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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Deployment | 24% | - CI/CD and deployment strategies
|
| Topic 2: Development with AWS Services | 32% | - AWS core services development
|
| Topic 3: Troubleshooting and Optimization | 18% | - Monitoring and logging
|
| Topic 4: Security | 26% | - Data security
|
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NEW QUESTION # 711
A developer manages an application that stores user objects in an Amazon S3 bucket without versioning enabled. The application has premium users and basic users.
After premium users upload objects, the premium users have unlimited downloads of their objects. Their objects are stored with a premium/ prefix. After basic users upload objects, the basic users can download their objects for 90 days. Their objects are stored with a basic/ prefix.
The developer needs to implement a solution to automatically delete objects for the basic users after 90 days.
Which solution will meet these requirements with the LEAST development effort?
Answer: B
Explanation:
The requirement is to automatically delete objects for basic users after 90 days with the least development effort. Amazon S3 provides a built-in, fully managed feature for this: S3 Lifecycle configuration rules .
Lifecycle rules can filter objects by prefix (and/or tags) and then perform actions such as expiring (deleting) objects after a specified number of days since creation. Because the bucket does not have versioning enabled, the relevant action is to expire the current object after the retention period.
Option C matches this exactly: create an S3 Lifecycle rule that applies only to objects under the basic/ prefix and configure the rule to expire current versions after 90 days . S3 then automatically and continuously enforces the policy without any code, scheduling, or operational maintenance. This is typically the lowest- effort and most reliable approach because it is handled by S3 itself.
Option A requires writing and operating a Lambda function plus a scheduler, handling pagination, permissions, error handling, retries, and potential costs and throttling when scanning large buckets. That is more development and operational effort than a lifecycle rule. Option B is incorrect because it targets the premium/ prefix, but premium objects must be retained indefinitely. Option D refers to delete markers and unfinished multipart uploads; delete markers are relevant primarily for versioned buckets , and cleaning up multipart uploads does not implement the required "delete basic objects after 90 days" behavior.
Therefore, the correct solution is C : use an S3 Lifecycle rule scoped to the basic/ prefix to expire objects after 90 days, achieving automated retention with minimal development effort.
NEW QUESTION # 712
A developer is writing an application for a company. The application will be deployed on Amazon EC2 and will use an Amazon RDS for Microsoft SQL Server database. The company's security team requires that database credentials are rotated at least weekly.
How should the developer configure the database credentials for this application?
Answer: C
Explanation:
The requirement is to rotate database credentials at least weekly. The AWS service that is purpose-built for storing and automatically rotating secrets is AWS Secrets Manager. Secrets Manager supports rotation schedules (including daily, weekly, or custom intervals) and integrates with supported databases through rotation Lambda functions. With rotation enabled, Secrets Manager can periodically generate a new password, update the secret value, and apply the new credential to the database user, while allowing applications to retrieve the current credential securely at runtime.
Option C matches this best practice: create a database user, store the username/password in a Secrets Manager secret, and enable rotation (daily rotation more than satisfies a weekly requirement). Applications on EC2 can retrieve the secret at startup or on a refresh interval using the AWS SDK, and can cache it according to best practices to reduce API calls, while still supporting rotation.
Option A (Parameter Store secure string) encrypts parameters, but Parameter Store does not provide the same managed secret rotation workflow for database credentials; rotating the KMS key is not the same as rotating the database password. Option B is not applicable as stated for RDS for Microsoft SQL Server: IAM database authentication is supported for specific engines (commonly MySQL and PostgreSQL) and is not the standard mechanism for SQL Server password rotation. Option D is insecure and operationally fragile: embedding long-lived credentials in user data and environment variables makes rotation difficult and increases exposure risk.
Therefore, the most secure and compliant configuration is C: store DB credentials in AWS Secrets Manager and enable automated rotation on an interval that meets or exceeds the weekly rotation requirement.
