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Exam Cloud Digital Leader topic 1 question 236 discussion

Actual exam question from Google's Cloud Digital Leader
Question #: 236
Topic #: 1
[All Cloud Digital Leader Questions]

An organization has hired a team of data scientists and developers. They want to create unique value in their business by coding an advanced machine learning model in Vertex AI Workbench. Which service should the organization use to train the model?

  • A. Custom training
  • B. AutoML
  • C. Compute Engine
  • D. Prebuilt APIs
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Suggested Answer: A 🗳️

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joshnort
3 months, 2 weeks ago
Selected Answer: A
A. Custom training Since the organization has a team of data scientists and developers and wants to create an advanced machine learning model, Custom training is the best option. This service in Vertex AI Workbench allows the team to train their own machine learning models using custom code and frameworks. It provides flexibility and control over the training process, making it suitable for advanced and unique use cases.
upvoted 2 times
joshnort
3 months, 2 weeks ago
Here’s why the other options are less suitable: B. AutoML: This service is great for users who may not have extensive machine learning expertise, as it automates model training. However, for a team of experienced data scientists and developers working on advanced models, AutoML might not provide the level of customization needed. C. Compute Engine: While Compute Engine provides virtual machines that can be used for various workloads, it is not a specialized service for training machine learning models. It can be used in conjunction with other services but does not provide the tools specifically for training models in Vertex AI. D. Prebuilt APIs: Prebuilt APIs are designed for using existing machine learning models rather than creating new, custom models. These would not allow the team to code and train their own advanced model.
upvoted 1 times
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Vivek007
6 months ago
A: Custom training allows organizations to train machine learning models using their own custom data and algorithms. This approach is suitable for cases where organizations have specific requirements, unique datasets, or specialized machine learning tasks that cannot be addressed by pre-built solutions. With custom training, organizations have full control over the model architecture, hyperparameters, and training process, enabling them to create highly tailored and optimized machine learning models to address their specific business needs.
upvoted 4 times
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