
BigQuery Machine Learning: Develop ML Models Where Your Data Liv
Authored by Jake Catron
Instructional Technology
Professional Development

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5 questions
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1.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
For Classification or Regression problems with decision trees, which of the following models is most relevant?
Linear Regression
XGBoost
Wide and Deep NNs
AutoML Tables
2.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Where labels are not available, for example where customer segmentation is required, which of the following BigQuery supported models is useful?
Recommendation - Matrix Factorization
K-Means Clustering
Time Series Forecasting
Time Series Anomaly Detection
3.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Which of the following are advantages of BigQuery ML when compared to Python based ML frameworks?
Moving and formatting large amounts of data takes longer with Python based models compared to model training in BigQuery
BigQuery ML custom models can be created without the use of multiple tools
BigQuery ML automates multiple steps in the ML workflow
All of the options
4.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What are the 3 key steps for creating a Recommendation System with BigQuery ML?
Prepare training data in BigQuery, train a recommendation system with BigQuery ML, use the predicted recommendations in production
Prepare training data in BigQuery, select a recommendation system from BigQuery ML, deploy and test the model
Import training data to BigQuery, train a recommendation system with BigQuery ML, tune the hyperparameters
Prepare training data in BigQuery, specify the model options in BigQuery ML, export the predictions to Google Analytics
5.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Which of these BigQuery supported classification models is most relevant for predicting binary results, such as True/False?
DNN Classifier (TensorFlow)
Logistic Regression
XGBoost
AutoML Tables
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