NEW QUESTION # 713
A developer is receiving an intermittent ProvisionedThroughputExceededException error from an application that is based on Amazon DynamoDB. According to the Amazon CloudWatch metrics for the table, the application is not exceeding the provisioned throughput. What could be the cause of the issue?
Answer: A
Explanation:
DynamoDB distributes throughput across partitions based on the hash key. A hot partition (caused by high usage of a specific hash key) can result in a ProvisionedThroughputExceededException, even if overall usage is below the provisioned capacity.
* Why Option B is Correct:
* Partition-Level Limits: Each partition has a limit of 3,000 read capacity units or 1,000 write capacity units per second.
* Hot Partition: Excessive use of a single hash key can overwhelm its partition.
* Why Not Other Options:
* Option A: DynamoDB storage size does not affect throughput.
* Option C: Provisioned scaling operations are unrelated to throughput errors.
* Option D: Sort keys do not impact partition-level throughput.
DynamoDB Partition Key Design Best Practices
NEW QUESTION # 714
A developer used the AWS SDK to create an application that aggregates and produces log records for 10 services. The application delivers data to an Amazon Kinesis Data Streams stream.
Each record contains a log message with a service name, creation timestamp, and other log information. The stream has 15 shards in provisioned capacity mode. The stream uses service name as the partition key.
The developer notices that when all the services are producing logs,
ProvisionedThroughputExceededException errors occur during PutRecord requests. The stream metrics show that the write capacity the applications use is below the provisioned capacity.
Answer: A
Explanation:
Comprehensive and Detailed Step-by-Step Explanation:
* Issue Analysis:
* The stream uses service name as the partition key. This can cause "hot partition" issues when a few service names generate significantly more logs compared to others, causing uneven distribution of data across shards.
* Metrics show that the write capacity used is below provisioned capacity, which confirms that the throughput errors are due to shard-level limits and not overall capacity.
* Option C: Change Partition Key to Creation Timestamp:
* By changing the partition key to the creation timestamp (or a composite key including timestamp), the distribution of data across shards can be randomized, ensuring an even spread of records.
* This resolves the shard overutilization issue and eliminates
ProvisionedThroughputExceededException.
* Why Other Options Are Incorrect:
* Option A: Switching to on-demand capacity mode might temporarily alleviate the issue, but the root cause (hot partitioning) remains unresolved.
* Option B: Adding shards increases capacity but does not fix the skewed data distribution caused by using the service name as the partition key.
* Option D: Creating separate streams for each service adds unnecessary complexity and does not scale well as the number of services grows.
References:
Best Practices for Kinesis Data Streams Partition Key Design
NEW QUESTION # 715
A company developed an API application on AWS by using Amazon CloudFront. Amazon API Gateway, and AWS Lambda. The API has a minimum of four requests every second A developer notices that many API users run the same query by using the POST method. The developer wants to cache the POST request to optimize the API resources.
Which solution will meet these requirements'?
Answer: C
Explanation:
Explanation
This solution will meet the requirements by using Amazon CloudFront, which is a content delivery network (CDN) service that speeds up the delivery of web content and APIs to end users. The developer can configure the CloudFront cache, which is a set of edge locations that store copies of popular or recently accessed content close to the viewers. The developer can also update the application to return cached content based upon the default request headers, which are a set of HTTP headers that CloudFront automatically forwards to the origin server and uses to determine whether an object in an edge location is still valid. By caching the POST requests, the developer can optimize the API resources and reduce the latency for repeated queries. Option B is not optimal because it will override the cache method in the selected stage of API Gateway, which is not possible or effective as API Gateway does not support caching for POST methods by default. Option C is not optimal because it will save the latest request response in Lambda /tmp directory, which is a local storage space that is available for each Lambda function invocation, not a cache that can be shared across multiple invocations or requests. Option D is not optimal because it will save the latest request in AWS Systems Manager Parameter Store, which is a service that provides secure and scalable storage for configuration data and secrets, not a cache for API responses.
References: [Amazon CloudFront], [Caching Content Based on Request Headers]
NEW QUESTION # 716
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