Using Pre-built Kubeflow Components for BigQuery Integration

PMLE By islever Editorial Last updated

Developing end-to-end ML pipelines. Building models given the task considering cost, complexity, latency, and scalability.

Short answer

D Locate the Kubeflow Pipelines repository on GitHub, find the BigQuery Query Component, copy its URL, and use it to load the component into your pipeline to execute queries against BigQuery.

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?
  1. Use the BigQuery console to execute your query, and then save the query results into a new BigQuery table.
  2. Write a Python script that uses the BigQuery API to execute queries against BigQuery. Execute this script as the first step in your Kubeflow pipeline.
  3. Use the Kubeflow Pipelines domain-specific language to create a custom component that uses the Python BigQuery client library to execute queries.
  4. Locate the Kubeflow Pipelines repository on GitHub. Find the BigQuery Query Component, copy that componentג€™s URL, and use it to load the component into your pipeline. Use the component to execute queries against BigQuery. Correct Answer

Key Takeaways

The trap lies in interpreting 'easiest' as 'most flexible'; the exam expects you to choose the pre-built component (D) to minimize coding effort, whereas options B and C require writing custom code.

To easily integrate BigQuery results into a Kubeflow pipeline, use the pre-built BigQuery Query Component loaded by URL rather than writing custom scripts or creating new components.

Common mistake: Candidates often choose Option B (Python script) because it feels more direct and familiar to developers, failing to recognize that the exam prioritizes the use of pre-built, reusable components to reduce development overhead.

Expert Analysis

The question asks for the easiest way to integrate a BigQuery query result into a Kubeflow pipeline step. In the context of Google Cloud certification exams, 'easiest' typically refers to minimizing custom code development and leveraging pre-built, managed components.

Option D is correct because it utilizes a pre-existing component from the official Kubeflow Pipelines repository. By loading the component via its URL (load_component_from_url), you avoid writing any Python logic or defining custom container images. This aligns with the exam's preference for using existing infrastructure and reusable components over building custom solutions when possible.

Option B (writing a Python script) and Option C (creating a custom component) require significant manual effort: writing code, handling dependencies, and potentially managing containers. While they are valid technical approaches, they are not the 'easiest' in terms of initial setup time and complexity compared to reusing a standard component. Option A is incorrect because it involves manual intervention outside the automated pipeline workflow, which contradicts the goal of an automated ML pipeline.

Comments highlight this distinction: one user notes that while writing a script (B) might seem simpler to some developers, the exam considers the pre-built component (D) as the intended answer for 'ease' due to zero-code integration. Another comment correctly identifies the function load_component_from_url as the key mechanism for using these pre-built components.

Official Reference

These links open the official vendor documentation in a new tab.

Exam Strategy

When a question asks for the 'easiest', 'simplest', or 'recommended' approach involving cloud services and pipelines, look first for pre-built connectors, operators, or components provided by the vendor or the open-source community. Avoid selecting options that require writing custom code unless the scenario explicitly demands unique logic not covered by existing tools.

Frequently Asked Questions

Why isn't writing a Python script (Option B) considered the easiest?

While writing a script is straightforward for developers, it requires managing imports, error handling, and potentially containerizing the code. The exam defines 'easiest' as leveraging pre-built components to eliminate this boilerplate work.

How do I load a pre-built Kubeflow component?

You can load a pre-built component using the kfp.components.load_component_from_url() function, passing it the direct URL to the component's YAML definition file hosted in a repository like GitHub.

More PMLE FAQ →

Community Votes

D
81%
B
19%

81% of anonymous learners picked answer D. Votes are pick records left by other test-takers — they are not the verified answer.

Discussion 27 contributions

Compiled from public community threads and reader submissions, lightly edited for clarity. Handles and vote tallies are shown as recorded, and comments reflect the views of individual contributors.

