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Exam Professional Machine Learning Engineer topic 1 question 33 discussion

Actual exam question from Google's Professional Machine Learning Engineer
Question #: 33
Topic #: 1
[All Professional Machine Learning Engineer Questions]

You have a demand forecasting pipeline in production that uses Dataflow to preprocess raw data prior to model training and prediction. During preprocessing, you employ Z-score normalization on data stored in BigQuery and write it back to BigQuery. New training data is added every week. You want to make the process more efficient by minimizing computation time and manual intervention. What should you do?

  • A. Normalize the data using Google Kubernetes Engine.
  • B. Translate the normalization algorithm into SQL for use with BigQuery.
  • C. Use the normalizer_fn argument in TensorFlow's Feature Column API.
  • D. Normalize the data with Apache Spark using the Dataproc connector for BigQuery.
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Suggested Answer: B 🗳️
by maartenalexander at June 22, 2021, 12:27 p.m.

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maartenalexander
Highly Voted 3 years, 3 months ago
B. I think. BiqQuery definitely minimizes computational time for normalization. I think it would also minimize manual intervention. For data normalization in dataflow you'd have to pass in values of mean and standard deviation as a side-input. That seems more work than a simple SQL query
upvoted 22 times
93alejandrosanchez
2 years, 12 months ago
I agree that B would definitely get the job done. But wouldn't D work as well and keep all the data pre-processing in Dataflow?
upvoted 2 times
kaike_reis
2 years, 11 months ago
Dataflow uses Beam, different from dataproc that uses Spark. I think that D would be wrong because we would add one more service into the pipeline for a simple transformation (minus the mean and divide by std).
upvoted 4 times
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PhilipKoku
Most Recent 4 months, 1 week ago
Selected Answer: B
B) Using BigQuery
upvoted 1 times
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Sum_Sum
11 months, 1 week ago
Selected Answer: B
z-scores is very easy to do in BQ - no need for more complex solutions
upvoted 2 times
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elenamatay
1 year, 1 month ago
B. All that maartenalexander said, + BigQuery already has a function for that: https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-standard-scaler , we could even schedule the query for calculating this automatically :)
upvoted 3 times
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aaggii
1 year, 3 months ago
Selected Answer: C
Every week when new data is loaded mean and standard deviation is calculated for it and passed as parameter to calculate z score at serving https://towardsdatascience.com/how-to-normalize-features-in-tensorflow-5b7b0e3a4177
upvoted 1 times
tavva_prudhvi
1 year, 2 months ago
owever, in the given scenario, you are using Dataflow for preprocessing and BigQuery for storing data. To make the process more efficient by minimizing computation time and manual intervention, you should still opt for option B: Translate the normalization algorithm into SQL for use with BigQuery. This way, you can perform the normalization directly in BigQuery, which will save time and resources compared to using an external tool.
upvoted 1 times
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SamuelTsch
1 year, 3 months ago
Selected Answer: B
A, D usually need additional configuration, which could cost much more time.
upvoted 1 times
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M25
1 year, 5 months ago
Selected Answer: B
Went with B
upvoted 2 times
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SergioRubiano
1 year, 6 months ago
Selected Answer: B
Best way is B
upvoted 2 times
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Fatiy
1 year, 7 months ago
Selected Answer: D
Option D is the best solution because Apache Spark provides a distributed computing platform that can handle large-scale data processing with ease. By using the Dataproc connector for BigQuery, Spark can read data directly from BigQuery and perform the normalization process in a distributed manner. This can significantly reduce computation time and manual intervention. Option A is not a good solution because Kubernetes is a container orchestration platform that does not directly provide data normalization capabilities. Option B is not a good solution because Z-score normalization is a data transformation technique that cannot be easily translated into SQL. Option C is not a good solution because the normalizer_fn argument in TensorFlow's Feature Column API is only applicable for feature normalization during model training, not for data preprocessing.
upvoted 2 times
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ares81
1 year, 9 months ago
Selected Answer: B
Best way to proceed is B.
upvoted 2 times
Fatiy
1 year, 7 months ago
SQL is not as flexible as other programming languages like Python, which can limit the ability to customize the normalization process or incorporate new features in the future.
upvoted 1 times
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Mohamed_Mossad
2 years, 4 months ago
Selected Answer: B
B is the most efficient as you will not load --> process --> save , no you will only write some sql in bigquery and voila :D
upvoted 4 times
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baimus
2 years, 7 months ago
It's B, bigquery can do this internally, no need for dataflow
upvoted 2 times
Fatiy
1 year, 7 months ago
SQL is not as flexible as other programming languages like Python, which can limit the ability to customize the normalization process or incorporate new features in the future.
upvoted 1 times
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xiaoF
2 years, 8 months ago
Selected Answer: B
I agree with B.
upvoted 2 times
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alashin
3 years, 3 months ago
B. I agree with B as well.
upvoted 3 times
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