Google PCA Manage implementation of cloud architecture Practice Question
A company uses Cloud Bigtable for time-series data. They experience high latency and uneven load distribution across nodes. What is the most likely cause?
⚠ Common exam trap
Google Cloud often tests the misconception that column families or read consistency levels are the root cause of performance issues, when in fact row key design is the primary driver of load distribution in Bigtable.
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
✓
The table has a single row key pattern that causes hot spotting
Cloud Bigtable partitions data by row key range and distributes tablets across nodes. A single row key pattern (e.g., monotonically increasing timestamps) causes all writes to target the same tablet, creating a hot spot. This leads to uneven load distribution and high latency because one node is overwhelmed while others remain idle.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The data is stored in a single column family
Why it's wrong here
Column families don't affect distribution.
- ✗
The app is using strong reads instead of eventual consistency
Why it's wrong here
Strong reads are not the cause of uneven load.
- ✓
The table has a single row key pattern that causes hot spotting
Why this is correct
Sequential row keys lead to hot spots.
- ✗
The cluster has too many nodes
Why it's wrong here
More nodes usually improve performance.
Go deeper
Related to this question
Learn chapter
Introduction to Google Cloud Platform
Key term
Row
A row is a horizontal record in a database table that contains all the information about a single entity, like one customer or one product.
Key term
Data
Data is raw, unprocessed information, like numbers, words, or measurements, that can be stored, processed, and analyzed by computers.
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