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Amazon AWS Certified CloudOps Engineer - Associate Sample Questions (Q46-Q51):
NEW QUESTION # 46
A company has an internal web application that runs on Amazon EC2 instances behind an Application Load Balancer. The instances run in an Amazon EC2 Auto Scaling group in a single Availability Zone. A CloudOps engineer must make the application highly available.
Which action should the CloudOps engineer take to meet this requirement?
- A. Update the Auto Scaling group to launch new instances in an Availability Zone in a second AWS Region.
- B. Increase the maximum number of instances in the Auto Scaling group to meet the capacity that is required at peak usage.
- C. Update the Auto Scaling group to launch new instances in a second Availability Zone in the same AWS Region.
- D. Increase the minimum number of instances in the Auto Scaling group to meet the capacity that is required at peak usage.
Answer: C
Explanation:
High availability within AWS is achieved by distributing resources across multiple Availability Zones in the same Region. By updating the Auto Scaling group to span at least two Availability Zones, the application can continue serving traffic even if one zone becomes unavailable. This configuration works seamlessly with the Application Load Balancer, which automatically routes traffic only to healthy instances across those zones.
NEW QUESTION # 47
An application uses an Amazon Aurora MySQL DB cluster that includes one Aurora Replica. The application' s read performance degrades when there are more than 200 user connections. The number of user connections is approximately 180 on a consistent basis. Occasionally, the number of user connections increases rapidly to more than 200.
A CloudOps engineer must implement a solution that will scale the application automatically as user demand increases or decreases.
Which solution will meet these requirements?
- A. Modify the DB cluster by increasing the Aurora Replica instance size.
- B. Modify the DB cluster by changing to serverless mode whenever the number of user connections exceeds 200.
- C. Create an auto scaling policy that has a target value of 195 for the DatabaseConnections metric.
- D. Migrate to a new Aurora DB cluster that has multiple writer instances. Modify the application's database connection string.
Answer: C
Explanation:
Aurora Auto Scaling can automatically add or remove Aurora Replicas based on target metrics, including average connections across Aurora Replicas. AWS documentation lists "Average connections of Aurora Replicas" as a target metric when adding an auto scaling policy to an Aurora DB cluster. Because performance degrades above 200 connections and baseline usage is around 180, setting a target near 195 allows scaling to begin before the connection count crosses the problem threshold. Option A is vertical scaling and does not automatically respond to fluctuating demand. Option B is not how Aurora switches between provisioned and serverless modes during spikes. Option C is unnecessary and does not address read scaling with replicas. Therefore, an Aurora auto scaling policy using the database connections metric is correct.
NEW QUESTION # 48
A financial services company stores customer images in an Amazon S3 bucket in the us-east-1 Region. To comply with regulations, the company must ensure that all existing objects are replicated to an S3 bucket in a second AWS Region. If an object replication fails, the company must be able to retry replication for the object.
What solution will meet these requirements?
- A. Configure Amazon S3 Cross-Region Replication (CRR). Use S3 Replication Time Control (S3 RTC) to replicate existing objects.
- B. Configure Amazon S3 Cross-Region Replication (CRR). Use S3 Batch Replication to replicate existing objects.
- C. Configure Amazon S3 Cross-Region Replication (CRR). Use Amazon S3 live replication to replicate existing objects.
- D. Use S3 Lifecycle rules to move objects to the destination bucket in a second Region.
Answer: B
Explanation:
Per the AWS Cloud Operations and S3 Data Management documentation, Cross-Region Replication (CRR) automatically replicates new objects between S3 buckets across Regions.
However, CRR alone does not retroactively replicate existing objects created before replication configuration. To include such objects, AWS introduced S3 Batch Replication.
S3 Batch Replication scans the source bucket and replicates all existing objects that were not copied previously. Additionally, it can retry failed replication tasks automatically, ensuring regulatory compliance for complete dataset replication.
S3 Replication Time Control (S3 RTC) guarantees predictable replication times for new objects only-- it does not cover previously stored data. S3 Lifecycle rules (Option D) move or transition objects between storage classes or buckets, but not in a replication context.
Therefore, the correct solution is to use S3 Cross-Region Replication (CRR) combined with S3 Batch Replication to ensure all current and future data is synchronized across Regions with retry capability.
NEW QUESTION # 49
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?
- A. Define and use a new custom Amazon CloudWatch metric based on the SQS ApproximateNumberOfMessagesDelayed metric in the target tracking policy.
- B. Define and use step scaling by specifying a ChangeInCapacity value for the EC2 instances.
- C. Define and use Amazon CloudWatch metric math to calculate the SQS queue backlog for each instance in the target tracking policy.
- D. Define and use simple scaling by specifying a ChangeInCapacity value for the EC2 instances.
Answer: C
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
NEW QUESTION # 50
A company has a stateful web application that is hosted on Amazon EC2 instances in an Auto Scaling group.
The instances run behind an Application Load Balancer (ALB) that has a single target group. The ALB is configured as the origin in an Amazon CloudFront distribution. Users are reporting random logouts from the web application.
Which combination of actions should a CloudOps engineer take to resolve this problem? (Select TWO.)
- A. Configure header forwarding in the CloudFront distribution cache behavior.
- B. Configure cookie forwarding in the CloudFront distribution cache behavior.
- C. Enable group-level stickiness on the ALB listener rule.
- D. Enable sticky sessions on the ALB target group.
- E. Change to the least outstanding requests algorithm on the ALB target group.
Answer: B,D
Explanation:
Stateful applications require session persistence to ensure that subsequent requests from the same user are routed to the same backend instance. When CloudFront is used in front of an ALB, session-related cookies must be forwarded correctly; otherwise, CloudFront can route requests to different targets, causing session loss and random logouts.
Configuring cookie forwarding in the CloudFront cache behavior ensures that session cookies (such as authentication tokens) are forwarded to the ALB and not stripped or cached incorrectly. Without this configuration, CloudFront may serve cached responses that do not align with the user's active session state, leading to authentication issues.
On the ALB side, sticky sessions (session affinity) must be enabled on the target group to ensure that requests with the same session cookie are consistently routed to the same EC2 instance. ALB stickiness uses application cookies to bind a user session to a specific target, which is critical for stateful applications that store session data in memory.
Option A affects load distribution efficiency but does not address session persistence. Option C (header forwarding) is unnecessary unless the application explicitly stores session state in headers, which is uncommon. Option D applies only when using multiple target groups and listener rules, which is not the case here.
Together, enabling cookie forwarding in CloudFront and sticky sessions at the ALB target group resolves the logout issue by maintaining consistent session routing from the user through CloudFront to the same backend instance.
NEW QUESTION # 51
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