Topic 1 Question 201
You developed a Vertex AI pipeline that trains a classification model on data stored in a large BigQuery table. The pipeline has four steps, where each step is created by a Python function that uses the KubeFlow v2 API. The components have the following names:
You launch your Vertex AI pipeline as the following:
You perform many model iterations by adjusting the code and parameters of the training step. You observe high costs associated with the development, particularly the data export and preprocessing steps. You need to reduce model development costs. What should you do?
Change the components’ YAML filenames to export.yaml, preprocess,yaml, f "train- {dt}.yaml", f"calibrate-{dt).vaml".
Add the {"kubeflow.v1.caching": True} parameter to the set of params provided to your PipelineJob.
Move the first step of your pipeline to a separate step, and provide a cached path to Cloud Storage as an input to the main pipeline.
Change the name of the pipeline to f"my-awesome-pipeline-{dt}".
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- 正解だと思う選択肢: B
Enables caching: Setting this parameter instructs Vertex AI Pipelines to cache the outputs of pipeline steps that have successfully completed. This means that if a step's inputs haven't changed, its execution can be skipped, reusing the cached output instead. Targets costly steps: The prompt highlights that data export and preprocessing steps are particularly expensive. Caching these steps can significantly reduce costs during model iterations.
👍 1pikachu0072024/01/12
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