Amazon SOA-C03 Fragen und Antworten, AWS Certified CloudOps Engineer - Associate Prüfungsfragen

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Amazon SOA-C03 Prüfungsplan:

ThemaEinzelheiten
Thema 1
  • Reliability and Business Continuity: This section measures the skills of System Administrators and focuses on maintaining scalability, elasticity, and fault tolerance. It includes configuring load balancing, auto scaling, Multi-AZ deployments, implementing backup and restore strategies with AWS Backup and versioning, and ensuring disaster recovery to meet RTO and RPO goals.
Thema 2
  • Security and Compliance: This section measures skills of Security Engineers and includes implementing IAM policies, roles, MFA, and access controls. It focuses on troubleshooting access issues, enforcing compliance, securing data at rest and in transit using AWS KMS and ACM, protecting secrets, and applying findings from Security Hub, GuardDuty, and Inspector.
Thema 3
  • Monitoring, Logging, Analysis, Remediation, and Performance Optimization: This section of the exam measures skills of CloudOps Engineers and covers implementing AWS monitoring tools such as CloudWatch, CloudTrail, and Prometheus. It evaluates configuring alarms, dashboards, and notifications, analyzing performance metrics, troubleshooting issues using EventBridge and Systems Manager, and applying strategies to optimize compute, storage, and database performance.
Thema 4
  • Deployment, Provisioning, and Automation: This section measures the skills of Cloud Engineers and covers provisioning and maintaining cloud resources using AWS CloudFormation, CDK, and third-party tools. It evaluates automation of deployments, remediation of resource issues, and managing infrastructure using Systems Manager and event-driven processes like Lambda or S3 notifications.
Thema 5
  • Networking and Content Delivery: This section measures skills of Cloud Network Engineers and focuses on VPC configuration, subnets, routing, network ACLs, and gateways. It includes optimizing network cost and performance, configuring DNS with Route 53, using CloudFront and Global Accelerator for content delivery, and troubleshooting network and hybrid connectivity using logs and monitoring tools.

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Amazon AWS Certified CloudOps Engineer - Associate SOA-C03 Prüfungsfragen mit Lösungen (Q150-Q155):

150. Frage
A company runs custom statistical analysis software on a cluster of Amazon EC2 instances. The software is highly sensitive to network latency between nodes, although network throughput is not a limitation.
Which solution will minimize network latency?

Antwort: B

Begründung:
The AWS Cloud Operations and Compute documentation explains that placement groups control how EC2 instances are physically arranged within AWS data centers to optimize network performance.
Cluster placement groups place instances physically close together within a single Availability Zone, connected through high-bandwidth, low-latency networking (ideal for tightly coupled, HPC, or distributed workloads).
Spread placement groups distribute instances across distinct racks or Availability Zones for fault tolerance, increasing latency.
Partition placement groups separate instances into partitions for isolation, not latency reduction.
Therefore, to minimize latency for workloads such as computational clusters, the CloudOps engineer should use a cluster placement group. This placement ensures single-digit microsecond latency and enhanced packet rate performance between instances.
Elastic IPs (Option B) do not influence internal networking. Jumbo frames (Option C) can marginally improve throughput but do not reduce propagation latency. Spread placement (Option D) increases distance, worsening latency.
Hence, Option A -- using a cluster placement group -- delivers the lowest possible network latency and is AWS's best-practice design for HPC-style clusters.


151. Frage
An AWS Lambda function is intermittently failing several times a day. A CloudOps engineer must find out how often this error occurred in the last 7 days.
Which action will meet this requirement in the MOST operationally efficient manner?

Antwort: B

Begründung:
The AWS Cloud Operations and Monitoring documentation states that Amazon CloudWatch Logs Insights provides a purpose-built query engine for analyzing and visualizing log data directly within CloudWatch. For Lambda, all invocation results (including errors) are automatically logged to CloudWatch Logs.
By querying these logs with CloudWatch Logs Insights, the CloudOps engineer can efficiently count the number of "ERROR" or "Exception" occurrences over the past 7 days using simple SQL-like commands. This method is serverless, cost-efficient, and real-time.
Athena (Options A and B) would require exporting data to Amazon S3, and OpenSearch (Option D) adds unnecessary operational complexity.
Thus, Option C provides the most efficient and native AWS CloudOps approach for rapid Lambda error analysis.


152. Frage
A company is preparing for a marketing campaign that will increase traffic to a new web application. The application uses Amazon API Gateway and AWS Lambda for the application logic. The application stores relevant user data in an Amazon Aurora MySQL DB cluster that has one Aurora Replica. Database queries for the application are 5% write and 95% read. What should a CloudOps engineer do to scale the database when traffic increases?

Antwort: C

Begründung:
Because the application workload is 95% reads, the best database scaling approach is to add or remove Aurora Replicas. Aurora Auto Scaling can automatically adjust the number of Aurora Replicas in a DB cluster based on metrics such as average CPU utilization or average connections across replicas. This allows the read tier to scale as campaign traffic increases while the primary writer continues to handle the smaller write workload. Option B is incorrect because Aurora Auto Scaling scales the number of replicas; it does not vertically resize existing replicas.
Options C and D are worded around generic AWS Auto Scaling rather than Aurora Auto Scaling and use less appropriate metric relationships. Scaling based on replica CPU utilization directly addresses read pressure. Therefore, configure Aurora Auto Scaling for Aurora Replicas.


153. Frage
A company hosts a web application on an Amazon EC2 instance. The web server logs are published to Amazon CloudWatch Logs. The log events have the same structure and include the HTTP response codes that are associated with the user requests. The company needs to monitor the number of times that the web server returns an HTTP 404 response.
What is the MOST operationally efficient solution that meets these requirements?

Antwort: A

Begründung:
A CloudWatch Logs metric filter can automatically scan log events in real time and extract specific patterns - such as HTTP 404 response codes - to publish custom metrics. This provides continuous, automated monitoring without the need for scheduled queries or external scripts, making it the most operationally efficient and scalable solution.


154. Frage
A company uses memory-optimized Amazon EC2 instances behind a Network Load Balancer (NLB) to run an application. The company launched the EC2 instances from an AWS-provided Red Hat Enterprise Linux (RHEL) AMI.
A CloudOps engineer must monitor RAM utilization in 5-minute intervals. The CloudOps engineer must ensure that the EC2 instances scale in and out appropriately based on incoming load.
Which solution will meet these requirements?

Antwort: A

Begründung:
Comprehensive Explanation (250-350 words):
EC2 does not publish RAM utilization as a native CloudWatch metric by default. Memory metrics such as mem_used_percent are typically collected by the CloudWatch Agent, which runs on the instance and publishes custom metrics to CloudWatch. Because the requirement is RAM utilization at 5-minute intervals, the CloudWatch Agent can be configured to emit metrics at that cadence (or faster).
"Detailed monitoring" for EC2 mainly affects EC2-provided metrics (like CPU) by changing the period from
5 minutes (basic) to 1 minute (detailed). It does not magically provide memory utilization. Therefore, the key requirement is installing/configuring the CloudWatch Agent and ensuring it has permissions to publish metrics (via an IAM role attached to the instance / instance profile).
Option C correctly combines: (1) basic monitoring (fine for the ask), (2) CloudWatch Agent to publish mem_used_percent, (3) IAM role permissions to allow publishing, and (4) Auto Scaling policy that scales based on the memory metric.
Option B incorrectly implies detailed monitoring provides mem_used_percent (it does not). Option D assumes a "standard" memory metric exists without the agent, which is not correct. Option A references mem_active, which is not the typical metric name exposed by CloudWatch Agent's standard memory measurements for scaling policies, and also omits the IAM role requirement needed for publishing custom metrics.
Thus, C is the AWS-correct path for memory-based scaling using CloudWatch custom metrics.


155. Frage
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