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Question 219 - Professional Machine Learning Engineer discussion

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You work at an ecommerce startup. You need to create a customer churn prediction model Your company's recent sales records are stored in a BigQuery table You want to understand how your initial model is making predictions. You also want to iterate on the model as quickly as possible while minimizing cost How should you build your first model?

A.
Export the data to a Cloud Storage Bucket Load the data into a pandas DataFrame on Vertex Al Workbench and train a logistic regression model with scikit-learn.
Answers
A.
Export the data to a Cloud Storage Bucket Load the data into a pandas DataFrame on Vertex Al Workbench and train a logistic regression model with scikit-learn.
B.
Create a tf.data.Dataset by using the TensorFlow BigQueryChent Implement a deep neural network in TensorFlow.
Answers
B.
Create a tf.data.Dataset by using the TensorFlow BigQueryChent Implement a deep neural network in TensorFlow.
C.
Prepare the data in BigQuery and associate the data with a Vertex Al dataset Create an AutoMLTabuiarTrainmgJob to train a classification model.
Answers
C.
Prepare the data in BigQuery and associate the data with a Vertex Al dataset Create an AutoMLTabuiarTrainmgJob to train a classification model.
D.
Export the data to a Cloud Storage Bucket Create tf. data. Dataset to read the data from Cloud Storage Implement a deep neural network in TensorFlow.
Answers
D.
Export the data to a Cloud Storage Bucket Create tf. data. Dataset to read the data from Cloud Storage Implement a deep neural network in TensorFlow.
Suggested answer: C

Explanation:

BigQuery is a service that allows you to store and query large amounts of data in a scalable and cost-effective way. You can use BigQuery to prepare the data for your customer churn prediction model, such as filtering, aggregating, and transforming the data. You can then associate the data with a Vertex AI dataset, which is a service that allows you to store and manage your ML data on Google Cloud. By using a Vertex AI dataset, you can easily access the data from other Vertex AI services, such as AutoML. AutoML is a service that allows you to create and train ML models without writing code. You can use AutoML to create an AutoMLTabularTrainingJob, which is a type of job that trains a classification model for tabular data, such as customer churn. By using an AutoMLTabularTrainingJob, you can benefit from the automated feature engineering, model selection, and hyperparameter tuning that AutoML provides. You can also use Vertex Explainable AI to understand how your model is making predictions, such as which features are most important and how they affect the prediction outcome. By using BigQuery, Vertex AI dataset, and AutoMLTabularTrainingJob, you can build your first model as quickly as possible while minimizing cost and complexity.Reference:

BigQuery documentation

Vertex AI dataset documentation

AutoMLTabularTrainingJob documentation

Vertex Explainable AI documentation

Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate

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asked 18/09/2024
Kinshuk Choubisa
44 questions
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