Free Professional Data Engineer Exam Braindumps (page: 16)

Page 16 of 68

Which of the following IAM roles does your Compute Engine account require to be able to run pipeline jobs?

  1. dataflow.worker
  2. dataflow.compute
  3. dataflow.developer
  4. dataflow.viewer

Answer(s): A

Explanation:

The dataflow.worker role provides the permissions necessary for a Compute Engine service
account to execute work units for a Dataflow pipeline


Reference:

https://cloud.google.com/dataflow/access-control



What Dataflow concept determines when a Window's contents should be output based on certain criteria being met?

  1. Sessions
  2. OutputCriteria
  3. Windows
  4. Triggers

Answer(s): D

Explanation:

Triggers control when the elements for a specific key and window are output. As elements arrive, they are put into one or more windows by a Window transform and its associated WindowFn, and then passed to the associated Trigger to determine if the Windows contents should be output.


Reference:

https://cloud.google.com/dataflow/java- sdk/JavaDoc/com/google/cloud/dataflow/sdk/transforms/windowing/Trigger



The YARN ResourceManager and the HDFS NameNode interfaces are available on a Cloud Dataproc cluster ____.

  1. application node
  2. conditional node
  3. master node
  4. worker node

Answer(s): C

Explanation:

The YARN ResourceManager and the HDFS NameNode interfaces are available on a Cloud Dataproc cluster master node. The cluster master-host-name is the name of your Cloud Dataproc cluster followed by an -m suffix--for example, if your cluster is named "my- cluster", the master-host-name would be "my-cluster-m".


Reference:

https://cloud.google.com/dataproc/docs/concepts/cluster-web- interfaces#interfaces



Suppose you have a dataset of images that are each labeled as to whether or not they contain a human face. To create a neural network that recognizes human faces in images using this labeled dataset, what approach would likely be the most effective?

  1. Use K-means Clustering to detect faces in the pixels.
  2. Use feature engineering to add features for eyes, noses, and mouths to the input data.
  3. Use deep learning by creating a neural network with multiple hidden layers to automatically detect features of faces.
  4. Build a neural network with an input layer of pixels, a hidden layer, and an output layer with two categories.

Answer(s): C

Explanation:

Traditional machine learning relies on shallow nets, composed of one input and one output layer, and at most one hidden layer in between. More than three layers (including input and
output) qualifies as "deep" learning. So deep is a strictly defined, technical term that means more than one hidden layer.
In deep-learning networks, each layer of nodes trains on a distinct set of features based on the previous layer's output. The further you advance into the neural net, the more complex the features your nodes can recognize, since they aggregate and recombine features from the
previous layer.
A neural network with only one hidden layer would be unable to automatically recognize high-level features of faces, such as eyes, because it wouldn't be able to "build" these features using previous hidden layers that detect low-level features, such as lines. Feature engineering is difficult to perform on raw image data. K-means Clustering is an unsupervised learning method used to categorize unlabeled data.


Reference:

https://deeplearning4j.org/neuralnet-overview



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