Google Professional Data Engineer Google Certified Professional – Data Engineer Online Training
Google Professional Data Engineer Online Training
The questions for Professional Data Engineer were last updated at Jul 16,2025.
- Exam Code: Professional Data Engineer
- Exam Name: Google Certified Professional – Data Engineer
- Certification Provider: Google
- Latest update: Jul 16,2025
The CUSTOM tier for Cloud Machine Learning Engine allows you to specify the number of which types of cluster nodes?
- A . Workers
- B . Masters, workers, and parameter servers
- C . Workers and parameter servers
- D . Parameter servers
Which software libraries are supported by Cloud Machine Learning Engine?
- A . Theano and TensorFlow
- B . Theano and Torch
- C . TensorFlow
- D . TensorFlow and Torch
Which TensorFlow function can you use to configure a categorical column if you don’t know all of the possible values for that column?
- A . categorical_column_with_vocabulary_list
- B . categorical_column_with_hash_bucket
- C . categorical_column_with_unknown_values
- D . sparse_column_with_keys
Which of the following statements about the Wide & Deep Learning model are true? (Select 2 answers.)
- A . The wide model is used for memorization, while the deep model is used for generalization.
- B . A good use for the wide and deep model is a recommender system.
- C . The wide model is used for generalization, while the deep model is used for memorization.
- D . A good use for the wide and deep model is a small-scale linear regression problem.
To run a TensorFlow training job on your own computer using Cloud Machine Learning Engine, what would your command start with?
- A . gcloud ml-engine local train
- B . gcloud ml-engine jobs submit training
- C . gcloud ml-engine jobs submit training local
- D . You can’t run a TensorFlow program on your own computer using Cloud ML Engine .
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?
- A . Unsupervised learning
- B . Regressor
- C . Classifier
- D . Clustering estimator
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?
- A . Use K-means Clustering to detect faces in the pixels.
- B . Use feature engineering to add features for eyes, noses, and mouths to the input data.
- C . Use deep learning by creating a neural network with multiple hidden layers to automatically detect features of faces.
- D . Build a neural network with an input layer of pixels, a hidden layer, and an output layer with two categories.
What are two of the characteristics of using online prediction rather than batch prediction?
- A . It is optimized to handle a high volume of data instances in a job and to run more complex models.
- B . Predictions are returned in the response message.
- C . Predictions are written to output files in a Cloud Storage location that you specify.
- D . It is optimized to minimize the latency of serving predictions.
Which of these are examples of a value in a sparse vector? (Select 2 answers.)
- A . [0, 5, 0, 0, 0, 0]
- B . [0, 0, 0, 1, 0, 0, 1]
- C . [0, 1]
- D . [1, 0, 0, 0, 0, 0, 0]
How can you get a neural network to learn about relationships between categories in a categorical feature?
- A . Create a multi-hot column
- B . Create a one-hot column
- C . Create a hash bucket
- D . Create an embedding column
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