data accessible on Google Cloud so it can be processed by a Dataflow job. What should you do?

Options

  • AUpload the data to BigQuery using the bq command line tool.
  • BUpload the data to Cloud Storage using the gcloud storage command.
  • CUpload the data into Cloud SQL using the import function in the Google Cloud console.
  • DUpload the data into Cloud Spanner using the import function in the Google Cloud console.

How the community answered

(61 responses)
  • A
    2% (1)
  • B
    87% (53)
  • C
    8% (5)
  • D
    3% (2)

Why each option

To make a large quantity of unstructured data in various file formats accessible for processing by a Dataflow job on Google Cloud, the data should be uploaded to Cloud Storage, which serves as a scalable and flexible storage solution for raw data.

AUpload the data to BigQuery using the bq command line tool.

BigQuery is a data warehouse designed for structured and semi-structured data, and while Dataflow can interact with it, it's not typically used as the primary raw storage for arbitrary unstructured files before ETL.

BUpload the data to Cloud Storage using the gcloud storage command.Correct

Cloud Storage is the ideal service for storing large quantities of unstructured data in various file formats, acting as a highly scalable and durable landing zone, and Dataflow jobs can directly read from and write to Cloud Storage buckets.

CUpload the data into Cloud SQL using the import function in the Google Cloud console.

Cloud SQL is a relational database service and is not suitable for storing large quantities of unstructured data in various file formats.

DUpload the data into Cloud Spanner using the import function in the Google Cloud console.

Cloud Spanner is a globally distributed relational database and, like Cloud SQL, is not designed for storing raw unstructured files.

Concept tested: Choosing appropriate storage for unstructured data and Dataflow integration

Source: https://cloud.google.com/storage/docs/introduction

Topics

#Cloud Storage#ETL#Dataflow#data ingestion

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