Practice Test 1 | Google Cloud Certified Professional Data Engineer | Dumps | Mock Test
Data scientists are testing a TensorFlow model on Google Cloud using four NVIDIA Tesla P100 GPUs to test a TensorFlow model. After experimenting with several use cases, they decide to scale up by using a different machine type for testing. As a data engineer, you are responsible of assisting with choosing the right machine type to reach a better model performance. What should you do?
A. Use TPU machine type for testing the TensorFlow on.
B. Scale up machine type by using NVIDIA Tesla V100 GPUs.
C. Use 8 NVIDIA Tesla K80 GPUs instead of the current 4 P100 GPUs.
D. Increase number of Tesla P100 GPUs used until test results return satisfactory performance.
Answer: A.
Description:
Google built the Tensor Processing Unit (TPU) in order to make it possible for data scientists to achieve business and research breakthroughs ranging from network security to medical diagnoses. Cloud TPU is the custom-designed machine learning ASIC that powers Google products like Translate, Photos, Search, Assistant, and Gmail.
As for cost, below is a benchmark for cost comparison between TPU & GPU types:
So, for this scenario, using TPU machine type is the recommended type to build Tensorflow models on. Source(s):
TPU Machine Type:
GPU Machine Type:
https://cloud.google.com/gpu/
https://cloud.google.com/blog/products/ai-machine-learning/what-makes-tpus-fine-tuned-for-deep- learning
Using GPUs for training models in the cloud: https://cloud.google.com/ml-engine/docs/tensorflow/using-gpus
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