- A
Use Key Visualiser to identify the hot row/key; then redesign the row key to distribute load.
Key Visualiser shows access patterns and hot spots. Redesigning row keys (e.g., salting) spreads the load.
- B
Use 'gcloud bigtable hot-spots list' command; then split the hot table into multiple tables.
Why wrong: No such command; splitting tables is not a typical mitigation.
- C
Use Cloud Monitoring to check CPU utilisation; then add more nodes to the cluster.
Why wrong: Adding nodes helps with overall throughput but does not fix hot spots caused by row key design.
- D
Use the Bigtable admin console to view table statistics; then change the storage type from HDD to SSD.
Why wrong: Changing storage type requires a new cluster and does not address hot spots.
Question 779 of 1,000
PCDOE Manage Database Solutions Practice Question
This PCDOE practice question tests your understanding of manage database solutions. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A Bigtable instance is experiencing high latency and uneven load distribution. The operations team suspects a hot spot. Which tool should they use to identify the hot spot, and what action can they take to mitigate it?
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
✓
Use Key Visualiser to identify the hot row/key; then redesign the row key to distribute load.
Key Visualizer is the correct tool for identifying hot spots in Cloud Bigtable because it provides a heatmap of row key access patterns, allowing you to pinpoint specific rows or key ranges causing uneven load. Once identified, redesigning the row key (e.g., by salting or reversing keys) distributes writes and reads across nodes, mitigating the hot spot without requiring manual table splits or storage changes.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use Key Visualiser to identify the hot row/key; then redesign the row key to distribute load.
Why this is correct
Key Visualiser shows access patterns and hot spots. Redesigning row keys (e.g., salting) spreads the load.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use 'gcloud bigtable hot-spots list' command; then split the hot table into multiple tables.
Why it's wrong here
No such command; splitting tables is not a typical mitigation.
- ✗
Use Cloud Monitoring to check CPU utilisation; then add more nodes to the cluster.
Why it's wrong here
Adding nodes helps with overall throughput but does not fix hot spots caused by row key design.
- ✗
Use the Bigtable admin console to view table statistics; then change the storage type from HDD to SSD.
Why it's wrong here
Changing storage type requires a new cluster and does not address hot spots.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that monitoring CPU or adding nodes solves hot spots, but the trap here is that hot spots are a data design issue, not a capacity issue—only Key Visualizer and key redesign address the root cause.
Trap categories for this question
Command / output trap
No such command; splitting tables is not a typical mitigation.
Detailed technical explanation
How to think about this question
Key Visualizer works by sampling Bigtable's internal metrics over a time window and generating a heatmap that visualizes read/write load per row key prefix. Under the hood, it leverages the `google.bigtable.admin.v2.BigtableTableAdmin` API to collect access patterns, and the heatmap reveals 'hot' keys where throughput exceeds the per-node limit (typically 10,000 writes per second per node). In real-world scenarios, a common mistake is using monotonically increasing keys (e.g., timestamps), which concentrate writes on a single tablet server; salting with a hash prefix or reversing the key spreads load across all nodes.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this PCDOE question test?
Manage Database Solutions — This question tests Manage Database Solutions — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use Key Visualiser to identify the hot row/key; then redesign the row key to distribute load. — Key Visualizer is the correct tool for identifying hot spots in Cloud Bigtable because it provides a heatmap of row key access patterns, allowing you to pinpoint specific rows or key ranges causing uneven load. Once identified, redesigning the row key (e.g., by salting or reversing keys) distributes writes and reads across nodes, mitigating the hot spot without requiring manual table splits or storage changes.
What should I do if I get this PCDOE question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
This PCDOE practice question is part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the PCDOE exam.
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