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Exam Professional Machine Learning Engineer topic 1 question 97 discussion

Actual exam question from Google's Professional Machine Learning Engineer
Question #: 97
Topic #: 1
[All Professional Machine Learning Engineer Questions]

You are a data scientist at an industrial equipment manufacturing company. You are developing a regression model to estimate the power consumption in the company’s manufacturing plants based on sensor data collected from all of the plants. The sensors collect tens of millions of records every day. You need to schedule daily training runs for your model that use all the data collected up to the current date. You want your model to scale smoothly and require minimal development work. What should you do?

  • A. Train a regression model using AutoML Tables.
  • B. Develop a custom TensorFlow regression model, and optimize it using Vertex AI Training.
  • C. Develop a custom scikit-learn regression model, and optimize it using Vertex AI Training.
  • D. Develop a regression model using BigQuery ML.
Show Suggested Answer Hide Answer
Suggested Answer: D 🗳️
by mil_spyro at Dec. 17, 2022, 11:12 a.m.

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niketd
Highly Voted 2 years ago
Selected Answer: D
The key is to understand the amount of data that needs to be used for training - the sensor collects tens of millions of records every day and the model needs to use all the data up to the current date. There is a limitation for AutoML is 100M rows -> https://cloud.google.com/vertex-ai/docs/tabular-data/classification-regression/prepare-data
upvoted 14 times
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Laur_C
Most Recent 4 months, 2 weeks ago
Selected Answer: A
Old question - quota for AutoML (now Vertex AI) is Between 1,000 and 200,000,000 rows so should be able to handle well. Plus "minimal development work" is usually a key word for AutoML (Vertex Ai)
upvoted 2 times
Laur_C
4 months, 2 weeks ago
AutoML modal limits: https://cloud.google.com/vertex-ai/docs/quotas#tabular_1
upvoted 1 times
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pinimichele01
1 year ago
Selected Answer: D
There is a limitation for AutoML is 100M rows -> https://cloud.google.com/vertex-ai/docs/tabular-data/classification-regression/prepare-data
upvoted 1 times
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vale_76_na_xxx
1 year, 4 months ago
I go for A
upvoted 2 times
pinimichele01
1 year ago
There is a limitation for AutoML is 100M rows -> https://cloud.google.com/vertex-ai/docs/tabular-data/classification-regression/prepare-data
upvoted 1 times
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Mickey321
1 year, 5 months ago
Selected Answer: A
Either A or D . Since not stated where is sensor data stored . hence go for A
upvoted 2 times
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PST21
1 year, 10 months ago
Ans D. BigQuery ML allows you to schedule daily training runs by incorporating the latest data collected up to the current date. By specifying the appropriate SQL query, you can include all the relevant data in the training process, ensuring that your model is updated regularly.
upvoted 2 times
maukaba
1 year, 5 months ago
it says "use all the data collected up to the current date" not a just a selection of "relevant" (?!) data
upvoted 1 times
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ggwp1999
1 year, 11 months ago
Selected Answer: A
I would go with A because it states that it requires minimal development work. Not sure tho, correct me if I’m wrong
upvoted 3 times
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M25
1 year, 11 months ago
Selected Answer: D
Went with D
upvoted 1 times
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JamesDoe
2 years, 1 month ago
Selected Answer: A
Old question, the quotas were removed when they moved AutoML into VertexAI. https://cloud.google.com/vertex-ai/docs/quotas#model_quotas#tabular
upvoted 3 times
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Yajnas_arpohc
2 years, 1 month ago
Would go w A given the specifics mentioned in question. BigQuery is an unnecessary distraction IMO (e.g. why would we assume BigQuery and not BigTable!)
upvoted 1 times
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TNT87
2 years, 1 month ago
Selected Answer: D
Answer D https://cloud.google.com/blog/products/data-analytics/automl-tables-now-generally-available-bigquery-ml This legacy version of AutoML Tables is deprecated and will no longer be available on Google Cloud after January 23, 2024. All the functionality of legacy AutoML Tables and new features are available on the Vertex AI platform. See Migrate to Vertex AI to learn how to migrate your resources.
upvoted 2 times
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FherRO
2 years, 2 months ago
Selected Answer: A
You require minimal development work and the question doesn't mention if your data is stored in BQ
upvoted 1 times
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Ade_jr
2 years, 3 months ago
Selected Answer: D
Answer is D, AutoML has 200M rows as limits
upvoted 3 times
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ares81
2 years, 3 months ago
Selected Answer: A
A and D seem both good, but A works better, for me.
upvoted 1 times
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mymy9418
2 years, 4 months ago
Selected Answer: A
But BQML also has limits on training data https://cloud.google.com/bigquery-ml/quotas
upvoted 2 times
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hiromi
2 years, 4 months ago
Selected Answer: D
Vote for D A dosen't work because AutoML has limits on training data - https://www.examtopics.com/exams/google/professional-machine-learning-engineer/view/10/
upvoted 3 times
behzadsw
2 years, 3 months ago
Wrong. The limit is 200 M records. We have 10M records. see: https://cloud.google.com/automl-tables/docs/quotas
upvoted 1 times
adarifian
2 years, 1 month ago
it's more than 10M. the training needs to use all the data collected up to the current date
upvoted 2 times
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mil_spyro
2 years, 4 months ago
Selected Answer: D
BigQuery ML can scale smoothly and requires minimal development work. Model can be build using SQL queries rather than writing custom code.
upvoted 3 times
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