PCDE Practice Question: Manage a solution that can span multiple database technologies
A Cloud Bigtable instance is experiencing high read latency due to hot spots. The operations team uses the Key Visualizer tool and identifies that a small set of row keys are being read disproportionately. Which action should they take to mitigate the hot spotting?
⚠ Common exam trap
A common mistake in Google Cloud exams is to think that adding nodes or changing storage type can fix design-level hotspots, when in fact the correct action is always to address the data distribution pattern at the row key level using the Key Visualizer results.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Redesign the row key scheme to distribute the workload across more tablets.
Hot spotting in Cloud Bigtable is typically caused by a suboptimal row key design that concentrates read/write traffic on a small number of tablets. Redesigning the row key scheme to distribute the workload—for example, by adding a hash prefix or using a more granular key—spreads requests across multiple tablets, alleviating the hotspot. Key Visualizer explicitly identifies the skewed row keys, guiding the redesign effort.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the number of nodes in the Bigtable cluster to handle the load.
Why it's wrong here
Adding nodes helps with overall throughput but does not fix the hot spot on a few row keys.
- ✗
Change the storage type from HDD to SSD to improve performance.
Why it's wrong here
Storage type affects latency but not the distribution of load across row keys.
- ✓
Redesign the row key scheme to distribute the workload across more tablets.
Why this is correct
Row key design is critical. Using a hash or adding a salt prefix spreads reads across nodes.
- ✗
Enable replication across multiple zones.
Why it's wrong here
Replication provides high availability and read scaling but does not solve hot spotting on a single cluster.
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