Quick Answer
The answer is request count, CPU utilization, and storage utilization. These three metrics are critical for monitoring Cloud Bigtable performance because they directly indicate the health and capacity of your cluster: request count measures throughput and helps detect throttling, CPU utilization reveals how close your nodes are to their processing limits, and storage utilization tracks data growth against available capacity. On the Google Professional Cloud Database Engineer exam, this question tests your ability to distinguish operational metrics from less actionable ones like disk I/O or network latency, which are secondary for Bigtable’s architecture. A common trap is choosing latency metrics instead, but latency is a symptom, not a root cause—these three metrics let you proactively scale before performance degrades. Memory tip: think “RCS” for Request, CPU, Storage—the three pillars of Bigtable health.
This PCDE practice question tests your understanding of monitor and optimize database performance. 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.
Which THREE metrics from Cloud Monitoring are important for monitoring Cloud Bigtable performance?
Storage utilization
Why wrong: Storage utilization is for capacity planning, not performance.
CPU utilization
High CPU indicates nodes are busy processing requests.
Latency (P99)
P99 latency shows the worst-case request performance.
Request count
Request count measures throughput and can indicate load patterns.
Disk usage
Why wrong: Disk usage is not a standard metric; Bigtable uses SSD load instead.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
CPU utilization
CPU utilization (option B) is a critical metric for Cloud Bigtable because it directly reflects the processing load on the cluster's nodes. High CPU utilization indicates that the cluster is approaching its throughput limits, which can lead to increased latency and throttling. Monitoring this metric helps in scaling decisions, such as adding nodes or optimizing queries, to maintain performance.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Storage utilization
Why it's wrong here
Storage utilization is for capacity planning, not performance.
CPU utilization
Why this is correct
High CPU indicates nodes are busy processing requests.
Related concept
Read the scenario before looking for a memorised answer.
Latency (P99)
Why this is correct
P99 latency shows the worst-case request performance.
Related concept
Read the scenario before looking for a memorised answer.
Request count
Why this is correct
Request count measures throughput and can indicate load patterns.
Related concept
Read the scenario before looking for a memorised answer.
Disk usage
Why it's wrong here
Disk usage is not a standard metric; Bigtable uses SSD load instead.
Common exam traps
The trap here is that candidates often confuse storage-related metrics (like disk usage or storage utilization) with performance metrics, but Cloud Bigtable abstracts storage management, making CPU, latency, and request count the direct indicators of performance health.
Detailed technical explanation
Cloud Bigtable uses a shared-nothing architecture where each tablet server handles a subset of tablets; CPU utilization per node is a key indicator of load distribution and potential hot-spotting. Latency at the 99th percentile (P99) is particularly important because Bigtable is designed for low-latency access, and high P99 values often indicate compaction storms, uneven data distribution, or resource contention. Request count, when combined with latency, helps calculate effective throughput and identify whether performance degradation is due to increased load or system inefficiencies.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
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FAQ
Monitor and optimize database performance — This question tests Monitor and optimize database performance — Read the scenario before looking for a memorised answer..
The correct answer is: CPU utilization — CPU utilization (option B) is a critical metric for Cloud Bigtable because it directly reflects the processing load on the cluster's nodes. High CPU utilization indicates that the cluster is approaching its throughput limits, which can lead to increased latency and throttling. Monitoring this metric helps in scaling decisions, such as adding nodes or optimizing queries, to maintain performance.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 25, 2026
This PCDE 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 PCDE exam.
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