The Auto Scaling group cannot scale out because its maximum capacity is already set to 2, the same as the current desired capacity. To automate the resolution, the CloudOps engineer must raise the maximum capacity and configure a scaling policy that adds instances when CPU utilization exceeds the threshold. Since the workload is CPU-bound and peak periods are unpredictable, metric-based scaling is appropriate. Option A adds unmanaged capacity outside the Auto Scaling group, which defeats automated fleet management. Option B correctly increases maximum capacity but incorrectly changes to burstable instances; burstable instances are a poor choice for sustained CPU-heavy workloads. Option D is incomplete because increasing desired capacity alone does not guarantee future automated scale-out unless the maximum capacity also permits it. Therefore, option C is correct.

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According to the AWS Cloud Operations and ElastiCache documentation, cache evictions occur when the cache runs out of memory and must remove items to make space for new data.

To reduce evictions and retain frequently accessed items, AWS recommends increasing the total available memory — either by scaling up to larger node types or scaling out by adding shards/nodes. Migrating to a cluster with larger nodes is the simplest and most efficient solution because it immediately expands capacity without architectural changes.

Adjusting TTL (Options B and C) controls expiration timing, not memory allocation. Adding a single node (Option A) may help, but redistributing data requires resharding, introducing more complexity.

Thus, Option D provides the lowest operational overhead and ensures high cache hit rates by increasing total cache memory.

The least-effort, most scalable approach to ensure consistent software installation and ongoing updates across tagged Windows instances is to use AWS Systems Manager’s native software distribution and desired-state capabilities. Systems Manager Distributor lets you package third-party software as an “SSM package,” version it, and publish updates in a controlled way. This directly supports the requirement to install a third-party agent and deploy periodic updates when new versions are available.

State Manager Associations let you enforce configuration continuously or on a schedule across a fleet, targeting instances by tags (which the scenario explicitly says are in place). By creating an association that runs AWS-ConfigureAWSPackage, you instruct Systems Manager to install (and optionally update) a specific SSM package on all instances that match the Windows tag criteria. This provides ongoing compliance: newly launched instances that receive the tag will automatically get the agent installed, and existing instances will receive updates according to the association’s frequency and the package version strategy.

Option C (custom Lambda that logs in) is high operational overhead and poor practice (credentials, connectivity, error handling, scaling, and security concerns). Option D (RunRemoteScript) can work but is more brittle than ConfigureAWSPackage because it shifts lifecycle management (versioning, state, idempotency) into scripts. Option B (OpsItem + Inventory) is not an enforcement mechanism for installation/updating; it’s for operations tracking and visibility.

Therefore, Distributor (A) + State Manager association using AWS-ConfigureAWSPackage targeted by tags (E) is the cleanest and lowest-ops solution.

The AWS Cloud Operations and Monitoring documentation specifies that the Amazon CloudWatch Agent is the recommended tool for collecting system and application logs from EC2 instances. The agent pushes these logs into a centralized CloudWatch Logs group, providing durable storage and real-time monitoring.

Once the logs are centralized, a CloudWatch Metric Filter can be configured to search for specific error keywords (for example, “ERROR” or “FAILURE”). This filter transforms matching log entries into custom metrics. From there, a CloudWatch Alarm can monitor the metric threshold and publish notifications to an Amazon SNS topic, which can send email or SMS alerts to subscribed recipients.

This combination provides a fully automated, managed, and serverless solution for log aggregation and error alerting. It eliminates the need for manual cron jobs (Option B), custom scripts (Option D), or Lambda-based log streaming (Option C).

AWS Trusted Advisor publishes service quota metrics to Amazon CloudWatch. These metrics can be monitored using CloudWatch alarms, which support threshold-based alerting. By creating a CloudWatch alarm for each service quota metric, the CloudOps engineer can trigger alerts when usage exceeds 60%.

Amazon SNS is the AWS-native service for email notifications. CloudWatch alarms integrate directly with SNS, making this the most straightforward solution. SNS supports email subscriptions without additional infrastructure.

Options B and C incorrectly use SQS for email notifications, which requires additional processing and does not natively send emails. Option D relies on the AWS Health Dashboard, which does not support configurable threshold-based alerts for service quotas.

Therefore, CloudWatch alarms combined with SNS provide the correct and most efficient solution.

AWS Systems Manager Run Command includes a built-in rate control feature that allows administrators to control the maximum number of concurrent executions across target instances. This directly addresses the requirement to limit downloads to 30 at a time without custom orchestration or additional services.

By targeting instances using tags, the solution automatically scales as new instances are added, which aligns with future growth expectations. Rate control ensures controlled concurrency and protects the private repository from overload.

Option A is manual and does not scale operationally. Option B introduces unnecessary complexity with Lambda and concurrency management that does not map cleanly to instance execution concurrency. Option D significantly increases architectural complexity without added value.

Run Command with rate control is the simplest, most native, and most scalable solution.

Auto Scaling groups with target tracking scaling policies automatically adjust capacity based on real-time demand. This solution is ideal for handling unpredictable and random traffic spikes.

Target tracking scaling maintains a metric, such as average CPU utilization or request count per target, at a defined target value. The Auto Scaling group automatically launches or terminates instances as traffic fluctuates.

Manual scaling, EventBridge-based scaling, or scheduled scaling do not respond dynamically to random traffic patterns.

Therefore, an Auto Scaling group with a target tracking scaling policy is the correct solution.

AWS Compute Optimizer analyzes historical utilization metrics and provides rightsizing recommendations for EBS volumes, including IOPS and throughput. It is fully managed, continuously updated, and requires minimal operational effort.

Manual reviews and benchmarking do not scale. Changing instance types does not address EBS overprovisioning.

Therefore, AWS Compute Optimizer is the best solution.

According to AWS Cloud Operations and VPC Networking documentation, when VPCs are peered, security groups can reference peer account security groups directly to restrict traffic between them.

This feature allows specifying the security group ID (sg-1234abcd) from the source account (111122223333) in the target database’s security group inbound rule. AWS automatically validates that the VPCs are connected through an existing VPC peering connection and that mutual permissions are properly configured.

You do not prefix the security group ID with the account or peering connection (Options A and B), and using the destination account ID (Option D) is incorrect because it represents the database side, not the source.

Hence, the correct configuration is Option C, which references the application servers’ security group directly for precise, least-privilege access control.

AWS Backup provides managed backup plans and supports cross-account and cross-Region backup copy workflows for supported AWS resources, including database backup scenarios. This is the least operationally intensive approach because scheduling, retention, copy operations, and monitoring are handled by AWS Backup instead of custom scripts. Option B can work but requires Lambda code, credentials, retries, error handling, and maintenance. Option C exports data to S3 and then replicates files, which is not the same as a managed Aurora backup copy and adds unnecessary complexity. AWS Application Migration Service is intended for server migration and lift-and-shift replication, not daily Aurora database backup copying. Therefore, an AWS Backup plan with the second account and second Region as the destination best matches automated disaster recovery and operational efficiency.

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