Exam Professional Machine Learning Engineer topic 1 question 202 discussion

C is the right answer because it ensures: Cost-effectiveness: Dataproc is managed and you only pay for the compute time used, which is cost-effective for a POC. A standard cluster is enough for the task. Ease of use: Dataproc simplifies the process of setting up and managing a Spark cluster Minimal effort: a Dataproc cluster + a Vertex AI Workbench instance is a straightforward process through the console or command-line tools, minimizing setup time and effort compared to manually configuring VMs or Kubernetes clusters. *A and B include manual installation steps; D creates a notebook environment but it's not enough to run a PySpark job.

D. Just ran a pilot on Workbench

D. "minimal cost and effort". There's only one answer.

C and D are both valid, as people point out you can technically have Spark preinstalled on D. But this is for a proof of concept for the real design. The concept is not proved by using a notebook, as it's not best practice. Therefore C makes more sense, and is still low effort as it's managed.

C is the answer

I'm following option C. Please take a look the concept of 'Dataproc documentation' (ref: https://cloud.google.com/dataproc/docs) With option D: doesn't provide a solution for managing and scaling the Spark environment, which is necessary for running PySpark workloads.

Vertex AI Workbench notebook: This option provides a pre-configured environment with popular data science libraries like PySpark already installed. It allows you to focus on migrating your PySpark code with minimal changes. n2-standard-4 instance type: This is a general-purpose machine type suitable for various data science tasks. It offers a good balance between cost and performance for initial exploration.

went with D: https://cloud.google.com/vertex-ai/docs/workbench/instances/create-dataproc-enabled

When you want to move your Apache Spark workloads from an on-premises environment to Google Cloud, we recommend using Dataproc to run Apache Spark/Apache Hadoop clusters. https://cloud.google.com/architecture/hadoop/migrating-apache-spark-jobs-to-cloud-dataproc#overview

D Can use Notebook pre-installed libraries and tools, including PySpark.

My bad, I mean is Option D.

I went with C. For Proof Of Concept and requires minimal cost and effort. Furthermore, Vertex AI Workbench notebooks come pre-configured with PySpark.

My answer: C C: This option leverages Google Cloud's Dataproc service, which is designed for running Apache Spark and other big data processing frameworks. By creating a Standard Dataproc cluster, you can easily scale resources as needed for your workload. A. n2-standard-4 VM: This requires manual setup and ongoing maintenance, increasing cost and effort. B. GKE cluster: While offering containerization benefits, it necessitates managing containers and Spark configurations, adding complexity. D. With Vertex AI Workbench, your team can develop, train, and deploy machine learning models using popular frameworks like TensorFlow, PyTorch, and scikit-learn. However, while Vertex AI Workbench supports PySpark, it may not be the optimal choice for migrating existing PySpark workloads, as it's primarily focused on machine learning tasks.

Agree with BlehMaks https://cloud.google.com/architecture/hadoop/migrating-apache-spark-jobs-to-cloud-dataproc#overview Dataproc cluster seems more suitable

https://cloud.google.com/vertex-ai-notebooks?hl=en Data Data Lake and Spark in one place Whether you use TensorFlow, PyTorch, or Spark, you can run any engine from Vertex AI Workbench.  D is correct

https://cloud.google.com/architecture/hadoop/migrating-apache-spark-jobs-to-cloud-dataproc#overview

Minimal setup: Vertex AI Workbench notebooks come pre-configured with PySpark and other data science tools, eliminating the need for manual installation and setup. Cost-effectiveness: Vertex AI Workbench offers managed notebooks with pay-as-you-go pricing, making it a cost-efficient option for proof-of-concept testing. Ease of use: Data scientists can directly run PySpark code in the notebook without managing infrastructure, streamlining the migration process. Scalability: Vertex AI Workbench can easily scale to handle larger workloads or multiple users if the proof-of-concept is successful.