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Google Data Engineer Sample Questions:

1. Cloud Dataproc is a managed Apache Hadoop and Apache service.

A) Blaze
B) Spark
C) Ignite
D) Fire

2. You are selecting services to write and transform JSON messages from Cloud Pub/Sub to BigQuery for a data pipeline on Google Cloud. You want to minimize service costs. You also want to monitor and accommodate input data volume that will vary in size with minimal manual intervention. What should you do?

A) Use Cloud Dataflow to run your transformation
B) Use Cloud Dataproc to run your transformation
C) Use the default autoscaling setting for worker instances.
D) Use the diagnose command to generate an operational output archiv
E) Monitor the job system lag with Stackdrive
F) Resize the number of worker nodes in your cluster via the command line.
G) Use Cloud Dataproc to run your transformation
H) Use Cloud Dataflow to run your transformation
I) Monitor CPU utilization for the cluste
J) Monitor the total execution time for a sampling of job
K) Configure the job to use non-default Compute Engine machine types when needed.
L) Locate the bottleneck and adjust cluster resources.

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

4. You work for a shipping company that uses handheld scanners to read shipping labels. Your company has strict data privacy standards that require scanners to only transmit recipients' personally identifiable information (PII) to analytics systems, which violates user privacy rules. You want to quickly build a scalable solution using cloud-native managed services to prevent exposure of PII to the analytics systems. What should you do?

A) Use Stackdriver logging to analyze the data passed through the total pipeline to identify transactions that may contain sensitive information.
B) Install a third-party data validation tool on Compute Engine virtual machines to check the incoming data for sensitive information.
C) Create an authorized view in BigQuery to restrict access to tables with sensitive data.
D) Use the tagging and confidence levels to either pass or quarantine the data in a bucket for review.
E) Build a Cloud Function that reads the topics and makes a call to the Cloud Data Loss Prevention AP

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

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

Solutions: