The CUSTOM tier for Cloud Machine Learning Engine allows you to specify the number of which types of cluster nodes?
Answer(s): C
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.You may set TrainingInput.workerCount to specify the number of workers to use.You may set TrainingInput.parameterServerCount to specify the number of parameter servers to use.You can specify the type of machine for the master node, but you can't specify more than one master node.
https://cloud.google.com/ml-engine/docs/training- overview#job_configuration_parameters
Which software libraries are supported by Cloud Machine Learning Engine?
Cloud ML Engine mainly does two things:Enables you to train machine learning models at scale by running TensorFlow training applications in the cloud.Hosts those trained models for you in the cloud so that you can use them to get predictions about new data.
https://cloud.google.com/ml-engine/docs/technical-overview#what_it_does
Which TensorFlow function can you use to configure a categorical column if you don't know all of the possible values for that column?
Answer(s): B
If you know the set of all possible feature values of a column and there are only a few of them, you can use categorical_column_with_vocabulary_list. Each key in the list will get assigned an auto- incremental ID starting from 0.What if we don't know the set of possible values in advance? Not a problem. We can use categorical_column_with_hash_bucket instead. What will happen is that each possible value in the feature column occupation will be hashed to an integer ID as we encounter them in training.
https://www.tensorflow.org/tutorials/wide
Which of the following statements about the Wide & Deep Learning model are true? (Select 2 answers.)
Answer(s): A,B
Can we teach computers to learn like humans do, by combining the power of memorization and generalization? It's not an easy question to answer, but by jointly training a wide linear model (for memorization) alongside a deep neural network (for generalization), one can combine the strengths of both to bring us one step closer. At Google, we call it Wide & Deep Learning. It's useful for generic large-scale regression and classification problems with sparse inputs (categorical features with a large number of possible feature values), such as recommender systems, search, and ranking problems.
https://research.googleblog.com/2016/06/wide-deep-learning-better-together- with.html
To run a TensorFlow training job on your own computer using Cloud Machine Learning Engine, what would your command start with?
Answer(s): A
gcloud ml-engine local train - run a Cloud ML Engine training job locallyThis command runs the specified module in an environment similar to that of a live Cloud ML Engine Training Job.This is especially useful in the case of testing distributed models, as it allows you to validate that you are properly interacting with the Cloud ML Engine cluster configuration.
https://cloud.google.com/sdk/gcloud/reference/ml-engine/local/train
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