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Google Data Engineer Sample Questions:

1. You have historical data covering the last three years in BigQuery and a data pipeline that delivers new data to BigQuery daily. You have noticed that when the Data Science team runs a query filtered on a date column and limited to 30-90 days of data, the query scans the entire table. You also noticed that your bill is increasing more quickly than you expected. You want to resolve the issue as cost-effectively as possible while maintaining the ability to conduct SQL queries. What should you do?

A) Partition the tables by a column containing a TIMESTAMP or DATE Type.
B) Write an Apache Beam pipeline that creates a BigQuery table per data
C) Modify your pipeline to maintain the last 30-90 days of data in one table and the longer history in a different table to minimize full table scans over the entire history.
D) Recommend that the Data Science team export the table to a CSV file on Cloud Storage and use Cloud Datalab to explore the data by reading the files directly.
E) Recommend that the Data Science team use wildcards on the table name suffixes to select the data they need.
F) Re-create the tables using DD

2. You operate an IoT pipeline built around Apache Kafka that normally receives around 5000 messages per second. You want to use Google Cloud Platform to create an alert as soon as the moving average over 1 hour drops below 4000 messages per second. What should you do?

A) Consume the stream of data in Cloud Dataflow using Kafka I
B) Use Kafka Connect to link your Kafka message queue to Cloud Pub/Su
C) If that number falls below 4000, send an alert.
D) If that number falls below 4000, send an alert.
E) Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to BigQuer
F) Use Cloud Scheduler to run a script every hour that counts the number of rows created in Cloud Bigtable in the last hour
G) Consume the stream of data in Cloud Dataflow using Kafka I
H) Use Kafka Connect to link your Kafka message queue to Cloud Pub/Su
I) Compute the average when the window closes, and send an alert if the average is less than 4000 messages.
J) Use Cloud Scheduler to run a script every five minutes that counts the number of rows created in BigQuery in the last hour
K) Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to Cloud Bigtabl
L) Set a sliding time window of 1 hour every 5 minute
M) Set a fixed time window of 1 hour.Compute the average when the window closes, and send an alert if the average is less than 4000 messages.

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

A) Write to Google Cloud Storage; read to Google Cloud Logging
B) Read and write to Google Cloud Storage; write 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

4. You are building a new data pipeline to share data between two different types of applications: jobs generators and job runners. Your solution must scale to accommodate increases in usage and must accommodate the addition of new applications without negatively affecting the performance of existing ones. What should you do?

A) Create a table on Cloud Spanner, and insert and delete rows with the job information
B) Create an API using App Engine to receive and send messages to the applications
C) Create a table on Cloud SQL, and insert and delete rows with the job information
D) Use a Cloud Pub/Sub topic to publish jobs, and use subscriptions to execute them

5. Which of the following job types are supported by Cloud Dataproc (select 3 answers)?

A) Pig
B) YARN
C) Spark
D) Hive

Solutions: