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Question 231 - Professional Machine Learning Engineer discussion

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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?

A.
Create a Vertex Al Workbench user-managed notebook using the default VM instance, and use the %%bigquery magic commands in Jupyter to query the tables.
Answers
A.
Create a Vertex Al Workbench user-managed notebook using the default VM instance, and use the %%bigquery magic commands in Jupyter to query the tables.
B.
Create a Vertex Al Workbench managed notebook to browse and query the tables directly from the JupyterLab interface.
Answers
B.
Create a Vertex Al Workbench managed notebook to browse and query the tables directly from the JupyterLab interface.
C.
Create a Vertex Al Workbench user-managed notebook on a Dataproc Hub. and use the %%bigquery magic commands in Jupyter to query the tables.
Answers
C.
Create a Vertex Al Workbench user-managed notebook on a Dataproc Hub. and use the %%bigquery magic commands in Jupyter to query the tables.
D.
Create a Vertex Al Workbench managed notebook on a Dataproc cluster, and use the spark-bigquery-connector to access the tables.
Answers
D.
Create a Vertex Al Workbench managed notebook on a Dataproc cluster, and use the spark-bigquery-connector to access the tables.
Suggested answer: A

Explanation:

Cost-effectiveness:User-managed notebooks in Vertex AI Workbench allow you to leverage pre-configured virtual machines with reasonable resource allocation, keeping costs lower comparedto 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 withdifferent algorithms.

asked 18/09/2024
Ronald de Groot
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