Practice Test 1 | Google Cloud Certified Professional Data Engineer | Dumps | Mock Test
You are asked by the data science team to deploy their Tensorflow deep neural network model to the cloud. You choose the ML model in the GCP cloud. Upon checking the available tiers, you suggested choosing a custom tier by launching a cluster with custom specifications to cover the requirements provided to deploy the model.
Which of the following specifications you can set for the ML Model cluster? (Choose TWO)
A. workerCount
B. masterCount
C. masterCPU
D. workerType
Answers: A and D
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.master type 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 workerCount and workerType.
References:
Specifying Machine Types or Scale Tiers:
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