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| Section | Weight | Objectives |
|---|---|---|
| Deployment | 24% | - Test applications in development environments
|
| Security | 26% | - Implement authentication and/or authorization for applications and AWS services
|
| Troubleshooting and Optimization | 18% | - Instrument code for observability
|
| Development with AWS Services | 32% | - Develop code for applications hosted on AWS
|
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NEW QUESTION # 349
A financial services company builds a credit card transaction processing application that uses an Amazon API Gateway HTTP API and AWS Lambda functions. The application logs all requests and request parameters to Amazon CloudWatch. The application makes the logs accessible to developer AWS accounts and a separate fraud detection AWS account by using a cross-account IAM role.
The company requires that only the fraud detection account be able to view customer credit card numbers that are associated with the transactions. Developers at the company must not be able to use the credit card numbers for testing or debugging.
The developers create the following data protection policy document snippet:
{
"Name": "data-protection-policy",
"Description": "Credit card redaction",
"Version": "2021-06-01",
"Statement": [{
"Sid": "redact-policy",
"DataIdentifier": [
"arn:aws:dataprotection::aws:data-identifier/CreditCardNumber"
],
"Operation": {
"Deidentify": {
"MaskConfig": {}
}
}
}]
}
Which combination of actions must the developers take to comply with the new policy? (Select TWO.)
Answer: A,B
Explanation:
To meet the requirement that developers cannot view credit card numbers while the fraud detection account can, the solution must (1) ensure the sensitive data is masked at ingestion/storage in CloudWatch Logs, and (2) allow only an authorized principal to view unmasked values.
First, the developers must attach the data protection policy to the CloudWatch log group that receives the API Gateway HTTP API access logs (and/or the Lambda log group if credit card numbers can appear there).
A CloudWatch Logs data protection policy is applied at the log group level and instructs CloudWatch Logs to identify sensitive data using managed data identifiers (for example, CreditCardNumber) and then apply the configured de-identification action (here, masking). Without attaching the policy to the log group, the masking/redaction behavior will not be enforced for newly ingested log events, and developers could still see raw request parameters.
Second, to permit only the fraud detection account to reveal the masked values when necessary, the IAM role that the fraud detection account assumes must have the specific CloudWatch Logs permission to unmask protected data. Granting logs:Unmask to that role enables authorized access to view the sensitive values while keeping them masked for everyone else who lacks that permission (such as developer accounts assuming other roles).
Options A and E are not required for CloudWatch Logs data protection enforcement; the core controls are log- group policy attachment (to perform masking) and IAM authorization (to permit unmasking only for the fraud role). Therefore, the correct combination is C (apply the policy to the log group that captures the HTTP API logs) and B (grant logs:Unmask to the fraud detection role).
NEW QUESTION # 350
A company has an analytics application that uses an AWS Lambda function to process transaction data asynchronously. A developer notices that asynchronous invocations of the Lambda function sometimes fail. When failed Lambda function invocations occur, the developer wants to invoke a second Lambda function to handle errors and log details.
Which solution will meet these requirements?
Answer: B
Explanation:
https://docs.aws.amazon.com/lambda/latest/dg/invocation-async.html#invocation-async- destinations
NEW QUESTION # 351
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: C
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 # 352
A company's developer needs to activate Amazon CloudWatch Logs Insights for an application's AWS Lambda functions. The company uses an AWS Serverless Application Model (AWS SAM) template to deploy the application. The SAM template includes a logical resource that is named CloudWatchLogGroup.
How should the developer modify the SAM template to activate CloudWatch Logs Insights for the Lambda functions?
Answer: A
Explanation:
To enable CloudWatch Logs Insights for AWS Lambda functions, you need to integrate Lambda Insights, which provides detailed metrics and logs for your Lambda functions, including enhanced monitoring, application tracing, and performance metrics. This can be done by adding the appropriate Lambda Insights extension and assigning the necessary IAM policies to the Lambda function.
Adding the Lambda Insights extension layer: This extension allows Lambda to send enhanced metrics and logs to CloudWatch for analysis with CloudWatch Logs Insights.
Adding the CloudWatchLambdaInsightsExecutionRolePolicy managed policy: This policy grants the necessary permissions for Lambda functions to send their logs and metrics to CloudWatch.
NEW QUESTION # 353
A developer updates an AWS Lambda function that an Amazon API Gateway API uses. The API is the backend for a web application.
The developer needs to test the updated Lambda function before deploying the Lambda function to production. The testing must not affect any production users of the web application.
Which solution will meet these requirements in the MOST operationally efficient way?
Answer: D
NEW QUESTION # 354
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