nerdexam
Google

PROFESSIONAL-MACHINE-LEARNING-ENGINEER · Question #6

You are developing a Kubeflow pipeline on Google Kubernetes Engine. The first step in the pipeline is to issue a query against BigQuery. You plan to use the results of that query as the input to the n

The correct answer is D. Locate the Kubeflow Pipelines repository on GitHub.. Kubeflow pipelines have different types of components, ranging from low- to high-level. They have a ComponentStore that allows you to access prebuilt functionality from GitHub. https://github.com/kubeflow/pipelines/blob/master/components/gcp/bigquery/query/sample.ipynb https://v0

Submitted by salim_om· Apr 18, 2026ML pipeline operationalization

Question

You are developing a Kubeflow pipeline on Google Kubernetes Engine. The first step in the pipeline is to issue a query against BigQuery. You plan to use the results of that query as the input to the next step in your pipeline. You want to achieve this in the easiest way possible. What should you do?

Options

  • AUse the BigQuery console to execute your query, and then save the query results into a new
  • BWrite a Python script that uses the BigQuery API to execute queries against BigQuery.
  • CUse the Kubeflow Pipelines domain-specific language to create a custom component that uses
  • DLocate the Kubeflow Pipelines repository on GitHub.

How the community answered

(20 responses)
  • A
    5% (1)
  • B
    5% (1)
  • C
    15% (3)
  • D
    75% (15)

Explanation

Kubeflow pipelines have different types of components, ranging from low- to high-level. They have a ComponentStore that allows you to access prebuilt functionality from GitHub. https://github.com/kubeflow/pipelines/blob/master/components/gcp/bigquery/query/sample.ipynb https://v0-5.kubeflow.org/docs/pipelines/reusable-components/

Topics

#Kubeflow Pipelines#BigQuery#Pipeline Components#GCP Integration

Community Discussion

No community discussion yet for this question.

Full PROFESSIONAL-MACHINE-LEARNING-ENGINEER Practice