SOA-C02 Monitoring, Logging, and Remediation Practice Question
A company runs a multi-tier application that uses an Amazon RDS for PostgreSQL database. The SysOps administrator needs to monitor the database for performance anomalies, such as sudden spikes in connections or query latencies. The administrator wants to receive alerts when metrics deviate from their expected baseline. The solution must automatically adjust to changes in normal behavior over time, such as seasonal patterns. Which AWS service or feature should the administrator use?
⚠ Common exam trap
Test-takers frequently confuse Performance Insights (a diagnostic tool for analyzing database load) with a monitoring and alerting solution, overlooking that it does not provide adaptive baselines or automatic anomaly detection.
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
✓
Configure Amazon CloudWatch Anomaly Detection on the relevant RDS metrics (e.g., DatabaseConnections, ReadLatency, WriteLatency) and set an alarm to notify when the metric breaches the anomaly band.
Amazon CloudWatch Anomaly Detection uses machine learning to continuously analyze metric patterns and establish a dynamic baseline that adapts to seasonal trends and gradual changes in normal behavior. By applying anomaly detection to RDS metrics like DatabaseConnections, ReadLatency, and WriteLatency, the administrator can set an alarm that triggers when a metric deviates outside the calculated anomaly band, automatically adjusting to evolving traffic patterns without manual threshold updates.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Configure Amazon CloudWatch Anomaly Detection on the relevant RDS metrics (e.g., DatabaseConnections, ReadLatency, WriteLatency) and set an alarm to notify when the metric breaches the anomaly band.
Why this is correct
CloudWatch Anomaly Detection uses machine learning to automatically model the expected patterns of RDS metrics such as DatabaseConnections, ReadLatency, and WriteLatency, including daily and weekly seasonal trends. It builds a dynamic baseline band around the metric's normal behavior and can trigger an alarm when data points breach that band, with no need to manually define static thresholds. The alarm action can notify via SNS, providing the automated, adaptive monitoring required to detect unusual RDS behavior without operator intervention.
- ✗
Use Amazon RDS Performance Insights to analyze database load and set CloudWatch alarms on the DBLoad metric with static thresholds.
Why it's wrong here
Amazon RDS Performance Insights is a diagnostic tool that visualizes database load in average active sessions (DBLoad), helping you spot bottlenecks after they occur. However, it does not perform automated baseline anomaly detection; you would need to pair it with CloudWatch alarms using static thresholds, which require manual tuning to known normal values and do not adapt to seasonal patterns or gradual workload changes. Performance Insights also has no built-in alerting, so it fails to meet the requirement for automatic notification when behavior deviates from an evolving baseline.
- ✗
Enable Amazon CloudWatch Metrics Explorer to create a dashboard that visualizes the metrics and manually review for anomalies.
Why it's wrong here
Amazon CloudWatch Metrics Explorer is a visualization feature that lets you query and graph multiple metrics interactively to explore trends and correlations across your resources. It does not provide any automated anomaly detection, baseline computation, or alerting mechanism; detecting an anomaly would require a human to watch the dashboard and manually spot deviations from expected patterns. Since the requirement explicitly calls for setting an alarm to notify on anomalies, this option lacks the necessary automated monitoring and proactive notification behavior.
- ✗
Use AWS X-Ray to trace database queries and set alarms on trace segment durations.
Why it's wrong here
AWS X-Ray is a distributed tracing service that samples and tracks requests as they flow through application components, producing trace segments and service maps to help debug latency and errors. It operates at the request/segment level, not on Amazon RDS instance-level telemetry such as connection counts, read latency, or write latency, so it cannot analyze or alarm on these metric anomalies. Even if you could trace database queries, X-Ray does not provide anomaly detection on metric time series, making it irrelevant to this monitoring scenario.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This SOA-C02 practice question is part of Courseiva's free Amazon Web Services 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 SOA-C02 exam.