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- Question 66
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Question 66/109
You lead a data science team at a large international corporation. Most of the models your team trains are large-scale models using high-level TensorFlow APIs on AI Platform with GPUs. Your team usually takes a few weeks or months to iterate on a new version of a model. You were recently asked to review your team's spending. How should you reduce your Google Cloud compute costs without impacting the model's performance?
Correct Answer: C
* Option A is incorrect because using AI Platform to run distributed training jobs with checkpoints does not reduce the compute costs, but rather increases them by using more resources and storing the checkpoints.
* Option B is incorrect because using AI Platform to run distributed training jobs without checkpoints may reduce the compute costs, but it also risks losing the progress of the training if the job fails or is interrupted.
* Option C is correct because migrating to training with Kubeflow on Google Kubernetes Engine, and using preemptible VMs with checkpoints can reduce the compute costs significantly by using cheaper and more scalable resources, while also preserving the state of the training with checkpoints.
* Option D is incorrect because using preemptible VMs without checkpoints may reduce the compute costs, but it also risks losing the training progress if the VMs are preempted.
References:
* Kubeflow on Google Cloud
* Using preemptible VMs and GPUs
* Saving and loading models
* Option B is incorrect because using AI Platform to run distributed training jobs without checkpoints may reduce the compute costs, but it also risks losing the progress of the training if the job fails or is interrupted.
* Option C is correct because migrating to training with Kubeflow on Google Kubernetes Engine, and using preemptible VMs with checkpoints can reduce the compute costs significantly by using cheaper and more scalable resources, while also preserving the state of the training with checkpoints.
* Option D is incorrect because using preemptible VMs without checkpoints may reduce the compute costs, but it also risks losing the training progress if the VMs are preempted.
References:
* Kubeflow on Google Cloud
* Using preemptible VMs and GPUs
* Saving and loading models
- Question List (109q)
- Question 1: You are an ML engineer responsible for designing and impleme...
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- Question 17: You are training and deploying updated versions of a regress...
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- Question 26: You have deployed multiple versions of an image classificati...
- Question 27: You have recently trained a scikit-learn model that you plan...
- Question 28: You are developing a custom image classification model in Py...
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- Question 30: You have recently developed a custom model for image classif...
- Question 31: You are profiling the performance of your TensorFlow model t...
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- Question 52: You are developing an ML model in a Vertex Al Workbench note...
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- Question 54: You recently trained an XGBoost model on tabular data You pl...
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- Question 62: You have built a custom model that performs several memory-i...
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- Question 65: You work for a company that is developing a new video stream...
- Question 66: You lead a data science team at a large international corpor...
- Question 67: You work for an auto insurance company. You are preparing a ...
- Question 68: You developed an ML model with Al Platform, and you want to ...
- Question 69: You work for a pharmaceutical company based in Canada. Your ...
- Question 70: You have trained a DNN regressor with TensorFlow to predict ...
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- Question 75: You work for a retail company. You have created a Vertex Al ...
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- Question 81: You are developing ML models with Al Platform for image segm...
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- Question 94: You are an ML engineer at a global shoe store. You manage th...
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- Question 98: Your organization manages an online message board A few mont...
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- Question 102: You recently developed a wide and deep model in TensorFlow. ...
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- Question 109: You built a custom ML model using scikit-learn. Training tim...
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