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Google Exam Professional Machine Learning Engineer Topic 4 Question 85 Discussion

Actual exam question for Google's Professional Machine Learning Engineer exam
Question #: 85
Topic #: 4
[All 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?

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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

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Lasandra
2 months ago
I dunno, man, all these options sound like a lot of work. Can't we just have a button that says 'Make me a recommendation engine' and it just does it all for us? Where's the AI in all this?
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Micah
2 months ago
Option D, hands down. Anything that involves Dataproc is bound to be a pain in the neck. I'll take the managed notebook and spark-bigquery-connector any day!
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Malcom
8 days ago
I agree with option D. Using the spark-bigquery-connector on a Dataproc cluster seems like a solid choice for this scenario.
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Denae
10 days ago
I think option B is the way to go. It's convenient to browse and query the tables directly from the JupyterLab interface.
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Ashlee
17 days ago
I prefer option A. It's simpler to just use the default VM instance and the %%bigquery magic commands in Jupyter.
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Sherell
21 days ago
Option D, hands down. Anything that involves Dataproc is bound to be a pain in the neck. I'll take the managed notebook and spark-bigquery-connector any day!
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Deeann
2 months ago
Hmm, I'm not sure any of these options are truly optimal. If I had to choose, I'd probably go with B, but I can't help but feel like there's a more elegant solution out there that would really streamline the whole process.
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Bernadine
2 months ago
Wow, these options are all over the place! I'm torn between A and C, but I think I'd lean towards C to get the benefits of Dataproc without the added complexity of managing a separate Dataproc cluster.
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Charlene
25 days ago
I agree, C seems like a good balance between functionality and simplicity.
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Candra
1 months ago
C sounds like a good option to leverage Dataproc without the extra hassle of managing a separate cluster.
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Holley
1 months ago
I think A could work well for quick querying with the %%bigquery magic commands.
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Delisa
2 months ago
I think option C is the way to go, as it provides a user-managed notebook on a Dataproc Hub for querying the tables.
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Virgie
2 months ago
I prefer option B because it allows us to browse and query the tables directly from the JupyterLab interface.
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Ressie
2 months ago
I disagree, I believe option D is more efficient as it utilizes the spark-bigquery-connector to access the tables.
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Emerson
3 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.
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Dorcas
2 months ago
I agree, using the spark-bigquery-connector on a Dataproc cluster seems like the way to go.
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Christoper
2 months ago
Option D sounds like a good choice. It's efficient for handling large datasets.
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Graciela
3 months ago
Option B makes the most sense, as it allows me to directly access the tables from the JupyterLab interface, which should minimize the setup and configuration overhead.
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Noel
2 months ago
That sounds like a good choice for minimizing the cost and development effort while experimenting with different algorithms.
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Rashad
2 months ago
I agree, using a Vertex AI Workbench managed notebook for browsing and querying tables in JupyterLab is a convenient option.
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Kimi
3 months ago
Option B makes the most sense, as it allows me to directly access the tables from the JupyterLab interface, which should minimize the setup and configuration overhead.
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Bettyann
3 months ago
I think option A is the best choice because it allows us to query the tables using %%bigquery magic commands in Jupyter.
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