Q: 10
Your organization wants to predict the behavior of visitors to its public website. To do that, you have
decided to build a machine learning model. Your team has database-related skills but only basic
machine learning skills, and would like to use those database skills.
Which Google Cloud product or feature should your organization choose?
Options
Discussion
Option A Saw a similar question in practice sets, pretty sure BigQuery ML lets you run ML models using SQL so database folks can stick to what they know.
Feels like D since Cloud SQL matches their database experience and keeps everything familiar. Still, not totally sure if it covers ML part.
D imo. Since their team already knows databases, Cloud SQL seems like the easiest path. I think you could leverage familiar SQL tools that way, unless the question is expecting more actual ML features.
C or D for me. Since the team only has basic ML skills, I was thinking Cloud SQL (D) might be the right fit because it's database-oriented and familiar to them. But reading closely, just having a database doesn't help with ML modeling directly, so maybe C (TensorFlow) is better if they want to actually build models. Not totally confident though-anyone see it differently?
Its D, since Cloud SQL uses standard SQL for queries and should fit a database team's skills.
Don't think it's C. While TensorFlow is powerful for ML, it's not really aimed at folks with just SQL skills. D seems tempting because of the database angle, but it doesn't help you build ML models directly.
Feels like A. Database teams can use SQL with BigQuery ML for building models, so fits better than Cloud SQL or the others.
I don't think it's D. Cloud SQL is just a managed database, doesn't actually let you build ML models within SQL itself. BigQuery ML seems like a trap here, but I get why folks mix them up since both involve SQL skills. Anyone else see it different?
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