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Google Data Engineer Sample Questions:

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

2. You launched a new gaming app almost three years ago. You have been uploading log files from the previous day to a separate Google BigQuery table with the table name format LOGS_yyyymmdd. You have been using table wildcard functions to generate daily and monthly reports for all time ranges. Recently, you discovered that some queries that cover long date ranges are exceeding the limit of 1,000 tables and failing. How can you resolve this issue?

A) Convert all daily log tables into date-partitioned tables
B) Convert the sharded tables into a single partitioned table
C) Create separate views to cover each month, and query from these views
D) Enable query caching so you can cache data from previous months

3. 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) Use Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Google Cloud Storage.
D) Store the data in Google Cloud Datastor
E) Use Google Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Cloud Datastore
F) Store and update the data in a regional Google Cloud Storage bucket and create a federated data source in BigQuery

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

5. Which of these sources can you not load data into BigQuery from?

A) Google Drive
B) Google Cloud SQL
C) File upload
D) Google Cloud Storage

Solutions: