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Monitoring, Logging, and Remediation →hardMultiple ChoiceObjective-mapped

SOA-C02 Monitoring, Logging, and Remediation Practice Question

This SOA-C02 practice question tests your understanding of monitoring, logging, and remediation. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 company runs a critical web application on a fleet of EC2 instances behind an Application Load Balancer (ALB). The instances are in an Auto Scaling group. The operations team uses CloudWatch alarms to monitor the application's health. Recently, they noticed that the application's error rate has increased sporadically, but the CPU utilization and memory usage remain normal. The team suspects that the issue is related to a specific HTTP endpoint returning 5xx errors. They want to set up monitoring that will alert them when the error rate exceeds 5% of total requests over a 5-minute period. The application logs are already sent to CloudWatch Logs. Which combination of steps should the SysOps administrator take to meet this requirement?

A
Create a metric filter in CloudWatch Logs to extract error codes and total requests from the application logs. Create two custom metrics: one for error count and one for total requests. Then create a CloudWatch alarm using a math expression that calculates error rate (error count / total requests) and triggers when >0.05 for 5 minutes.
This directly uses the application logs to compute error rate.
B
Enable AWS X-Ray on the application to trace requests and identify error patterns. Create a CloudWatch alarm on the X-Ray error rate metric.
Why wrong: This option uses AWS X-Ray, which is a tracing service and does not directly provide error rate metrics from logs as needed. X-Ray can help identify specific issues, but it's not the appropriate method for creating a CloudWatch alarm on error rate from application logs.
C
Install the CloudWatch agent on the EC2 instances to collect application-level metrics. Configure the agent to emit a custom metric for error rate. Then create an alarm on that metric.
Why wrong: The application logs are already sent to CloudWatch Logs; installing an agent is redundant and not leveraging existing logs.
D
Enable detailed monitoring on the ALB and create a CloudWatch alarm on the HTTPCode_ELB_5XX metric with a threshold of 5% of the request count. Use the ALB's RequestCount metric to compute the percentage.
Why wrong: Option D is wrong because it uses the ALB's HTTPCode_ELB_5XX metric, but the issue is application-specific, not ALB-level.

Full breakdown with real-world context →

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

✓

Create a metric filter in CloudWatch Logs to extract error codes and total requests from the application logs. Create two custom metrics: one for error count and one for total requests. Then create a CloudWatch alarm using a math expression that calculates error rate (error count / total requests) and triggers when >0.05 for 5 minutes.

Option A is correct because it creates a metric filter on the log group to count errors and total requests, then an alarm on the error rate. Option B is wrong because AWS X-Ray is for tracing, not for error rate monitoring from logs. Option C is wrong because it relies on the CloudWatch agent to generate metrics, which is not already set up. Option D is wrong because it uses the ALB's HTTPCode_ELB_5XX metric, but the issue is application-specific, not ALB-level.

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.

✓
Create a metric filter in CloudWatch Logs to extract error codes and total requests from the application logs. Create two custom metrics: one for error count and one for total requests. Then create a CloudWatch alarm using a math expression that calculates error rate (error count / total requests) and triggers when >0.05 for 5 minutes.
Why this is correct
This directly uses the application logs to compute error rate.
Related concept
Read the scenario before looking for a memorised answer.
✗
Enable AWS X-Ray on the application to trace requests and identify error patterns. Create a CloudWatch alarm on the X-Ray error rate metric.
Why it's wrong here
This option uses AWS X-Ray, which is a tracing service and does not directly provide error rate metrics from logs as needed. X-Ray can help identify specific issues, but it's not the appropriate method for creating a CloudWatch alarm on error rate from application logs.
✗
Install the CloudWatch agent on the EC2 instances to collect application-level metrics. Configure the agent to emit a custom metric for error rate. Then create an alarm on that metric.
Why it's wrong here
The application logs are already sent to CloudWatch Logs; installing an agent is redundant and not leveraging existing logs.
✗
Enable detailed monitoring on the ALB and create a CloudWatch alarm on the HTTPCode_ELB_5XX metric with a threshold of 5% of the request count. Use the ALB's RequestCount metric to compute the percentage.
Why it's wrong here
Option D is wrong because it uses the ALB's HTTPCode_ELB_5XX metric, but the issue is application-specific, not ALB-level.

Common exam traps

Common exam trap: answer the scenario, not the keyword

Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.

Detailed technical explanation

How to think about this question

This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.

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.
Use explanations to understand the rule behind the answer.

TExam Day Tips

Underline the problem statement mentally.
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

An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.

What to study next

Got this wrong? Here's your next step.

Identify which SOA-C02 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.

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FAQ

Questions learners often ask

What does this SOA-C02 question test?

Monitoring, Logging, and Remediation — This question tests Monitoring, Logging, and Remediation — Read the scenario before looking for a memorised answer..

What is the correct answer to this question?

The correct answer is: Create a metric filter in CloudWatch Logs to extract error codes and total requests from the application logs. Create two custom metrics: one for error count and one for total requests. Then create a CloudWatch alarm using a math expression that calculates error rate (error count / total requests) and triggers when >0.05 for 5 minutes. — Option A is correct because it creates a metric filter on the log group to count errors and total requests, then an alarm on the error rate. Option B is wrong because AWS X-Ray is for tracing, not for error rate monitoring from logs. Option C is wrong because it relies on the CloudWatch agent to generate metrics, which is not already set up. Option D is wrong because it uses the ALB's HTTPCode_ELB_5XX metric, but the issue is application-specific, not ALB-level.

What should I do if I get this SOA-C02 question wrong?

What is the key concept behind this question?

Read the scenario before looking for a memorised answer.

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