Exam Professional Machine Learning Engineer topic 1 question 261 discussion

"Managed notebooks are usually a good choice if you want to use a notebook for data exploration, analysis, modeling, or as part of an end-to-end data science workflow. Managed notebooks instances let you perform workflow-oriented tasks without leaving the JupyterLab interface. They also have many integrations and features for implementing your data science workflow." vs. "User-managed notebooks can be a good choice for users who require extensive customization or who need a lot of control over their environment." Seems more like the former -> B

B is right because this option allows you to minimize cost and development effort by using a managed notebook in Vertex AI Workbench, which integrates well with BigQuery and Cloud Storage. You can browse and query your data directly within the JupyterLab interface without having to create a separate BigQuery client or use the bq command-line tool.

see b1a8fae

agree with guilhermebutzke. Also, this option is easier to reuse in multiple experiments

My Answer: A A: Default VM instance is the best to minimize the cost, and the command %%bigquery magic is the most easy way to get data from BQ. B: Not necessary JupyerLab interface to run code. The %%bigquerv magic commands is sufficient to get data and run easily queries. C: Dataproc Hub seems overkill and it is more expensive than a default VM instance. C: spark-bigquery-connector unnecessary to get tables in the notebook. better use %%bigquery.

https://cloud.google.com/bigquery/docs/visualize-jupyter

https://cloud.google.com/vertex-ai/docs/workbench/notebook-solution#:~:text=For%20users%20who%20have%20specific,user%2Dmanaged%20notebooks%20instance's%20VM.

Option A: User-managed notebooks require VM instance management, adding cost and complexity. %%bigquery magic commands are still needed. Option C: Dataproc Hub adds unnecessary cost and complexity for simple BigQuery interactions. Option D: Spark-bigquery-connector adds complexity and overhead compared to the native BigQuery integration in managed notebooks.