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Google Data Engineer Sample Questions:

1. You are developing an application on Google Cloud that will automatically generate subject labels for users' blog posts. You are under competitive pressure to add this feature quickly, and you have no additional developer resources. No one on your team has experience with machine learning. What should you do?

A) Call the model from your application and process the results as labels.
B) Deploy the model using Cloud Machine Learning Engin
C) Build and train a text classification model using TensorFlo
D) Build and train a text classification model using TensorFlo
E) Process the generated Sentiment Analysis as labels.
F) Deploy the model using a KubernetesEngine cluste
G) Call the model from your application and process the results as labels.
H) Process the generated Entity Analysis as labels.
I) Call the Cloud Natural Language API from your applicatio
J) Call the Cloud Natural Language API from your applicatio

2. You are building a model to predict whether or not it will rain on a given day. You have thousands of input features and want to see if you can improve training speed by removing some features while having a minimum effect on model accuracy. What can you do?

A) Remove the features that have null values for more than 50% of the training records.
B) Instead of feeding in each feature individually, average their values in batches of 3.
C) Combine highly co-dependent features into one representative feature.
D) Eliminate features that are highly correlated to the output labels.

3. You have a data stored in BigQuery. The data in the BigQuery dataset must be highly available. You need to define a storage, backup, and recovery strategy of this data that minimizes cost. How should you configure the BigQuery table?

A) In the event of an emergency, use a point-in-time snapshot to recover the data.
B) Set the BigQuery dataset to be multi-regional
C) In the event of an emergency, use a point-in-time snapshot to recover the data.
D) Set the BigQuery dataset to be multi-regional
E) In the event of an emergency, use the backup copy of the table.
F) Create a scheduled query to make copies of the data to tables suffixed with the time of the backu
G) In the event of an emergency, use the backup copy of the table.
H) Create a scheduled query to make copies of the data to tables suffixed with the time of the backup
I) Set the BigQuery dataset to be regiona
J) Set the BigQuery dataset to be regiona

4. Which of the following is NOT true about Dataflow pipelines?

A) Dataflow pipelines are tied to Dataflow, and cannot be run on any other runner
B) Dataflow pipelines can consume data from other Google Cloud services
C) Dataflow pipelines can be programmed in Java
D) Dataflow pipelines use a unified programming model, so can work both with streaming and batch data sources

5. Which of the following is NOT one of the three main types of triggers that Dataflow supports?

A) Trigger that is a combination of other triggers
B) Trigger based on element count
C) Trigger based on element size in bytes
D) Trigger based on time

Solutions: