Question 200 of 280
Choose 3You 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?
✓AChange the components’ YAML filenames to export.yaml, preprocess,yaml, f "train-
{dt}.yaml", f"calibrate-{dt).vaml".
BAdd the {"kubeflow.v1.caching": True} parameter to the set of params provided to your PipelineJob.
CMove 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.
DChange the name of the pipeline to f"my-awesome-pipeline-{dt}".
ACreate a n2-standard-4 VM instance and install Java, Scala, and Apache Spark dependencies on it.
BCreate a Google Kubernetes Engine cluster with a basic node pool configuration, install Java, Scala, and Apache Spark dependencies on it.
✓CCreate a Standard (1 master, 3 workers) Dataproc cluster, and run a Vertex AI Workbench notebook instance on it.
DCreate a Vertex AI Workbench notebook with instance type n2-standard-4.
AVertex ML Metadata, Vertex AI Feature Store, and Vertex AI Vizier
BVertex AI Pipelines, Vertex AI Experiments, and Vertex AI Vizier
✓CVertex ML Metadata, Vertex AI Experiments, and Vertex AI TensorBoard
DVertex AI Pipelines, Vertex AI Feature Store, and Vertex AI TensorBoard
✓
Correct Answer: A, C, C
Change the components’ YAML filenames to export.yaml, preprocess,yaml, f "train- {dt}.yaml", f"calibrate-{dt).vaml".
▥
Explanation
The correct answer is highlighted above. Review the wording carefully, then use the next question to continue building your understanding of Google certification topics.