Practice Test 2 | Google Cloud Certified Professional Data Engineer | Dumps | Mock Test
You want to launch a Cloud Machine Learning Engine cluster to deploy a deep neural network model built by Tensorflow by data scientists of your company. Reviewing the standard tiers available by Google ML Engine, you could not find a tier that suits the requirements data scientists need for the cluster. Google allows you to specify custom cluster specification.
Which of the following specifications you are allowed to set? (Choose 2)
A. workerCount
B. parameterServerCount
C. masterCount
D. workerMemory
Answers: A & B.
The Custom tier is not a set tier, but rather enables you to use your own cluster specification. When you use this tier, set values to configure your processing cluster according to these guidelines:
-
- You must set TrainingInput.masterType to specify the type of machine to use for your master node. This is the only required setting. See the machine types described below.
- You may set TrainingInput.workerCount to specify the number of workers to use. If you specify one or more workers, you must also set TrainingInput.workerType to specify the type of machine to use for your worker nodes.
- You may set TrainingInput.parameterServerCount to specify the number of parameter servers to use. If you specify one or more parameter servers, you must also set TrainingInput.parameterServerType to specify the type of machine to use for your parameter servers.
From the explanation, specifications can be set from the answers are workerCount &
parameterServerCount.
Source(s):
Specifying Machine Types or Scale Tiers: https://cloud.google.com/ml-engine/docs/tensorflow/machine-types
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