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Comprehensive and Detailed Explanation From Exact Extract of AWS CloudOps Doocuments:

EC2 Instance Connect Endpoint (EIC Endpoint) enables SSH to instances in private subnets without public IPs and without needing to traverse the public internet. CloudOps guidance explains that you deploy the endpoint in the same VPC/subnet as the targets, then allow inbound SSH on the instance security group from the endpoint’s security group. Access is governed by IAM—administrators must have Instance Connect permissions; while the example uses a broad policy, the key mechanism is EIC in the private subnet plus SG rules scoped to the endpoint. Systems Manager Session Manager can provide shell access without SSH, but the requirement explicitly states “connect through SSH,” making EIC the purpose-built solution. Options B and D misuse Systems Manager for SSH and propose unnecessary SG changes or incorrect endpoint placement; Option C places the endpoint in a public subnet, which is not required for private SSH access. Therefore, creating an EC2 Instance Connect endpoint in the private subnet and updating SGs accordingly meets the requirement while keeping the instance non-internet-exposed.

To analyze and visualize custom statistics such as the p90 latency (90th percentile), a CloudWatch metric must be generated from the log data. The correct method is to create a metric filter that extracts the latency value from each log event and publishes it as a CloudWatch metric. Once the metric is published, percentile statistics (p90, p95, etc.) can be displayed in CloudWatch dashboards or alarms.

AWS documentation states:

“You can use metric filters to extract numerical fields from log events and publish them as metrics in CloudWatch. CloudWatch supports percentile statistics such as p90 and p95 for these metrics.”

Contributor Insights (Option A) is for analyzing frequent contributors, not numeric distributions. Subscription filters (Option C) are used for log streaming, and Application Insights (Option D) provides monitoring of application health but not custom p90 statistics. Hence, Option B is the CloudOps-aligned, minimal-overhead solution for percentile latency monitoring.

According to the AWS Cloud Operations and Database Reliability documentation, Amazon RDS Multi-AZ deployments provide high availability and automatic failover by maintaining a synchronous standby replica in a different Availability Zone.

In the event of instance failure, planned maintenance, or Availability Zone outage, Amazon RDS automatically promotes the standby to primary with minimal downtime (typically less than 60 seconds). The failover is transparent to applications because the DB endpoint remains the same.

By contrast, read replicas (Option B) are asynchronous and do not provide automated failover. Auto Scaling (Option C) applies to EC2, not RDS. RDS Proxy (Option D) improves connection management but does not add redundancy.

Thus, Option A — converting the RDS instance into a Multi-AZ deployment — delivers the required high availability and business continuity with minimal operational effort.

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.

According to AWS Cloud Operations and Systems Manager documentation, Session Manager requires that each managed instance be associated with an IAM instance profile that grants Systems Manager core permissions. The required permissions are provided by the AmazonSSMManagedInstanceCore AWS-managed policy.

If this policy is missing or misconfigured, the Systems Manager Agent (SSM Agent) cannot communicate with the Systems Manager service, causing connection failures even if the agent is installed and running. This explains why other instances work—those instances likely have the correct IAM role attached.

Enabling port 22 (Option A) violates the company’s security policy, while configuring user names (Option C) and key pairs (Option D) are irrelevant because Session Manager operates over secure API channels, not SSH keys.

Therefore, the correct resolution is to attach or update the instance profile with the AmazonSSMManagedInstanceCore policy, restoring Session Manager connectivity.

According to CloudOps automation and monitoring best practices, CloudWatch dashboards should be provisioned as infrastructure-as-code (IaC) resources using AWS CloudFormation to ensure consistency, repeatability, and version control. AWS CloudFormation supports the AWS::CloudWatch::Dashboard resource, where the DashboardBody property accepts a JSON object describing widgets, metrics, and layout.

By exporting the existing dashboard configuration as JSON and embedding it into the CloudFormation template, every deployment of the application automatically creates its corresponding dashboard. This method aligns with the CloudOps requirement for automated deployment and operational visibility within the same stack lifecycle.

AWS documentation explicitly states:

“Use the AWS::CloudWatch::Dashboard resource to create a dashboard from your template. You can include the same JSON you use to define a dashboard in the console.”

Option A requires manual execution. Options C and D incorrectly reference or reuse existing dashboards, failing to produce unique, deployment-specific dashboards.

Comprehensive and Detailed Explanation From Exact Extract of AWS CloudOps Documents:

The requirement is to fix performance degradation from predictable peak CPU load without service disruptions. The most reliable and operationally standard approach is horizontal scaling with an Auto Scaling group driven by CloudWatch metrics/alarms (or target tracking). Launching additional instances and distributing traffic (typically behind a load balancer) increases capacity while keeping existing instances serving requests—no reboot or stop/start required.

Option D meets the requirement because Auto Scaling can add capacity when CPU exceeds a threshold and remove capacity when demand falls. This improves performance during peak periods and maintains availability. It is also operationally efficient: scaling actions are automated, consistent, and can be tuned with cooldowns/health checks.

Options A and C describe vertical scaling (instance resize). Resizing an EC2 instance type generally requires stopping the instance, changing the type, and starting it again—this is disruptive for a single-instance web server and often causes downtime. Option B (restarting the application) directly introduces disruption and does not address underlying capacity constraints; it can also worsen customer impact during peaks.

As per the AWS Cloud Operations and Content Delivery documentation, CloudFront determines cache behavior by evaluating both origin headers (e.g., Cache-Control and Expires) and distribution-level TTL settings.

When Cache-Control max-age conflicts with the Maximum TTL configured in CloudFront, the shorter TTL value takes precedence. This results in CloudFront caching content for only 5 minutes instead of 1 hour, despite the origin headers suggesting a longer duration.

AWS documentation explicitly states: “When both origin cache headers and CloudFront TTL settings are defined, CloudFront uses the most restrictive caching period.” This mismatch causes the perceived performance drop, as CloudFront frequently revalidates content.

Therefore, Option D is correct — cache-duration settings conflict with each other, leading to unexpected caching behavior.

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.

Among the available placement strategies:

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.

Comprehensive and Detailed Explanation From Exact Extract of AWS CloudOps Documents:

The correct answers are B and D. AWS CloudOps documentation clearly states that AWS Secrets Manager is the recommended service for storing and managing database credentials securely. Secrets Manager integrates natively with Amazon RDS and supports automatic, scheduled secret rotation.

To rotate credentials weekly, Secrets Manager requires a Lambda rotation function. AWS provides managed rotation templates for Amazon RDS for MySQL that update the database password and the stored secret atomically. This combination ensures credentials are protected, rotated automatically, and audited with minimal operational effort.

Option A is incorrect because RDS Proxy does not store or rotate credentials; it only retrieves them from Secrets Manager. Option C is incorrect because Systems Manager Parameter Store does not support native automatic rotation. Option E is incorrect because Automation runbooks are not the recommended mechanism for secrets rotation and add unnecessary complexity.

AWS CloudOps best practices strongly recommend Secrets Manager with Lambda-based rotation for database credential protection and compliance.