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Exam Professional Machine Learning Engineer All Questions

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Exam Professional Machine Learning Engineer topic 1 question 287 discussion

Actual exam question from Google's Professional Machine Learning Engineer
Question #: 287
Topic #: 1
[All Professional Machine Learning Engineer Questions]

You are tasked with building an MLOps pipeline to retrain tree-based models in production. The pipeline will include components related to data ingestion, data processing, model training, model evaluation, and model deployment. Your organization primarily uses PySpark-based workloads for data preprocessing. You want to minimize infrastructure management effort. How should you set up the pipeline?

  • A. Set up a TensorFlow Extended (TFX) pipeline on Vertex AI Pipelines to orchestrate the MLOps pipeline. Write a custom component for the PySpark-based workloads on Dataproc.
  • B. Set up a Vertex AI Pipelines to orchestrate the MLOps pipeline. Use the predefined Dataproc component for the PySpark-based workloads.
  • C. Set up Kubeflow Pipelines on Google Kubernetes Engine to orchestrate the MLOps pipeline. Write a custom component for the PySparkbased workloads on Dataproc.
  • D. Set up Cloud Composer to orchestrate the MLOps pipeline. Use Dataproc workflow templates for the PySpark-based workloads in Cloud Composer.
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Suggested Answer: B 🗳️
by carolctech at Oct. 26, 2024, 6:47 p.m.

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carolctech
4 weeks ago
Selected Answer: B
B) Best option due to higher ease of use, integration with existing PySpark infrastructure (via Dataproc) and minimal infrastructure management overhead, because: Vertex AI Pipelines is fully managed, minimizing infra management effort and natively integrated with Dataproc for PySpark (while Composer is not); Dataproc’s predefined component for PySpark workload reduces effort and error probability; It is suitable for tree-based models (other options are too, but with more effort)
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