Google Professional Machine Learning Engineer Question 169
Select 3Google Cloud PlatformYou are tasked with training a machine learning model using TensorFlow in Vertex AI Workbench. The dataset is stored in BigQuery, and you need to preprocess the data, train the model, and save the trained model to Google Cloud Storage for deployment. Which steps should you take to accomplish this task?
- A
Use BigQuery client libraries in Vertex AI Workbench to query and load the dataset.
- B
Directly train the model on the dataset stored in BigQuery without any preprocessing.
- C
Write a preprocessing pipeline using TensorFlow or Pandas to clean and prepare the data after loading it into the Workbench.
- D
Train the TensorFlow model in Vertex AI Workbench and save the trained model locally on the Workbench environment.
- E
Save the trained TensorFlow model to a Cloud Storage bucket for deployment.
Show answer and explanation
Correct answers: A, C, E
Explanation
To train and deploy a model using Vertex AI Workbench, it is essential to first load the data from BigQuery using the appropriate libraries, preprocess it for training, and save the trained model to a persistent storage solution like Cloud Storage for deployment. Skipping preprocessing or saving the model locally would hinder effective deployment and scalability.
- A. Correct.
Correct: Using BigQuery client libraries is a best practice for querying and loading data into Vertex AI Workbench.
- B. Incorrect.
Incorrect: Training the model directly on raw data without preprocessing can lead to poor performance or errors, especially if the data requires cleaning or transformation.
- C. Correct.
Correct: Preprocessing the data with TensorFlow or Pandas ensures the data is in the right format for training and helps improve model performance.
- D. Incorrect.
Incorrect: Saving the trained model locally on the Workbench environment is not recommended for deployment as it is not persistent or accessible for production use.
- E. Correct.
Correct: Saving the trained model to a Cloud Storage bucket is the proper way to persist the model and make it accessible for deployment or further use.