Google Exam Professional-Machine-Learning-Engineer Topic 4 Question 85 Discussion

Actual exam question for Google's Google Professional Machine Learning Engineer exam

Question #: 85
Topic #: 4

[All Google Professional Machine Learning Engineer Questions]

You are developing a recommendation engine for an online clothing store. The historical customer transaction data is stored in BigQuery and Cloud Storage. You need to perform exploratory data analysis (EDA), preprocessing and model training. You plan to rerun these EDA, preprocessing, and training steps as you experiment with different types of algorithms. You want to minimize the cost and development effort of running these steps as you experiment. How should you configure the environment?

ACreate a Vertex Al Workbench user-managed notebook using the default VM instance, and use the %%bigquery magic commands in Jupyter to query the tables.

BCreate a Vertex Al Workbench managed notebook to browse and query the tables directly from the JupyterLab interface.

CCreate a Vertex Al Workbench user-managed notebook on a Dataproc Hub. and use the %%bigquery magic commands in Jupyter to query the tables.

DCreate a Vertex Al Workbench managed notebook on a Dataproc cluster, and use the spark-bigquery-connector to access the tables.

Show Suggested Answer

Suggested Answer: A

Cost-effectiveness:User-managed notebooks in Vertex AI Workbench allow you to leverage pre-configured virtual machines with reasonable resource allocation, keeping costs lower compared to options involving managed notebooks or Dataproc clusters.

Development flexibility:User-managed notebooks offer full control over the environment, allowing you to install additional libraries or dependencies needed for your specific EDA, preprocessing, and model training tasks. This flexibility is crucial while experimenting with different algorithms.

BigQuery integration:The %%bigquery magic commands provide seamless integration with BigQuery within the Jupyter Notebook environment. This enables efficient querying and exploration of customer transaction data stored in BigQuery directly from the notebook, streamlining the workflow.

Other options and why they are not the best fit:

B) Managed notebook:While managed notebooks offer an easier setup, they might have limited customization options, potentially hindering your ability to install specific libraries or tools.

C) Dataproc Hub:Dataproc Hub focuses on running large-scale distributed workloads, and it might be overkill for your scenario involving exploratory analysis and experimentation with different algorithms. Additionally, it could incur higher costs compared to a user-managed notebook.

D) Dataproc cluster with spark-bigquery-connector:Similar to option C, using a Dataproc cluster with the spark-bigquery-connector would be more complex and potentially more expensive than using %%bigquery magic commands within a user-managed notebook for accessing BigQuery data.

https://cloud.google.com/vertex-ai/docs/workbench/instances/bigquery

https://cloud.google.com/vertex-ai-notebooks

by Ronny at Aug 27, 2024, 07:00 PM

Limited Time Offer

25%

1 months ago

I'd go with Option D. Using the spark-bigquery-connector on a Dataproc cluster seems like the most efficient way to handle the large datasets and complex analysis required.

upvoted 0 times

Dorcas

24 days ago

I agree, using the spark-bigquery-connector on a Dataproc cluster seems like the way to go.

upvoted 0 times

...

Christoper

27 days ago

Option D sounds like a good choice. It's efficient for handling large datasets.

upvoted 0 times

...

2 months ago

I think option A is the best choice because it allows us to query the tables using %%bigquery magic commands in Jupyter.

upvoted 0 times

...