Free AWS Certified CloudOps Engineer - Associate SOA-C03 Exam braindumps download (AWS Certified CloudOps Engineer - Associate SOA-C03 exam dumps Free)

QUESTION: 61

Optimization]

A company uses an Amazon Simple Queue Service (Amazon SQS) queue and Amazon EC2 instances in an Auto Scaling group with target tracking for a web application. The company collects the ASGAverageNetworkIn metric but notices that instances do not scale fast enough during peak traffic. There are a large number of SQS messages accumulating in the queue.

A CloudOps engineer must reduce the number of SQS messages during peak periods.

Which solution will meet this requirement?

Define and use a new custom Amazon CloudWatch metric based on the SQS ApproximateNumberOfMessagesDelayed metric in the target tracking policy.
Define and use Amazon CloudWatch metric math to calculate the SQS queue backlog for each instance in the target tracking policy.
Define and use step scaling by specifying a ChangeInCapacity value for the EC2 instances.
Define and use simple scaling by specifying a ChangeInCapacity value for the EC2 instances.

Answer(s): B

Explanation:

According to the AWS Cloud Operations and Auto Scaling documentation, scaling applications that consume Amazon SQS messages should be driven by queue backlog per instance, not by general system metrics such as network traffic or CPU.

The correct approach is to calculate a custom metric using CloudWatch metric math that divides the

SQS metric ApproximateNumberOfMessagesVisible by the number of active EC2 instances in the Auto Scaling group. This "backlog per instance" value represents the average number of messages waiting to be processed by each instance.

Then, the CloudOps engineer can create a target tracking policy that automatically scales out or in based on maintaining a desired backlog threshold. This approach ensures dynamic, workload-driven scaling behavior that reacts in near real time to message volume.

Step and simple scaling (Options C and D) require manual thresholds and do not automatically balance the load per instance.

Thus, Option B--using CloudWatch metric math to define queue backlog per instance for target tracking--is the most effective and AWS-recommended CloudOps practice.

Reference:

AWS Cloud Operations & Scaling Guide Scaling Based on Amazon SQS Queue Backlog Per Instance

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QUESTION: 62

A CloudOps engineer has created an AWS Service Catalog portfolio and shared it with a second AWS account in the company, managed by a different CloudOps engineer.

Which action can the CloudOps engineer in the second account perform?

Add a product from the imported portfolio to a local portfolio.
Add new products to the imported portfolio.
Change the launch role for the products contained in the imported portfolio.
Customize the products in the imported portfolio.

Answer(s): A

Explanation:

Per the AWS Cloud Operations and Service Catalog documentation, when a portfolio is shared across AWS accounts, the recipient account imports the shared portfolio.

The recipient CloudOps engineer cannot modify the original products or their configurations but can:

Add products from the imported portfolio into their local portfolios for deployment,

Control end-user access in the recipient account, and

Manage local constraints or permissions.

However, the recipient cannot edit, delete, or reconfigure the shared products (Options B, C, and D). The source (owner) account retains full administrative control over products, launch roles, and lifecycle policies.

This model aligns with AWS CloudOps principles of centralized governance with distributed self- service deployment across multiple accounts.

Thus, Option A is correct--imported portfolios allow the recipient to add products to a local portfolio but not alter the shared configuration.

Reference:

AWS Cloud Operations & Governance Guide Managing Shared AWS Service Catalog Portfolios Across Multiple Accounts

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QUESTION: 63

A company is migrating a legacy application to AWS. The application runs on EC2 instances across multiple Availability Zones behind an Application Load Balancer (ALB). The target group routing algorithm is set to weighted random, and the application requires session affinity (sticky sessions).

After deployment, users report random application errors that were not present before migration, even though target health checks are passing.

Which solution will meet this requirement?

Set the routing algorithm of the target group to least outstanding requests.
Turn on anomaly mitigation for the target group.
Turn off the cross-zone load balancing attribute of the target group.
Increase the deregistration delay attribute of the target group.

Answer(s): A

Explanation:

According to the AWS Cloud Operations and Elastic Load Balancing documentation, Application Load Balancer (ALB) supports multiple routing algorithms to distribute requests among targets:

Round robin (default)

Least outstanding requests (LOR)

Weighted random

When applications require session affinity, AWS recommends using "least outstanding requests" as the load balancing algorithm because it reduces latency, distributes load evenly, and ensures consistent target responsiveness during high traffic.

Using weighted random routing with sticky sessions can cause sessions to be routed inconsistently if one target's capacity fluctuates, leading to session mismatches and application errors -- especially when user sessions rely on instance-specific state.

Disabling cross-zone balancing (Option C) or adjusting deregistration delay (Option D) does not address routing inconsistency. Anomaly mitigation (Option B) protects against target performance degradation, not sticky-session misrouting.

Therefore, the correct solution is Option A -- changing the target group's routing algorithm to least outstanding requests ensures smoother, predictable session handling and resolves random application errors.

Reference:

AWS Cloud Operations & Load Balancing Guide Optimizing Application Load Balancer Routing Algorithms for Stateful Applications

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QUESTION: 64

A CloudOps engineer must manage the security of an AWS account. Recently, an IAM user's access key was mistakenly uploaded to a public code repository. The engineer must identify everything that was changed using this compromised key.

How should the CloudOps engineer meet these requirements?

Create an Amazon EventBridge rule to send all IAM events to an AWS Lambda function for analysis.
Query Amazon EC2 logs by using Amazon CloudWatch Logs Insights for all events initiated with the compromised access key within the suspected timeframe.
Search AWS CloudTrail event history for all events initiated with the compromised access key within the suspected timeframe.
Search VPC Flow Logs for all events initiated with the compromised access key within the suspected timeframe.

Answer(s): C

Explanation:

According to the AWS Cloud Operations and Security documentation, AWS CloudTrail is the authoritative service for recording API activity across all AWS services within an account.

When an access key is compromised, CloudTrail logs all API requests made using that key, including details such as:

The user identity (access key ID) that made the request,

The service, operation, resource, and timestamp affected, and

The source IP address and region of the request.

By searching the CloudTrail event history for the specific access key ID, the CloudOps engineer can identify every action performed by that key during the suspected breach window.

Other options are incorrect:

EventBridge (A) is event-driven, not historical.

CloudWatch Logs (B) monitors system logs, not AWS API activity.

VPC Flow Logs (D) track network-level traffic, not API calls.

Therefore, the correct solution is Option C -- using AWS CloudTrail event history to audit and trace all actions executed via the compromised access key.

Reference:

AWS Cloud Operations & Security Management Guide Investigating Compromised Access Keys Using AWS CloudTrail

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QUESTION: 65

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?

Place all the EC2 instances into a cluster placement group.
Configure and assign two Elastic IP addresses for each EC2 instance.
Configure jumbo frames on all the EC2 instances in the cluster.
Place all the EC2 instances into a spread placement group in the same AWS Region.

Answer(s): A

Explanation:

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.

Reference:

AWS Cloud Operations & Compute Optimization Guide Optimizing EC2 Networking with Cluster Placement Groups for Low Latency Workloads

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Free AWS Certified CloudOps Engineer - Associate SOA-C03 Exam Braindumps AWS Certified CloudOps Engineer - Associate Questions 61–65 (page: 14)

QUESTION: 61

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QUESTION: 62

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QUESTION: 63

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QUESTION: 64

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QUESTION: 65

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Free AWS Certified CloudOps Engineer - Associate SOA-C03 Exam Braindumps
AWS Certified CloudOps Engineer - Associate Questions 61–65 (page: 14)