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| Certification Vendor: | Amazon Web Services (AWS) |
|---|---|
| Exam Name: | AWS Certified SysOps Administrator - Associate (SOA-C03) |
| Exam Number: | SOA-C03 |
| Passing Score: | 720 (scaled score out of 1000) |
| Real Exam Qty: | 65 questions |
| Available Languages: | Korean, English, Japanese, German, Simplified Chinese, Spanish (Latin America), French |
| Exam Duration: | 130 minutes |
| Certificate Validity Period: | 3 years |
| Exam Price: | 150 USD |
| Exam Format: | Multiple response, Multiple choice |
| Related Certifications: | AWS Certified Developer - Associate AWS Certified Solutions Architect - Associate |
| Recommended Training: | AWS Certified SysOps Administrator Associate Training (Official Learning Resources) AWS Skill Builder - SysOps Administrator Learning Path |
| Exam Registration: | AWS Certification Official Registration AWS Certification Portal |
| Sample Questions: | Amazon SOA-C03 Sample Questions |
| Exam Way: | Online proctored or test center delivery |
| Pre Condition: | No formal prerequisites, but AWS recommends at least 1 year of experience managing AWS workloads in a SysOps or cloud operations role. |
| Official Syllabus URL: | https://aws.amazon.com/certification/certified-sysops-administrator-associate/ |
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NEW QUESTION # 99
A company has an Amazon EC2 instance that has high CPU utilization. The EC2 instance is a t3.large instance and is running a test web application. The company discovers that the web application would operate better on a compute optimized large instance. What should a CloudOps engineer do to make this change?
Answer: C
Explanation:
For an EBS-backed EC2 instance, the instance must be stopped before changing its instance type. After stopping the instance, the CloudOps engineer can change it from t3.large to an appropriate compute optimized instance type and then restart it.
NEW QUESTION # 100
A company's Amazon EC2 instance with high CPU utilization is a t3.large instance running a test web app. The company determines the app would run better on a compute-optimized large instance.
What should the CloudOps engineer do?
Answer: C
Explanation:
As described in the AWS Cloud Operations and EC2 Management documentation, changing an instance type (e.g., from T3 to C5) requires that the instance be stopped first. Once stopped, the engineer can modify the instance type through the AWS Management Console, CLI, or API, then start the instance again to apply changes.
This process preserves the root volume, networking configuration, and data, making it an operationally safe and efficient way to upgrade to a different instance family.
Changing the instance type while running (Option D) is unsupported. VM Import/Export (Option A) is for external VM migration. Hibernation (Option B) does not apply to type changes.
Thus, Option C is correct - stopping the instance, changing its type, and restarting it meets AWS best practices.
NEW QUESTION # 101
A company has a microservice that runs on a set of Amazon EC2 instances. The EC2 instances run behind an Application Load Balancer (ALB).
A CloudOps engineer must use Amazon Route 53 to create a record that maps the ALB URL to example.com.
Which type of record will meet this requirement?
Answer: D
Explanation:
An alias record is the recommended Route 53 record type to map domain names (e.g., example.com) to AWS-managed resources such as an Application Load Balancer. Alias records are extension types of A or AAAA records that support AWS resources directly, providing automatic DNS integration and no additional query costs.
"Use alias records to map your domain or subdomain to an AWS resource such as an Application Load Balancer, CloudFront distribution, or S3 website endpoint." A and AAAA records are used for static IP addresses, not load balancers. CNAME records cannot be used at the root domain (e.g., example.com). Thus, Option C is correct as it meets CloudOps networking best practices for scalable, managed DNS resolution to ALBs.
NEW QUESTION # 102
A company has a critical serverless application that uses multiple AWS Lambda functions. Each Lambda function generates 1 GB of log data daily in its own Amazon CloudWatch Logs log group. The company's security team asks for a count of application errors, grouped by type, across all of the log groups.
What should a CloudOps engineer do to meet this requirement?
Answer: C
Explanation:
CloudWatch Logs Insights is purpose-built for interactive querying and analysis across log data that is stored in CloudWatch Logs. It supports selecting multiple log groups at once (including many Lambda log groups), filtering events to match error patterns, extracting fields, and aggregating results. The requirement is to produce a count of application errors grouped by type across all log groups, which aligns directly with Logs Insights capabilities.
With Logs Insights, a CloudOps engineer can query all relevant Lambda log groups, parse or extract an "error type" field from structured JSON logs (or use pattern parsing for unstructured logs), and then aggregate using the stats command with count() grouped by the parsed error type field. This approach is fast to implement, requires no data pipeline, and scales well for large volumes because Logs Insights is optimized for CloudWatch Logs data. It also supports time-range selection so the security team can request daily or incident- window reporting.
Option B is incorrect because CloudWatch Logs "search" is a basic filtering feature and does not provide the same structured aggregation and grouping features as Logs Insights for large-scale, cross-log-group analytics.
Option C (Athena) would require exporting logs to Amazon S3 (for example, via subscription filters, Kinesis Data Firehose, or scheduled exports) and maintaining a schema and partitions, which adds operational overhead not required here. Option D is not applicable because CloudWatch log data is not queried through Amazon RDS, and using an RDS database for log analytics would introduce significant ingestion and operational complexity.
Therefore, running a CloudWatch Logs Insights query using stats and count() to group errors by type across all Lambda log groups is the correct solution.
NEW QUESTION # 103
A CloudOps engineer wants to configure observability of specific metrics for a public website that runs on Amazon Elastic Kubernetes Service (Amazon EKS). The CloudOps engineer wants to observe latency, traffic, errors, and saturation metrics. The CloudOps engineer wants to define service level objectives (SLOs) and monitor service level indicators (SLIs). The CloudOps engineer also wants to correlate metrics, logs, and traces to support faster issue resolution.
Which solution will meet these requirements with the LEAST operational effort?
Answer: C
Explanation:
Amazon CloudWatch Application Signals is designed to provide built-in observability for containerized workloads, including Amazon EKS. It automatically collects golden signals such as latency, traffic, errors, and saturation without requiring complex instrumentation.
Application Signals supports SLO definition and SLI monitoring and correlates metrics, logs, and traces within CloudWatch. This significantly reduces operational overhead compared to manually configuring OpenTelemetry pipelines, Prometheus, and Grafana.
CloudWatch RUM and Synthetics focus on frontend monitoring, not backend service observability.
Application Insights does not provide full SLO/SLI support for EKS workloads.
Therefore, CloudWatch Application Signals is the correct and least-effort solution.
NEW QUESTION # 104
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