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Question 220 - Professional Machine Learning Engineer discussion

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You are developing a training pipeline for a new XGBoost classification model based on tabular data The data is stored in a BigQuery table You need to complete the following steps

1. Randomly split the data into training and evaluation datasets in a 65/35 ratio

2. Conduct feature engineering

3 Obtain metrics for the evaluation dataset.

4 Compare models trained in different pipeline executions

How should you execute these steps'?

A.
1 Using Vertex Al Pipelines, add a component to divide the data into training and evaluation sets, and add another component for feature engineering 2. Enable auto logging of metrics in the training component. 3 Compare pipeline runs in Vertex Al Experiments
Answers
A.
1 Using Vertex Al Pipelines, add a component to divide the data into training and evaluation sets, and add another component for feature engineering 2. Enable auto logging of metrics in the training component. 3 Compare pipeline runs in Vertex Al Experiments
B.
1 Using Vertex Al Pipelines, add a component to divide the data into training and evaluation sets, and add another component for feature engineering 2 Enable autologging of metrics in the training component 3 Compare models using the artifacts lineage in Vertex ML Metadata
Answers
B.
1 Using Vertex Al Pipelines, add a component to divide the data into training and evaluation sets, and add another component for feature engineering 2 Enable autologging of metrics in the training component 3 Compare models using the artifacts lineage in Vertex ML Metadata
C.
1 In BigQuery ML. use the create model statement with bocstzd_tree_classifier as the model type and use BigQuery to handle the data splits. 2 Use a SQL view to apply feature engineering and train the model using the data in that view 3. Compare the evaluation metrics of the models by using a SQL query with the ml. training_infc statement.
Answers
C.
1 In BigQuery ML. use the create model statement with bocstzd_tree_classifier as the model type and use BigQuery to handle the data splits. 2 Use a SQL view to apply feature engineering and train the model using the data in that view 3. Compare the evaluation metrics of the models by using a SQL query with the ml. training_infc statement.
D.
1 In BigQuery ML use the create model statement with boosted_tree_classifier as the model type, and use BigQuery to handle the data splits. 2 Use ml transform to specify the feature engineering transformations, and train the model using the data in the table ' 3. Compare the evaluation metrics of the models by using a SQL query with the ml. training_info statement.
Answers
D.
1 In BigQuery ML use the create model statement with boosted_tree_classifier as the model type, and use BigQuery to handle the data splits. 2 Use ml transform to specify the feature engineering transformations, and train the model using the data in the table ' 3. Compare the evaluation metrics of the models by using a SQL query with the ml. training_info statement.
Suggested answer: B

Explanation:

Vertex AI Pipelines is a service that allows you to create and run scalable and portable ML pipelines on Google Cloud. You can use Vertex AI Pipelines to add a component to divide the data into training and evaluation sets, and add another component for feature engineering. A component is a self-contained piece of code that performs a specific task in the pipeline. You can use the built-in components provided by Vertex AI Pipelines, or create your own custom components. By using Vertex AI Pipelines, you can orchestrate and automate your ML workflow, and track the provenance and lineage of your data and models. You can also enable autologging of metrics in the training component, which is a feature that automatically logs the metrics from your XGBoost model to Vertex AI Experiments. Vertex AI Experiments is a service that allows you to track, compare, and optimize your ML experiments on Google Cloud. You can use Vertex AI Experiments to monitor the training progress, visualize the metrics, and analyze the results of your model. You can also compare models using the artifacts lineage in Vertex ML Metadata. Vertex ML Metadata is a service that stores and manages the metadata of your ML artifacts, such as datasets, models, metrics, and executions. You can use Vertex ML Metadata to view the artifacts lineage, which is a graph that shows the relationships and dependencies among the artifacts. By using the artifacts lineage, you can compare the performance and quality of different models trained in different pipeline executions, and identify the best model for your use case. By using Vertex AI Pipelines, Vertex AI Experiments, and Vertex ML Metadata, you can execute the steps required for developing a training pipeline for a new XGBoost classification model based on tabular data stored in a BigQuery table.Reference:

Vertex AI Pipelines documentation

Vertex AI Experiments documentation

Vertex ML Metadata documentation

Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate

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asked 18/09/2024
Bruno Colussi
25 questions
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