Free Preparation Discussions
MENU
Google Exam Professional Machine Learning Engineer Topic 4 Question 85 Discussion
Topic #: 4
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?
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
Lasandra
2 months agoMicah
2 months agoMalcom
26 days agoDenae
28 days agoAshlee
1 months agoSherell
1 months agoDeeann
3 months agoBernadine
3 months agoCharlene
1 months agoCandra
2 months agoHolley
2 months agoDelisa
3 months agoVirgie
3 months agoRessie
3 months agoEmerson
3 months agoDorcas
3 months agoChristoper
3 months agoGraciela
4 months agoNoel
3 months agoRashad
3 months agoKimi
3 months agoBettyann
4 months ago