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| Section | Weight | Objectives |
|---|---|---|
| Monitoring, Logging, Analysis, Remediation, and Performance Optimization | 22% | - Collect and interpret metrics, logs, and events
|
| Deployment, Provisioning, and Automation | 20% | - Automate operational tasks and workflows
|
| Reliability and Business Continuity | 22% | - Design and implement high availability and fault tolerance
|
| Security and Compliance | 18% | - Implement identity and access management
|
| Networking and Content Delivery | 18% | - Optimize and troubleshoot network connectivity and performance
|
You can change the difficulty of these questions, which will help you determine what areas appertain to more study before taking your Amazon SOA-C03 Exam Dumps. Here we listed some of the most important benefits you can get from using our Amazon SOA-C03 practice questions.
NEW QUESTION # 13
A company has created a new video-on-demand (VOD) application. The application runs on a fleet of Amazon EC2 instances behind an Application Load Balancer (ALB). The company configured an Amazon CloudFront distribution and set the ALB as the origin. Because of increasing application demand, the company wants to move all video files to a central Amazon S3 bucket.
A CloudOps engineer needs to ensure that video files can be cached at edge locations after the company migrates the files to Amazon S3.
Which solution will meet this requirement?
Answer: C
Explanation:
CloudFront supports multiple origins with cache behaviors that route requests based on URL path patterns. By adding the S3 bucket as a new origin and creating a cache behavior for the video path, CloudFront can fetch the video files from S3 and cache them at edge locations for subsequent requests.
NEW QUESTION # 14
A company uses Amazon ElastiCache (Redis OSS) to cache application data. A CloudOps engineer must implement a solution to increase the resilience of the cache. The solution also must minimize the recovery time objective (RTO).
Which solution will meet these requirements?
Answer: B
Explanation:
Comprehensive and Detailed Explanation From Exact Extract of AWS CloudOps Doocuments:
For high availability and fast failover, ElastiCache for Redis supports replication groups with Multi-AZ and automatic failover. CloudOps guidance states that a primary node can be paired with one or more replicas across multiple Availability Zones; if the primary fails, Redis automatically promotes a replica to primary in seconds, thereby minimizing RTO. This architecture maintains in-memory data continuity without waiting for backup restore operations. Backups (Options B and D) provide durability but require restore and re-warm procedures that increase RTO and may impact application latency. Switching engines (Option A) to Memcached does not provide Redis replication/failover semantics and would not inherently improve resilience for this use case. Therefore, creating a read replica in a different AZ and enabling Multi-AZ with automatic failover is the prescribed CloudOps pattern to increase resilience and achieve the lowest practical RTO for Redis caches.
References:* AWS Certified CloudOps Engineer - Associate (SOA-C03) Exam Guide - Reliability and Business Continuity* Amazon ElastiCache for Redis - Replication Groups, Multi-AZ, and Automatic Failover* AWS Well-Architected Framework - Reliability Pillar
NEW QUESTION # 15
A company is using an Amazon Aurora MySQL DB cluster that has point-in-time recovery, backtracking
, and automatic backup enabled. A CloudOps engineer needs to roll back the DB cluster to a specific recovery point within the previous 72 hours. Restores must be completed in the same production DB cluster.
Which solution will meet these requirements?
Answer: D
Explanation:
As documented in AWS Cloud Operations and Database Recovery, Aurora Backtrack allows you to rewind the existing database cluster to a chosen point in time without creating a new cluster. This feature supports fine-grained rollback for accidental data changes, making it ideal for scenarios like table deletions or logical corruption.
Backtracking maintains continuous transaction logs and permits rewinding within a configurable window (up to 72 hours). It does not require creating a new cluster or endpoint, and it preserves the same production environment, fulfilling the operational requirement for in-place recovery.
In contrast, Point-in-Time Recovery (Option D) always creates a new cluster, while replica promotion (Option A) and Lambda restoration (Option B) are unrelated to immediate rollback operations.
Therefore, Option C, using Aurora Backtrack, best meets the requirement for same-cluster restoration and minimal downtime.
Reference: AWS Cloud Operations & Database Management Guide - Section: Using Aurora Backtrack for Fast In-Place Recovery
NEW QUESTION # 16
A company runs thousands of Amazon EC2 instances that are based on the Amazon Linux 2 Amazon Machine Image (AMI). A SysOps administrator must implement a solution to record commands and output from any user that needs an interactive session on one of the EC2 instances. The solution must log the data to a durable storage location. The solution also must provide automated notifications and alarms that are based on the log data.
Which solution will meet these requirements with the MOST operational efficiency?
Answer: B
Explanation:
Comprehensive and Detailed Explanation From Exact Extract of AWS CloudOps Documents:
The most operationally efficient solution is C because AWS Systems Manager Session Manager is purpose- built for secure, auditable interactive access to EC2 instances at scale-without managing bastion hosts or distributing SSH keys. Session Manager can be configured to log session activity, including commands and output, to durable destinations such as Amazon CloudWatch Logs (and optionally Amazon S3). This directly satisfies the requirement to record interactive sessions and store logs durably.
For automated notifications and alarms, CloudWatch Logs supports metric filters that transform matching log patterns into CloudWatch metrics. Those metrics can then drive CloudWatch alarms and notifications (for example, via Amazon SNS). This is a standard CloudOps pattern: centralize logs, derive metrics from security-relevant patterns, and alert automatically.
Option A and D require installing and operating agents and building a more complex analytics path (Athena queries for alerting), which is less efficient and introduces more moving parts across thousands of instances.
Option B adds a bastion host dependency that becomes an operational burden (scaling, patching, hardening, HA) and a potential choke point. Session Manager reduces these burdens by using SSM Agent already installed, IAM-based access control, and centralized logging/monitoring integrations.
References:
AWS Systems Manager User Guide - Session Manager and session logging to CloudWatch Logs/S3 Amazon CloudWatch Logs User Guide - Metric filters and alarms from log patterns AWS SysOps Administrator Study Guide - Centralized logging, auditing, and operational monitoring
NEW QUESTION # 17
A web application runs on Amazon EC2 instances in an Auto Scaling group behind an Application Load Balancer (ALB). During rolling updates, application initialization and cold starts cause 30-second latency spikes. A CloudOps engineer needs to keep latency flat by pre-initializing capacity. The CloudOps engineer must reuse instances across waves without extending the maintenance window.
Which solution will meet these requirements?
Answer: C
Explanation:
An Auto Scaling warm pool keeps pre-initialized instances ready before they are placed into service. This is exactly what the scenario needs because cold starts during rolling updates are causing latency spikes. By using stopped instances that have already completed initialization, replacement capacity can join the Auto Scaling group faster and with less runtime warmup impact. Instance reuse also allows instances to return to the warm pool after scale-in or refresh waves, reducing repeated initialization overhead. Target tracking with instance warmup prevents scaling decisions from being distorted while new instances stabilize. Options B, C, and D do not directly pre-initialize application capacity. Predictive scaling forecasts demand, but it does not solve cold-start latency during controlled deployment waves. Warm pools are the purpose-built CloudOps feature for this requirement.
NEW QUESTION # 18
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