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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Reliability and Business Continuity | 22% | - Design and implement high availability and fault tolerance
|
| Topic 2: Security and Compliance | 18% | - Implement identity and access management
|
| Topic 3: Monitoring, Logging, Analysis, Remediation, and Performance Optimization | 22% | - Collect and interpret metrics, logs, and events
|
| Topic 4: Deployment, Provisioning, and Automation | 20% | - Automate operational tasks and workflows
|
| Topic 5: Networking and Content Delivery | 18% | - Configure and maintain AWS networking services
|
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NEW QUESTION # 301
A company has an application that collects notifications from thousands of alarm systems. Notifications include alarm notifications and information notifications. All notifications are stored in an Amazon Simple Queue Service (Amazon SQS) queue. Amazon EC2 instances in an Auto Scaling group process the messages.
A CloudOps engineer needs to prioritize alarm notifications over information notifications.
Which solution will meet these requirements?
Answer: D
Explanation:
Amazon SQS does not support message prioritization within a single queue. To prioritize certain messages, AWS recommends using multiple queues and processing them in priority order.
By separating alarm notifications into one queue and information notifications into another, the application can poll and process the alarm queue first. This ensures that critical alarm messages are handled immediately, even during high load.
Scaling faster or using SNS fanout does not guarantee priority. DynamoDB streams are unrelated to SQS message ordering.
Therefore, separate queues provide a simple, scalable, and effective prioritization mechanism.
NEW QUESTION # 302
A CloudOps engineer creates a new VPC that includes a public subnet and a private subnet. The CloudOps engineer successfully launches 11 Amazon EC2 instances in the private subnet The CloudOps engineer attempts to launch one more EC2 instance in the same subnet. However, the CloudOps engineer receives an error message that states that not enough free IP addresses are available.
What must the CloudOps engineer do to deploy more EC2 instances?
Answer: A
Explanation:
A subnet's CIDR block size determines how many IP addresses it can allocate to resources.
Once all IP addresses in the subnet are used, you cannot expand or resize the CIDR block of an existing subnet. The only solution is to create a new subnet (with a larger or different CIDR range) within the same VPC and launch additional EC2 instances there. This allows scaling without IP address exhaustion.
NEW QUESTION # 303
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?
Answer: 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
NEW QUESTION # 304
A logistics company wants to run containerized applications on Amazon ECS behind an Application Load Balancer. The company wants to use a phased release method to test new application versions and gradually increase traffic shift. The company wants to start with 10% of the traffic to the new version, with 10% increments every 3 minutes until the traffic is fully shifted.
Which deployment strategy will meet these requirements?
Answer: A
Explanation:
The traffic pattern described is a linear deployment: shift a fixed percentage of traffic at fixed time intervals until the new version receives 100% of traffic. AWS CodeDeploy for Amazon ECS includes a predefined deployment configuration named CodeDeployDefault.ECSLinear10PercentEvery3Minutes, which shifts 10% of traffic every 3 minutes until all traffic is shifted. A canary deployment sends an initial small percentage to the new version and then shifts the remaining traffic after a bake period, not in equal repeated increments. A rolling deployment replaces tasks in batches but does not express ALB traffic shifting percentages in this way. Blue/green is the broader deployment model used by CodeDeploy, but the specific release strategy requested is linear. Therefore, option D is correct.
NEW QUESTION # 305
An ecommerce company uses Amazon ElastiCache (Redis OSS) for caching product queries. The CloudOps engineer observes a large number of cache evictions in Amazon CloudWatch metrics and needs to reduce evictions while retaining popular data in cache.
Which solution meets these requirements with the least operational overhead?
Answer: B
Explanation:
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.
NEW QUESTION # 306
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