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Google GCP-DE Exam Syllabus Topics:

Google Data Engineer Sample Questions:

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

Question 2

MJTelco needs you to create a schema in Google Bigtable that will allow for the historical analysis of the last 2 years of records. Each record that comes in is sent every 15 minutes, and contains a unique identifier of the device and a data record. The most common query is for all the data for a given device for a given day. Which schema should you use?

A. Rowkey: date#data_pointColumn data: device_id
B. Rowkey: dateColumn data: device_id, data_point
C. Rowkey: data_pointColumn data: device_id, date
D. Rowkey: date#device_idColumn data: data_point
E. Rowkey: device_idColumn data: date, data_point

Question 3

You are a retailer that wants to integrate your online sales capabilities with different in-home assistants, such as Google Home. You need to interpret customer voice commands and issue an order to the backend systems. Which solutions should you choose?

A. Dialogflow Enterprise Edition
B. Cloud Natural Language API
C. Cloud Speech-to-Text API
D. Cloud AutoML Natural Language

Question 4

You work for an economic consulting firm that helps companies identify economic trends as they happen. As part of your analysis, you use Google BigQuery to correlate customer data with the average prices of the 100 most common goods sold, including bread, gasoline, milk, and others. The average prices of these goods are updated every 30 minutes. You want to make sure this data stays up to date so you can combine it with other data in BigQuery as cheaply as possible. What should you do?

A. Store the data in a file in a regional Google Cloud Storage bucke
B. Load the data every 30 minutes into a new partitioned table in BigQuery.
C. Store and update the data in a regional Google Cloud Storage bucket and create a federated data source in BigQuery
D. Use Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Google Cloud Storage.
E. Use Google Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Cloud Datastore
F. Store the data in Google Cloud Datastor

Question 5

You are creating a new pipeline in Google Cloud to stream IoT data from Cloud Pub/Sub through Cloud Dataflow to BigQuery. While previewing the data, you notice that roughly 2% of the data appears to be corrupt. You need to modify the Cloud Dataflow pipeline to filter out this corrupt data. What should you do?

A. Add a ParDo transform in Cloud Dataflow to discard corrupt elements.
B. Add a GroupByKey transform in Cloud Dataflow to group all of the valid data together and discard the rest.
C. Add a Partition transform in Cloud Dataflow to separate valid data from corrupt data.
D. Add a SideInput that returns a Boolean if the element is corrupt.

Solutions: