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| Certification Vendor: | Amazon Web Services (AWS) |
|---|---|
| Exam Name: | AWS Certified SysOps Administrator - Associate (SOA-C03) |
| Exam Number: | SOA-C03 |
| Related Certifications: | AWS Certified Developer - Associate AWS Certified Solutions Architect - Associate |
| Passing Score: | 720 (scaled score out of 1000) |
| Exam Price: | 150 USD |
| Exam Format: | Multiple response, Multiple choice |
| Real Exam Qty: | 65 questions |
| Exam Duration: | 130 minutes |
| Available Languages: | French, Spanish (Latin America), Korean, English, Japanese, German, Simplified Chinese |
| Certificate Validity Period: | 3 years |
| 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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質問 # 75
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?
正解：D
解説：
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.
質問 # 76
A company uses Amazon S3 for object storage. A CloudOps engineer notices that the company's Amazon S3 usage has doubled every month across all the company's S3 buckets for the previous year. The company stores and consumes data in the same AWS Region where the data is generated. The company never accesses data that is older than 30 days. The CloudOps engineer needs to optimize Amazon S3 costs for the company.
Which solution will meet this requirement with the LEAST operational overhead?
正解：C
解説：
S3 Lifecycle policies are the correct managed mechanism for automatically expiring objects after a defined age. Because the company never accesses data older than 30 days, an expiration rule can delete objects after
30 days and reduce storage growth without custom code. This has the least operational overhead because S3 performs the expiration automatically. Option A introduces Lambda code, scheduling, permissions, retries, and edge-case handling, which is unnecessary. S3 Storage Lens can identify usage trends and aging objects, but it does not itself delete objects or enforce retention. Option C is vague and not an AWS-managed configuration as written. For CloudOps cost optimization, lifecycle expiration is the standard approach when objects have a predictable retention period and no long-term access requirement.
質問 # 77
A company's VPC has connectivity to an on-premises data center through an AWS Site-to-Site VPN. The company needs Amazon EC2 instances in the VPC to send DNS queries for example.com to the DNS servers in the data center. Which solution will meet these requirements?
正解：D
解説：
Route 53 Resolver outbound endpoints allow DNS queries from resources in a VPC to be forwarded to external DNS servers, such as DNS servers in an on-premises data center. A Resolver forwarding rule for example.com associated with the VPC sends matching DNS queries from the EC2 instances to the on-premises DNS servers over the VPN connection.
質問 # 78
A company has a stateful web application that is hosted on Amazon EC2 instances in an Auto Scaling group.
The instances run behind an Application Load Balancer (ALB) that has a single target group. The ALB is configured as the origin in an Amazon CloudFront distribution. Users are reporting random logouts from the web application.
Which combination of actions should a CloudOps engineer take to resolve this problem? (Select TWO.)
正解：B、E
解説：
Stateful applications require session persistence to ensure that subsequent requests from the same user are routed to the same backend instance. When CloudFront is used in front of an ALB, session-related cookies must be forwarded correctly; otherwise, CloudFront can route requests to different targets, causing session loss and random logouts.
Configuring cookie forwarding in the CloudFront cache behavior ensures that session cookies (such as authentication tokens) are forwarded to the ALB and not stripped or cached incorrectly. Without this configuration, CloudFront may serve cached responses that do not align with the user's active session state, leading to authentication issues.
On the ALB side, sticky sessions (session affinity) must be enabled on the target group to ensure that requests with the same session cookie are consistently routed to the same EC2 instance. ALB stickiness uses application cookies to bind a user session to a specific target, which is critical for stateful applications that store session data in memory.
Option A affects load distribution efficiency but does not address session persistence. Option C (header forwarding) is unnecessary unless the application explicitly stores session state in headers, which is uncommon. Option D applies only when using multiple target groups and listener rules, which is not the case here.
Together, enabling cookie forwarding in CloudFront and sticky sessions at the ALB target group resolves the logout issue by maintaining consistent session routing from the user through CloudFront to the same backend instance.
質問 # 79
A CloudOps engineer needs to give an existing AWS Lambda function access to an existing Amazon S3 bucket. Traffic between the Lambda function and the S3 bucket must not use public IP addresses. The Lambda function has been configured to run in a VPC. Which solution will meet these requirements?
正解：C
解説：
An Amazon S3 VPC interface endpoint provides private connectivity from the Lambda function's VPC to Amazon S3 without using public IP addresses or internet connectivity. The Lambda function can access S3 through the endpoint's private DNS names while traffic remains within the AWS network.
質問 # 80
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