Chapter 5 of 5 · Google Cloud Professional Data Engineer
Maintaining and automating data workloads
Cost, Composer DAGs, capacity, monitoring and failover.
- 5topics
- 26 minfast track
- 18%of the exam
Topics in this chapter
Take them in order, or jump to the one you need.
- 1Optimizing Cost and Resources for Data WorkloadsCut BigQuery, Dataflow and Dataproc costs per business need, protect capacity for critical jobs, and choose persistent or…5 min
- 2Cloud Composer DAGs and Repeatable SchedulingWrite Cloud Composer DAGs with operators, sensors, retries and templated dates, make tasks idempotent for backfills, and pick a…5 min
- 3BigQuery Editions, Reservations and Query PriorityBigQuery capacity: on-demand or editions, baseline and autoscaling slots, commitments, reservations, assignments, idle slots…5 min
- 4Monitoring and Troubleshooting Data ProcessesObserve pipelines with Cloud Monitoring, Cloud Logging and BigQuery admin charts, track planned usage, and fix common errors…5 min
- 5Fault Tolerance, Recovery and FailoverDesign data workloads for restarts, zones and regions, recover from corrupt or missing data, and use replication and failover in…6 min
About this chapter
What this chapter covers
Maintaining and automating data workloads is about 18% of the Google Cloud Professional Data Engineer exam. It covers running data systems day after day: keeping costs down while critical jobs still get the capacity they need, automating work so it repeats reliably, watching for problems and recovering from failures.
It covers optimizing resources (cost per business need, capacity for critical processes, persistent or job-based Dataproc clusters), automation and repeatability (Cloud Composer DAGs and scheduling), organizing workloads (BigQuery editions, reservations, interactive and batch queries), monitoring and troubleshooting (Cloud Monitoring, Cloud Logging, BigQuery administration, planned usage, errors, billing and quotas) and failure awareness (fault tolerance, restarts, multiple zones and regions, corrupt or missing data, replication and failover).
The exam has 40 to 50 multiple choice and multiple select questions in 2 hours. Operations questions often give a symptom, such as rising costs, queued queries or a failed job, and ask "What should you do?"; the best answer fixes the cause with the least ongoing effort.
How the topics fit together
Each step feeds the next: automation needs predictable capacity, monitoring shows whether capacity and automation work, and recovery plans decide what monitoring must catch.
Prefer managed, automatic answers. Autoscaling, job-based clusters, alerting policies and managed replication beat scripts that someone has to run by hand.
How to use these pages
Each topic page has the same shape, so you can skim for what you need.
- What it is: the idea in plain words.
- How it works: the mechanism, with tables and code examples.
- What to remember for the exam: the facts to memorise and how questions are phrased.
- Common mistakes: the classic traps.
- Check yourself: three questions to test your understanding.
Ready for more practice?
The full Google Cloud Professional Data Engineer question bank has 733 questions, with timed practice exams and an explanation for every answer.
This study guide is independent and is not affiliated with, endorsed by, or sponsored by Google Cloud. Vendor names, certification names and exam codes are trademarks of their respective owners and are used for identification only.