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NEW QUESTION # 101
A company hosts its application on AWS. The application runs on an Amazon Elastic Container Service (Amazon ECS) cluster that uses AWS Fargate. The cluster runs behind an Application Load Balancer The application stores data in an Amazon Aurora database A developer encrypts and manages database credentials inside the application The company wants to use a more secure credential storage method and implement periodic credential rotation.
Which solution will meet these requirements with the LEAST operational overhead?
Answer: A
Explanation:
* Secrets Management: AWS Secrets Manager is designed specifically for storing and managing sensitive credentials.
* Built-in Rotation: Secrets Manager provides automatic secret rotation functionality, enhancing security posture significantly.
* IAM Integration: IAM policies and roles grant fine-grained access to ECS Fargate, ensuring the principle of least privilege.
* Reduced Overhead: This solution centralizes secrets management and automates rotation, reducing operational overhead compared to the other options.
References:
AWS Secrets Manager: https://aws.amazon.com/secrets-manager/
Secrets Manager Rotation: https://docs.aws.amazon.com/secretsmanager/latest/userguide/rotating-secrets.html IAM for Secrets Manager: https://docs.aws.amazon.com/secretsmanager/latest/userguide/auth-and- access_iam-policies.html
NEW QUESTION # 102
A company uses the AWS SDK for JavaScript in the Browser to build a web application and then hosts the application on Amazon S3. The company wants the application to support 10,000 users concurrently. The company selects Amazon DynamoDB to store user preferences in a table.
There is a requirement to uniquely identify users at any scale.
Which solution will meet these requirements?
Answer: C
Explanation:
This will allow the application to support 10,000 users concurrently and will provide a unique identifier for each user. By using Amazon Cognito, the company can authenticate users and then access DynamoDB with the authenticated users to store their preferences in a table. This approach will allow the company to control access to the DynamoDB table and to scale to any number of users. Creating a user cookie or deploying an Amazon CloudFront distribution with an OAI would not solve the problem because these solutions do not provide a way to uniquely identify users or control access to DynamoDB. Creating an IAM user for each user and using fine- grained access control on the DynamoDB table would not be practical or scalable because it would require the company to manage and maintain a large number of IAM users.
When dealing with user profiles in serverless applications we often turn to Cognito for managing their credentials while the app itself will store user entities.
https://www.sorenandersen.com/manage-user-profile-data-between-cognito-and-dynamodb/
NEW QUESTION # 103
A company is expanding the compatibility of its photo-snaring mobile app to hundreds of additional devices with unique screen dimensions and resolutions. Photos are stored in Amazon S3 in their original format and resolution. The company uses an Amazon CloudFront distribution to serve the photos The app includes the dimension and resolution of the display as GET parameters with every request.
A developer needs to implement a solution that optimizes the photos that are served to each device to reduce load time and increase photo quality.
Which solution will meet these requirements MOST cost-effective?
Answer: B
Explanation:
This solution meets the requirements most cost-effectively because it optimizes the photos on demand and caches them for future requests. Lambda@Edge allows the developer to run Lambda functions at AWS locations closer to viewers, which can reduce latency and improve photo quality. The developer can create a Lambda@Edge function that uses the GET parameters from each request to optimize the photos with the required dimensions and resolutions and returns them as a response. The function can also store a copy of the processed photos on Amazon S3 for subsequent requests, which can reduce processing time and costs. Using S3 Batch Operations to create new variants of the photos will incur additional storage costs and may not cover all possible dimensions and resolutions. Creating a dynamic CloudFront origin or a Lambda@Edge function to route requests to corresponding photo variants will require maintaining a mapping of device types and photo variants, which can be complex and error-prone.
NEW QUESTION # 104
A developer is building an application that processes a stream of user-supplied data. The data stream must be consumed by multiple Amazon EC2-based processing applications in parallel and in real time. Each processor must be able to resume without losing data if there is a service interruption. The application architect plans to add other processors in the near future and wants to minimize the amount of data duplication involved.
Which solution will satisfy these requirements?
Answer: D
Explanation:
The requirements describe a real-time streaming use case where multiple processing applications must read the same stream in parallel, each must be able to resume processing after interruptions without data loss, and the system should support adding new processors with minimal duplication. Amazon Kinesis Data Streams (KDS) is purpose-built for this pattern.
With Kinesis Data Streams, producers write records to a stream that is split into shards. Multiple consumer applications can independently read from the stream using separate consumer groups (for example, using Kinesis Client Library). Each consumer maintains its own checkpoint (sequence number position), which allows it to resume from the last processed record after a restart or failure. This directly meets the "resume without losing data" requirement.
KDS also supports a configurable retention period (commonly 24 hours by default, extendable), enabling replay and recovery without forcing producers to re-send data or duplicating messages for each processor.
Adding new consumers is straightforward: a new processor can begin reading the existing stream (from latest or from a trim horizon), which minimizes duplication compared to fan-out duplication patterns.
Why the others don't fit:
* SQS is a queue, not a stream. Multiple consumers typically compete for messages rather than all consuming the same messages independently, and duplication is required if multiple apps must process the same data.
* Kinesis Data Firehose is primarily for delivery to destinations (S3, Redshift, OpenSearch) and not designed for multiple real-time parallel consumers with replay semantics.
* EventBridge is event routing with limited replay/resume semantics compared to KDS and is not intended for high-throughput stream processing with per-consumer checkpoints.
Therefore, Amazon Kinesis Data Streams satisfies all requirements.
NEW QUESTION # 105
A developer received the following error message during an AWS CloudFormation deployment:
Which action should the developer take to resolve this error?
Answer: B
NEW QUESTION # 106
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P.S. Free 2026 Amazon DVA-C02 dumps are available on Google Drive shared by TestInsides: https://drive.google.com/open?id=10yNBWsw3Ozow2kIZ7g0Lu72wnWvcl9xc