Page Reader AI | Exam Professional Machine Learning Engineer topic 1 question 278 discussion

Exam Professional Machine Learning Engineer topic 1 question 278 discussion - ExamTopics

AI Summary Hide AI Generated Summary

Problem

The question presents a scenario requiring the training of an XGBoost model on a small dataset with custom dependencies, aiming to minimize the training job's startup time using Vertex AI.

Options

A: Store data in Cloud Storage; create a custom container with the training application, reading data from Cloud Storage.
B: Use the XGBoost prebuilt container; create a Python distribution with data, installing dependencies at runtime.
C: Create a custom container including the data; load data into a pandas DataFrame.
D: Store data in Cloud Storage; use the XGBoost prebuilt container; create a Python distribution installing runtime dependencies; read data from Cloud Storage.

Solution

The suggested answer is A. This approach prioritizes efficient data handling by separating data storage (Cloud Storage) from the training application within a custom container. This is considered optimal for minimizing startup time.

You need to train an XGBoost model on a small dataset. Your training code requires custom dependencies. You want to minimize the startup time of your training job. How should you set up your Vertex AI custom training job?

A. Store the data in a Cloud Storage bucket, and create a custom container with your training application. In your training application, read the data from Cloud Storage and train the model.
B. Use the XGBoost prebuilt custom container. Create a Python source distribution that includes the data and installs the dependencies at runtime. In your training application, load the data into a pandas DataFrame and train the model.
C. Create a custom container that includes the data. In your training application, load the data into a pandas DataFrame and train the model.
D. Store the data in a Cloud Storage bucket, and use the XGBoost prebuilt custom container to run your training application. Create a Python source distribution that installs the dependencies at runtime. In your training application, read the data from Cloud Storage and train the model.

Show Suggested Answer Hide Answer

Was this article displayed correctly? Not happy with what you see?

See Archived Versions