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Free Professional Data Engineer Exam Braindumps (page: 20)

Page 19 of 95
PDF with 375 Questions

Which TensorFlow function can you use to configure a categorical column if you don't know all of the possible values for that column?

  1. categorical_column_with_vocabulary_list
  2. categorical_column_with_hash_bucket
  3. categorical_column_with_unknown_values
  4. sparse_column_with_keys

Answer(s): B

Explanation:

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.


Reference:

https://www.tensorflow.org/tutorials/wide



Which of the following statements about the Wide & Deep Learning model are true? (Select 2 answers.)

  1. The wide model is used for memorization, while the deep model is used for generalization.
  2. A good use for the wide and deep model is a recommender system.
  3. The wide model is used for generalization, while the deep model is used for memorization.
  4. A good use for the wide and deep model is a small-scale linear regression problem.

Answer(s): A,B

Explanation:

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.


Reference:

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?

  1. gcloud ml-engine local train
  2. gcloud ml-engine jobs submit training
  3. gcloud ml-engine jobs submit training local
  4. You can't run a TensorFlow program on your own computer using Cloud ML Engine .

Answer(s): A

Explanation:

gcloud ml-engine local train - run a Cloud ML Engine training job locally

This 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.


Reference:

https://cloud.google.com/sdk/gcloud/reference/ml-engine/local/train



If you want to create a machine learning model that predicts the price of a particular stock based on its recent price history, what type of estimator should you use?

  1. Unsupervised learning
  2. Regressor
  3. Classifier
  4. Clustering estimator

Answer(s): B

Explanation:

Regression is the supervised learning task for modeling and predicting continuous, numeric variables. Examples include predicting real-estate prices, stock price movements, or student test scores.

Classification is the supervised learning task for modeling and predicting categorical variables. Examples include predicting employee churn, email spam, financial fraud, or student letter grades.

Clustering is an unsupervised learning task for finding natural groupings of observations (i.e. clusters) based on the inherent structure within your dataset. Examples include customer segmentation, grouping similar items in e-commerce, and social network analysis.


Reference:

https://elitedatascience.com/machine-learning-algorithms






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