SOA-C02 Monitoring, Logging, and Remediation Practice Question
A SysOps administrator monitors a custom business metric published to Amazon CloudWatch. The metric exhibits irregular spikes that are not predictable. The administrator needs to be alerted when the metric deviates significantly from its normal pattern. Which CloudWatch feature should be used to set up the alarm with the least manual tuning?
⚠ Common exam trap
Watch out — candidates often confuse CloudWatch Metric Math with standard deviation (Option B) as a way to detect anomalies, but it requires manual formula creation and does not automatically adapt to pattern changes, unlike Anomaly Detection which learns and adjusts the baseline over time.
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
✓
CloudWatch Anomaly Detection
CloudWatch Anomaly Detection uses machine learning to automatically establish a baseline for a metric's normal pattern and create a band of expected values. When the metric deviates outside this band, it triggers an alarm without requiring manual threshold tuning, making it ideal for unpredictable, irregular spikes.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
CloudWatch Logs metric filter
Why it's wrong here
CloudWatch Logs metric filters parse log streams and emit numeric values by applying pattern matching and filters to extract metrics from log entries. This option works only when the data you want to monitor is available as textual log records, not when you already have a custom metric being published to CloudWatch. It also does not provide adaptive thresholds; you would still have to define static alarm limits against the extracted metric.
- ✗
CloudWatch Metric Math with standard deviation
Why it's wrong here
CloudWatch Metric Math lets you write expressions that combine existing metrics, such as computing a rolling average plus a multiple of the standard deviation, to produce a calculated time series. You must manually choose the number of standard deviations and periodically update the expression as the underlying business behavior changes, since the threshold value is derived from static arithmetic rather than learned from long-term patterns. This approach is labor-intensive and does not automatically adjust to seasonal or irregular changes the way an ML-based anomaly detection model does.
- ✓
CloudWatch Anomaly Detection
Why this is correct
CloudWatch Anomaly Detection applies machine learning algorithms to analyze a metric's historical behavior, including daily and weekly seasonality, and produces a dynamic baseline band with upper and lower thresholds. For a custom business metric with irregular patterns, these thresholds continually adapt as new data arrives, so you do not need to manually recalibrate for changing normal behavior. It emits an anomaly detection band that can be used in alarms to alert on deviations from expected values.
- ✗
AWS CloudTrail Insights
Why it's wrong here
AWS CloudTrail Insights is designed to detect unusual operational activity in your AWS account by analyzing API call patterns, such as spikes in access or errors, from CloudTrail logs. It does not ingest or evaluate custom business metrics that you publish to CloudWatch; rather, it focuses on governance, security, and operational health of AWS services. Therefore it is not a mechanism for monitoring a custom application-level metric.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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