Objective 5.4
PDEMonitoring and troubleshooting processes
Objective 5.4 sits in Maintaining and automating data workloads, which carries 18% of the Data Engineer exam. The questions below are original, written from the official objective title above, and each explanation cites the Google Cloud page it rests on.
Objective title verbatim from the official objectives. Google Cloud exam page ↗
A worked example
Shown solved, with the whole explanation open: this is what every question here carries.
An engineer sees the data freshness chart climbing steadily on a streaming Dataflow job. What is that metric measuring?
Correct.
Concept
A lag metric is interpretable only once you know which two clocks it subtracts. Confusing arrival with occurrence turns a healthy pipeline into an alarming chart.
Why C
Data freshness is the difference between the time an element is processed and that element's own timestamp. The chart shows the maximum value at any point, reflecting the element with the largest delay.
Now you: objective 5.4 questions
No account needed. The explanation opens when you answer.
Sample question 1 of 3
A team notices system latency rising on a streaming job while a downstream sink has stopped accepting writes. What does that metric count?
Sample question 2 of 3
An analyst reads the backlog seconds chart for a Streaming Engine job and wants to know how far to trust it. What assumption is built into that number?
Sample question 3 of 3
A team examines work item latency states on a streaming job and finds QUEUED times sitting consistently above one second. What does that indicate?
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Read the sources
These are the official pages the questions above cite. Reading them is studying the objective from the primary source, which is what the explanations point you toward anyway.