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Exam Professional Machine Learning Engineer All Questions

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Exam Professional Machine Learning Engineer topic 1 question 87 discussion

Actual exam question from Google's Professional Machine Learning Engineer
Question #: 87
Topic #: 1
[All Professional Machine Learning Engineer Questions]

You have been given a dataset with sales predictions based on your company’s marketing activities. The data is structured and stored in BigQuery, and has been carefully managed by a team of data analysts. You need to prepare a report providing insights into the predictive capabilities of the data. You were asked to run several ML models with different levels of sophistication, including simple models and multilayered neural networks. You only have a few hours to gather the results of your experiments. Which Google Cloud tools should you use to complete this task in the most efficient and self-serviced way?

  • A. Use BigQuery ML to run several regression models, and analyze their performance.
  • B. Read the data from BigQuery using Dataproc, and run several models using SparkML.
  • C. Use Vertex AI Workbench user-managed notebooks with scikit-learn code for a variety of ML algorithms and performance metrics.
  • D. Train a custom TensorFlow model with Vertex AI, reading the data from BigQuery featuring a variety of ML algorithms.
Show Suggested Answer Hide Answer
Suggested Answer: A 🗳️
by LearnSodas at Dec. 11, 2022, 8:07 p.m.

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Werner123
Highly Voted 8 months, 1 week ago
Selected Answer: A
You only have a few hours. The dataset is in BQ. The dataset is carefully managed. BQML it is.
upvoted 5 times
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bc3f222
Most Recent 2 months, 2 weeks ago
Selected Answer: A
not enough time, data already in big query therefore BQML
upvoted 1 times
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ludovikush
8 months, 2 weeks ago
Selected Answer: C
I agree with pico answer
upvoted 1 times
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iieva
9 months, 3 weeks ago
Selected Answer: A
All deep neural networks are multilayered neural networks, but not all multilayered neural networks are necessarily deep. The term "deep" is used to emphasize the depth of the network in the context of having many hidden layers, which has been shown to be effective for learning hierarchical representations of complex patterns in data. Hence BQ allows creation of DNNs (https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-create-dnn-models) it should be A.
upvoted 4 times
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pico
1 year, 1 month ago
Selected Answer: C
Vertex AI Workbench provides user-managed notebooks that allow you to run Python code using libraries like scikit-learn, TensorFlow, and more. You can easily connect to your BigQuery dataset from within the notebook, extract the data, and perform data preprocessing. You can then experiment with different ML algorithms available in scikit-learn and track performance metrics. It provides flexibility, control, and the ability to run various models quickly.
upvoted 3 times
pico
1 year, 1 month ago
Not A. BigQuery ML is convenient for quick model training and predictions within BigQuery itself, but it has limitations in terms of the variety of ML algorithms and customization options it offers. It may not be the best choice for running more sophisticated ML models or extensive experiments. and It only said regression model
upvoted 2 times
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MTTTT
1 year, 3 months ago
Selected Answer: C
I think multilayered neural networks need to be trained externally from BQ ML as stated here: https://cloud.google.com/bigquery/docs/bqml-introduction
upvoted 1 times
MTTTT
1 year, 3 months ago
nvm you can import DNN in BQ
upvoted 1 times
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SamuelTsch
1 year, 4 months ago
Selected Answer: A
According to the question, you don't have enough time. B, C, D need much more time to set up the service, or write the code. Also the data is already in BigQuery. BQML should be the fastest way. Besides, BQML supports xgboost, NN models as well.
upvoted 2 times
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Jarek7
1 year, 4 months ago
Selected Answer: C
The question says that "You were asked to run several ML models with different levels of sophistication, including simple models and multilayered neural networks" BQ ML doesn't allow this. BQ ML provides only simple regression/categorization models. It is not about training these "sophisticated models" but only run them, so you can easly do it within few hours with notebooks.
upvoted 2 times
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M25
1 year, 6 months ago
Selected Answer: A
Went with A
upvoted 2 times
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lucaluca1982
1 year, 6 months ago
Selected Answer: C
C allows to execute more complex tests
upvoted 1 times
tavva_prudhvi
1 year, 3 months ago
However, given the limited time constraint of a few hours and the fact that the data is already stored in BigQuery, option A is more efficient. BigQuery ML allows you to quickly create and evaluate ML models directly within BigQuery, without the need to move the data or set up a separate environment. This makes it faster and more convenient for running several regression models and analyzing their performance within the given time frame.
upvoted 1 times
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FherRO
1 year, 8 months ago
Selected Answer: A
B,C,D requires coding. You only have some hours, A is the fastest.
upvoted 2 times
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hiromi
1 year, 10 months ago
Selected Answer: A
I vote for A
upvoted 3 times
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ares81
1 year, 10 months ago
Selected Answer: A
It's A.
upvoted 2 times
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LearnSodas
1 year, 11 months ago
Selected Answer: A
I will go with A, since it's the fastest way to do it. Custom training in Vertex AI requires time and writing scikit-learn models in notebooks too
upvoted 2 times
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C (25%)
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