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Google GCP-DE Exam Syllabus Topics:

Google Data Engineer Sample Questions:

1. MJTelco is building a custom interface to share dat
a. They have these requirements:
They need to do aggregations over their petabyte-scale datasets.
They need to scan specific time range rows with a very fast response time (milliseconds). Which combination of Google Cloud Platform products should you recommend?

A) BigQuery and Cloud Storage
B) Cloud Bigtable and Cloud SQL
C) BigQuery and Cloud Bigtable
D) Cloud Datastore and Cloud Bigtable

2. What is the general recommendation when designing your row keys for a Cloud Bigtable schema?

A) Keep your row key reasonably short
B) Keep the row keep as an 8 bit integer
C) Include multiple time series values within the row key
D) Keep your row key as long as the field permits

3. 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 deep learning by creating a neural network with multiple hidden layers to automatically detect features of faces.
C) Use feature engineering to add features for eyes, noses, and mouths to the input data.
D) Build a neural network with an input layer of pixels, a hidden layer, and an output layer with two categories.

4. What are the minimum permissions needed for a service account used with Google Dataproc?

A) Read and write to Google Cloud Storage; write to Google Cloud Logging
B) Write to Google Cloud Storage; read to Google Cloud Logging
C) Execute to Google Cloud Storage; write to Google Cloud Logging
D) Execute to Google Cloud Storage; execute to Google Cloud Logging

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

Solutions: