A shipping company has live package-tracking data that is sent to an Apache Kafka stream in real time. This is
then loaded into BigQuery. Analysts in your company want to query the tracking data in BigQuery to analyze
geospatial trends in the lifecycle of a package. The table was originally created with ingest-date partitioning.
Over time, the query processing time has increased. You need to implement a change that would improve
query performance in BigQuery. What should you do?
Your software uses a simple JSON format for all messages. These messages are published to Google Cloud
Pub/Sub, then processed with Google Cloud Dataflow to create a real-time dashboard for the CFO. During
testing, you notice that some messages are missing in the dashboard. You check the logs, and all messages are
being published to Cloud Pub/Sub successfully. What should you do next?
You are developing a data pipeline that will run several data transformation programs on Compute Engine virtual machines. You do not want to use your credentials for authenticating and authorizing these programs. You want to follow Google Cloud recommended practices, how would you authenticate and authorize the data transformation programs?