Google Professional Machine Learning Engineer
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You are developing a Kubeflow pipeline on Google Kubernetes Engine (GKE) to automate your machine learning workflow. The first step in the pipeline is to issue a query against BigQuery to extract data. The results of this query will be used as input for the next step in your pipeline. Considering the need for simplicity and efficiency in implementing this step, what should you do?
You are developing a Kubeflow pipeline on Google Kubernetes Engine (GKE) to automate your machine learning workflow. The first step in the pipeline is to issue a query against BigQuery to extract data. The results of this query will be used as input for the next step in your pipeline. Considering the need for simplicity and efficiency in implementing this step, what should you do?
Explanation:
The correct answer is D. Kubeflow Pipelines provide pre-built components for various tasks, including querying BigQuery. By using the BigQuery Query Component from the Kubeflow Pipelines repository on GitHub, you can simply load this component into your pipeline, which reduces the amount of custom code you need to write. This approach is more efficient and less error-prone compared to manually running queries in the BigQuery console (Option A) or writing custom Python scripts (Option B and C). Therefore, using the pre-built component simplifies your workflow and ensures that the pipeline can be executed smoothly with minimal manual intervention.