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NEW QUESTION # 152
A company runs a website on Amazon EC2 instances. Users can upload images to an Amazon S3 bucket and publish the images to the website. The company wants to deploy a serverless image-processing application that uses an AWS Lambda function to resize the uploaded images.
The company's development team has created the Lambda function. A CloudOps engineer must implement a solution to invoke the Lambda function when users upload new images to the S3 bucket.
Which solution will meet this requirement?
Answer: A
Explanation:
Use Amazon S3 Event Notifications with AWS Lambda to trigger image processing on object creation. S3 natively supports invoking Lambda for events such as s3:ObjectCreated:*, providing a serverless, low-latency pipeline without managing additional services. AWS operational guidance states that "Amazon S3 can directly invoke a Lambda function in response to object- created events," allowing you to pass event metadata (bucket/key) to the function for resizing and writing results back to S3. This approach minimizes operational overhead, scales automatically with upload volume, and integrates with standard retry semantics. SNS or SQS can be added for fan-out or buffering patterns, but they are not required when the requirement is simply "invoke the Lambda function on upload." CloudWatch alarms do not detect individual S3 object uploads and cannot directly satisfy per-object triggers. Therefore, configuring S3 Lambda event notifications meets the requirement most directly and aligns with CloudOps best practices for event-driven, serverless automation.
NEW QUESTION # 153
A SysOps administrator is configuring an Auto Scaling group of Amazon EC2 instances for an application.
The average CPU utilization of the instances in the Auto Scaling group must remain at approximately 40% when the load on the application changes.
Which solution will meet this requirement in the MOST operationally efficient manner?
Answer: B
Explanation:
Comprehensive and Detailed Explanation From Exact Extract of AWS CloudOps Documents:
The correct answer is D because target tracking scaling policies are designed to automatically maintain a specific metric at a desired target value with the least administrative effort. AWS CloudOps documentation states that target tracking works like a thermostat, continuously adjusting capacity to keep the selected metric close to the defined target.
By specifying a target value of 40% average CPU utilization, the Auto Scaling group automatically scales out or in based on real-time demand without requiring manual thresholds, multiple alarms, or predefined scaling steps. This approach is fully managed by AWS and is the most operationally efficient option.
Option A is incorrect because scheduled scaling is based on predictable traffic patterns and cannot respond dynamically to real-time load changes. Option B is incorrect because simple scaling reacts only after an alarm is triggered and does not continuously maintain a target value. Option C is less efficient because step scaling requires additional configuration and tuning of scaling steps.
AWS CloudOps best practices clearly recommend target tracking scaling policies for maintaining steady performance metrics with minimal operational overhead.
References:
Amazon EC2 Auto Scaling User Guide - Target Tracking Policies
AWS SysOps Administrator Study Guide - Auto Scaling and Automation
AWS Well-Architected Framework - Operational Excellence and Performance Efficiency
NEW QUESTION # 154
A company has a production application that runs on large compute optimized Amazon EC2 instances behind an Application Load Balancer (ALB). The instances are in an Amazon EC2 Auto Scaling group. The Auto Scaling group has a desired capacity of 2, a maximum capacity of 2, and a minimum capacity of 1.
The application is CPU-bound. The EC2 instances show consistent CPU utilization of 90% or greater during peak usage periods. These peak usage periods are unpredictable and cause performance issues and latency issues.
Which solution will automate the resolution of these issues?
Answer: D
Explanation:
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.
NEW QUESTION # 155
A company has a workload that is sending log data to Amazon CloudWatch Logs. One of the fields includes a measure of application latency. A CloudOps engineer needs to monitor the p90 statistic of this field over time.
What should the CloudOps engineer do to meet this requirement?
Answer: A
Explanation:
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.
References (AWS CloudOps Documents / Study Guide):
* AWS Certified CloudOps Engineer - Associate (SOA-C03) Exam Guide - Domain 1: Monitoring and Logging
* Amazon CloudWatch Logs - Metric Filters
* AWS Well-Architected Framework - Operational Excellence Pillar
NEW QUESTION # 156
A company is planning to host an application on a set of Amazon EC2 instances that are distributed across multiple Availability Zones. The application must be able to scale to millions of requests each second.
A CloudOps engineer must design a solution to distribute the traffic to the EC2 instances. The solution must be optimized to handle sudden and volatile traffic patterns while using a single static IP address for each Availability Zone.
Which solution will meet these requirements?
Answer: B
Explanation:
A Network Load Balancer (NLB) is optimized for high performance and ultra-low latency, capable of handling millions of requests per second. It supports static IP addresses per Availability Zone, making it ideal for applications requiring zonal IP stability and rapid scaling to handle sudden traffic surges. In contrast, Application Load Balancers don't support static IPs directly, and other options like SQS or Global Accelerator don't provide direct traffic distribution to EC2 instances.
NEW QUESTION # 157
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