From public community threads 27

  1. maartenalexander 👍 26
    D. 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.
  2. NamitSehgal 👍 8 Answered D
    Not sure what is the reason behind putting A as it is manual and manual steps can not be part of automation. I would say Answer is D as it just require a clone of the component from github. Using a Python and import bigquery component may sounds good too, but ask was what is easiest. It depends how word "easy" is taken by individuals but definitely not A.
  3. gcp2021go 👍 6
    agree, links: https://github.com/kubeflow/pipelines/blob/master/components/gcp/bigquery/query/sample.ipynb; https://v0-5.kubeflow.org/docs/pipelines/reusable-components/
  4. raviperi 👍 6
    why create a custom component when a big query's reusable component is already present. Answer is D.
  5. Amabo 👍 4
    from kfp.components import load_component_from_url bigquery_query_op = load_component_from_url('https://raw.githubusercontent.com/kubeflow/pipelines/master/components/gcp/bigquery/query/component.yaml') def my_pipeline(): query_result = bigquery_query_op( project_id='my-project', query='SELECT * FROM my_dataset.my_table' ) # Use the query_result as input to the next step in the pipeline
Show 22 more comments
  1. chohan 👍 4
    Should be B
  2. friedi 👍 3 Answered B
    Very confused as to why D is the correct answer. To me it seems a) much simpler to just write a couple of lines of python (https://cloud.google.com/bigquery/docs/reference/libraries#client-libraries-install-python) and b) the documentation for the BigQuery reusable component (https://v0-5.kubeflow.org/docs/pipelines/reusable-components/) states that the data is written to Google Cloud Storage, which means we have to write the fetching logic in the next pipeline step, going against the "as simple as possible" requirement. Would be interested to hear why I am wrong.
  3. kaike_reis 👍 3
    D. The easiest way possible in developer's world: copy code from stackoverflow or github hahaha. Jokes a part, I think D is the correct. (A) is manual, so you have to do always. (B) could be, but is not the easiest one because you need to write a script for this. (C) uses Kubeflow intern solution, but you need to work to create a custom component. (D) is the (C) solution, but easier using a component created previously to do the job.
  4. David_ml 👍 3
    A is wrong. Answer is D. It's a pipeline which means you will run it multiple times? Do you always want to make the query manually each time you run your pipeline?
  5. taksan 👍 3 Answered D
    D is the correct answer, as reusing an existing component is the most streamlined way to interact with BigQuery.
  6. fragkris 👍 3 Answered B
    Im going "against the flow" and chosing B. It just sounds a lot easier option than D.
  7. PhilipKoku 👍 3 Answered B
    B) Python API
  8. David_ml 👍 3 Answered D
    Answer is D.
  9. xiaoF 👍 3
    D is good.
  10. tavva_prudhvi 👍 2
    Yup, you raised valid points. Depending on your specific requirements and familiarity with Python, writing a custom script using the BigQuery API (Option B) can be a simpler and more flexible approach. With Option B, you can write a Python script that uses the BigQuery API to execute queries against BigQuery and fetch the data directly into your pipeline. This way, you can process the data as needed and pass it to the next step in the pipeline without the need to fetch it from Google Cloud Storage. While using the reusable BigQuery Query Component (Option D) provides a pre-built solution, it does require additional steps to fetch the data from Google Cloud Storage for the next step in the pipeline, which might not be the simplest approach.
  11. friedi 👍 2
    Actually, the problem statement even says that the query result has to be used as input to the next step, meaning with answer D) we would have to download the results before passing them to the next step. Additionally, we would have to handle potentially existing files in Google Cloud Storage if the pipeline is either executed multiple times or even in parallel. (I will die on this hill 😆 ).
  12. b7ad1d9 👍 2 Answered D
    D The recommended approach by Google Cloud for integrating BigQuery (BQ) data into a Kubeflow Pipeline is to use the Google Cloud Pipeline Components (GCPC), which include purpose-built components for BigQuery operations. This is also the most automated option
  13. aepos 👍 2
    The result of D is just the path to the Cloud Storage where the result is stored not the data itself. So the input to the next step is this path, where you still have to load the data? So i would guess B. Can anyone explain if i am wrong?
  14. desertlotus1211 👍 2
    Writing a Python script using the BigQuery API is possible, but it's more complex than using an existing component. It requires more development effort and doesn't take advantage of the pre-built components available in Kubeflow.
  15. nktyagi 👍 2 Answered B
    much simpler to just write a couple of lines of python
  16. Mohamed_Mossad 👍 2 Answered D
    https://linuxtut.com/en/f4771efee37658c083cc/
  17. M25 👍 2 Answered D
    Went with D
  18. celia20200410 👍 1
    ans: c https://medium.com/google-cloud/using-bigquery-and-bigquery-ml-from-kubeflow-pipelines-991a2fa4bea8 https://cloud.google.com/architecture/architecture-for-mlops-using-tfx-kubeflow-pipelines-and-cloud-build#kubeflow-piplines-components Kubeflow Pipelines, a containerized task can invoke other services such as BigQuery jobs, AI Platform (distributed) training jobs, and Dataflow jobs.
  19. taylz876 👍 1 Answered D
    D- Kubeflow Pipelines already provides a prebuilt component for running BigQuery queries. You load it by its URL, and it executes the query and passes the result to the next step. There's no code to write, which makes it the easiest way, as the question requires.
  20. donchoripan 👍 1
    A. it says the easiest way possible so it sounds like just running the query on the console should be enogh. It doesn't says that the data will need to be uploaded again anytime soon, so we can asume that its just a one time query to be run.
  21. Mohamed_Mossad 👍 1
    answer between C,D but above link has an article which uses a ready .yml file for bigquery component on official kubeflow pipelines repo
  22. jsalvasoler 👍 1 Answered B
    Clearly B

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