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Google Exam Professional Data Engineer Topic 1 Question 84 Discussion

Actual exam question for Google's Professional Data Engineer exam
Question #: 84
Topic #: 1
[All Professional Data Engineer Questions]

You work for a large real estate firm and are preparing 6 TB of home sales data lo be used for machine learning You will use SOL to transform the data and use BigQuery ML lo create a machine learning model. You plan to use the model for predictions against a raw dataset that has not been transformed. How should you set up your workflow in order to prevent skew at prediction time?

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Suggested Answer: A

https://cloud.google.com/bigquery-ml/docs/bigqueryml-transform Using the TRANSFORM clause, you can specify all preprocessing during model creation. The preprocessing is automatically applied during the prediction and evaluation phases of machine learning


by Catina at May 20, 2024, 04:40 AM

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Tonette
10 months ago
Whoa, hold up, this question's got me feeling like a real estate mogul! I'm gonna go with Option B and keep my data transformations consistent. Gotta stay on top of that skew, am I right?
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Teddy
10 months ago
Option A looks like the real estate agent's choice - let BigQuery do all the heavy lifting! But hey, if it works, it works, right?
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Ozell
9 months ago
C) Use a BigQuery to define your preprocessing logic. When creating your model, use the view as your model training data. At prediction time, use BigQuery's ML EVALUATE clause without specifying any transformations on the raw input data.
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Michell
10 months ago
B) When creating your model, use BigQuery's TRANSFORM clause to define preprocessing steps. Before requesting predictions, use a saved query to transform your raw input data, and then use ML. EVALUATE.
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Omega
10 months ago
A) When creating your model, use BigQuery's TRANSFORM clause to define preprocessing steps. At prediction time, use BigQuery's ML. EVALUATE clause without specifying any transformations on the raw input data.
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Tonette
10 months ago
Option D, for sure. Preprocessing everything in Dataflow and then letting BigQuery handle the predictions? That's the kind of workflow that keeps things clean and streamlined.
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Bette
10 months ago
I agree, using Dataflow for preprocessing and BigQuery for predictions seems like a solid workflow.
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Alyssa
10 months ago
Option D sounds like the best approach. Dataflow can handle the preprocessing efficiently.
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Twila
10 months ago
D) Preprocess all data using Dataflow. At prediction time, use BigQuery's ML. EVALUATE clause without specifying any further transformations on the input data.
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Hannah
11 months ago
I'm all about Option C. Using a view for the model training data and then just evaluating the raw input at prediction time? Now that's what I call efficiency.
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Kassandra
10 months ago
Definitely, it simplifies the process and reduces the risk of skew at prediction time.
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Vanna
10 months ago
I agree, it seems like a more efficient workflow. Just evaluate the raw input at prediction time.
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Ozell
10 months ago
Option C sounds like the way to go. Using a view for training data is a smart move.
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Janella
11 months ago
Option B is the way to go, my dude. Gotta make sure that the preprocessing steps are the same for both training and prediction to avoid that pesky skew.
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Derrick
10 months ago
B) Option B sounds solid. Consistency in preprocessing is key to avoiding skew in predictions.
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Paz
10 months ago
A) When creating your model, use BigQuery's TRANSFORM clause to define preprocessing steps Before requesting predictions, use a saved query to transform your raw input data, and then use ML. EVALUATE
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Annalee
10 months ago
B) Option B is definitely the best choice. Consistency in preprocessing steps is key to avoiding skew in predictions.
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Annabelle
10 months ago
Yeah, you're right. Consistency in preprocessing is key to accurate predictions.
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Glynda
10 months ago
A) When creating your model, use BigQuery's TRANSFORM clause to define preprocessing steps Before requesting predictions, use a saved query to transform your raw input data, and then use ML. EVALUATE
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Denae
10 months ago
Option B is the way to go, my dude. Gotta make sure that the preprocessing steps are the same for both training and prediction to avoid that pesky skew.
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