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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Security | 26% | - Secure application data
|
| Topic 2: Troubleshooting and Optimization | 18% | - Troubleshoot application issues
|
| Topic 3: Deployment | 24% | - Implement CI/CD pipelines
|
| Topic 4: Development with AWS Services | 32% | - Interact with AWS data services
|
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NEW QUESTION # 694
A developer needs to migrate an online retail application to AWS to handle an anticipated increase in traffic.
The application currently runs on two servers: one server for the web application and another server for the database. The web server renders webpages and manages session state in memory. The database server hosts a MySQL database that contains order details. When traffic to the application is heavy, thememory usage for the web server approaches 100% and the application slows down considerably.
The developer has found that most of the memory increase and performance decrease is related to the load of managing additional user sessions. For the web server migration, the developer will use Amazon EC2 instances with an Auto Scaling group behind an Application Load Balancer.
Which additional set of changes should the developer make to the application to improve the application's performance?
Answer: C
Explanation:
Using Amazon ElastiCache for Memcached to store and manage the session data will reduce the memory load and improve the performance of the web server. Using Amazon RDS for MySQL DB instance to store the application data will provide a scalable, reliable, and managed database service. Option A is not optimal because it does not address the memory issue of the web server. Option C is not optimal because it does not provide a persistent storage for the application data. Option D is not optimal because it does not provide a high availability and durability for the session data.
NEW QUESTION # 695
A company uses a custom root certificate authority certificate chain (Root CA Cert) that is 10 KB in size generate SSL certificates for its on-premises HTTPS endpoints. One of the company's cloud based applications has hundreds of AWS Lambda functions that pull date from these endpoints. A developer updated the trust store of the Lambda execution environment to use the Root CA Cert when the Lambda execution environment is initialized. The developer bundled the Root CA Cert as a text file in the Lambdas deployment bundle.
After 3 months of development the root CA Cert is no longer valid and must be updated. The developer needs a more efficient solution to update the Root CA Cert for all deployed Lambda functions. The solution must not include rebuilding or updating all Lambda functions that use the Root CA Cert. The solution must also work for alldevelopment, testing and production environment. Each environment is managed in a separate AWS account.
When combination of steps Would the developer take to meet these environments MOST cost-effectively?
(Select TWO)
Answer: A,E
Explanation:
This solution will meet the requirements by storing the Root CA Cert as a Secure String parameter in AWS Systems Manager Parameter Store, which is a secure and scalable service for storing and managing configuration data and secrets. The resource-based policy will allow IAM users in different AWS accounts and environments to access the parameter without requiring cross-account roles or permissions. The Lambda code will be refactored to load the Root CA Cert from the parameter store and modify the runtime trust store outside the Lambda function handler, which will improve performance and reduce latency by avoiding repeated calls to Parameter Store and trust store modifications for each invocation of the Lambda function.
Option A is not optimal because it will use AWS Secrets Manager instead of AWS Systems Manager Parameter Store, which will incur additional costs and complexity for storing and managing a non-secret configuration data such as Root CA Cert. Option C is not optimal because it will deactivate the application secrets and monitor the application error logs temporarily, which will cause application downtime and potential data loss. Option D is not optimal because it will modify the runtime trust store inside the Lambda function handler, which will degrade performance and increase latency by repeating unnecessary operations for each invocation of the Lambda function.
NEW QUESTION # 696
A developer is designing a serverless application that customers use to select seats for a concert venue. Customers send the ticket requests to an Amazon API Gateway API with an AWS Lambda function that acknowledges the order and generates an order ID. The application includes two additional Lambda functions one for inventory management and one for payment processing. These two Lambda functions run in parallel and write the order to an Amazon DynamoDB table.
The application must provide seats to customers according to the following requirements. If a seat is accidently sold more than once, the first order that the application received must get the seat. In these cases, the application must process the payment for only the first order. However, if the first order is rejected during payment processing, the second order must get the seat. In these cases, the application must process the payment for the second order.
Which solution will meet these requirements?
Answer: B
Explanation:
Invoking inventory management before payment processing creates the required sequential workflow. The inventory function can conditionally reserve the seat for the earliest order, and payment is processed only for the order that successfully obtains the reservation. If payment fails, the reservation can be released so that the next order can acquire the seat and proceed to payment.
NEW QUESTION # 697
A development team uses an Amazon DynamoDB table as a database for an application. The team notices errors and slowdowns in the application during peak usage hours. The slowdowns and errors occur during a surge of user logins. The application receives frequent write requests. Application logs indicate that write requests are being throttled.
The development team needs to reduce the application latency and resolve the throttling errors.
Which solutions will meet these requirements? (Select TWO.)
Answer: A,E
Explanation:
The issue is write throttling during peak login surges. In DynamoDB provisioned capacity mode, throttling happens when the workload exceeds the table's configured write capacity units (WCU) (or a partition becomes "hot"). To resolve throttling and reduce latency, the table must have sufficient write capacity available when the surge occurs.
Option B directly addresses this by increasing the table's provisioned throughput (WCU). With more write capacity, DynamoDB can accept more writes per second without returning throttling errors, which reduces retries and improves end-user latency during peak usage.
Option E is also effective: switching the table to on-demand capacity mode automatically accommodates traffic spikes without the team needing to forecast and pre-provision capacity. On-demand is designed for unpredictable workloads and can scale to handle sudden surges, reducing the risk of throttling caused by under-provisioning. For many teams, this is the fastest operational fix because it removes manual capacity planning and reactive scaling steps.
Why the other options are not the best fit:
* A (DAX) improves read performance and reduces read latency, but it does not fix write throttling.
* C (retries and exponential backoff) is a best practice for handling throttling gracefully, but it does not
"resolve" throttling; it reduces contention and improves success rates at the cost of higher latency, and it does not increase available capacity.
* D focuses on control plane operations (schema/index/table management) which are unrelated to data plane write throttling during login surges.
Therefore, the two solutions that meet the requirements to resolve write throttling and reduce latency are B (increase provisioned throughput) and E (switch to on-demand capacity mode for automatic scaling).
NEW QUESTION # 698
A company built an online event platform For each event the company organizes quizzes and generates leaderboards that are based on the quiz scores. The company stores the leaderboard data in Amazon DynamoDB and retains the data for 30 days after an event is complete The company then uses a scheduled job to delete the old leaderboard data The DynamoDB table is configured with a fixed write capacity. During the months when many events occur, the DynamoDB write API requests are throttled when the scheduled delete job runs.
A developer must create a long-term solution that deletes the old leaderboard data and optimizes write throughput Which solution meets these requirements?
Answer: D
Explanation:
DynamoDB TTL (Time-to-Live): A native feature that automatically deletes items after a specified expiration time.
Efficiency: Eliminates the need for scheduled deletion jobs, optimizing write throughput by avoiding potential throttling conflicts.
Seamless Integration: TTL works directly within DynamoDB, requiring minimal development overhead.
Reference:
DynamoDB TTL Documentation: https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/TTL.html
NEW QUESTION # 699
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