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| Section | Weight | Objectives |
|---|---|---|
| Incident and Event Response | 14% | - Improve operational processes
|
| Security, Governance, and Compliance | 17% | - Automate security and compliance
|
| Resilient Cloud Solutions | 15% | - Design high availability architectures
|
| SDLC Automation | 22% | - Automate testing and validation
|
| Configuration Management and Infrastructure as Code | 17% | - Manage infrastructure deployments
|
| Monitoring and Logging | 15% | - Implement monitoring solutions
|
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NEW QUESTION # 354
A company's DevOps engineer is creating an AWS Lambda function to process notifications from an Amazon Simple Notification Service (Amazon SNS) topic. The Lambda function will process the notification messages and will write the contents of the notification messages to an Amazon RDS Multi-AZ DB instance.
During testing a database administrator accidentally shut down the DB instance. While the database was down the company lost several of the SNS notification messages that were delivered during that time.
The DevOps engineer needs to prevent the loss of notification messages in the future Which solutions will meet this requirement? (Select TWO.)
Answer: A,D
Explanation:
These solutions will meet the requirement because they will prevent the loss of notification messages in the future. An Amazon SQS queue is a service that provides a reliable, scalable, and secure message queue for asynchronous communication between distributed components. You can use an SQS queue to buffer messages from an SNS topic and ensure that they are delivered and processed by a Lambda function, even if the function or the database is temporarily unavailable.
Option C will configure an SQS dead-letter queue for the SNS topic. A dead-letter queue is a queue that receives messages that could not be delivered to any subscriber after a specified number of retries. You can use a dead-letter queue to store and analyze failed messages, or to reprocess them later. This way, you can avoid losing messages that could not be delivered to the Lambda function due to network errors, throttling, or other issues.
Option D will subscribe an SQS queue to the SNS topic and configure the Lambda function to process messages from the SQS queue. This will decouple the SNS topic from the Lambda function and provide more flexibility and control over the message delivery and processing. You can use an SQS queue to store messages from the SNS topic until they are ready to be processed by the Lambda function, and also to retry processing in case of failures. This way, you can avoid losing messages that could not be processed by the Lambda function due to database errors, timeouts, or other issues.
NEW QUESTION # 355
A company is developing an application that will generate log events. The log events consist of five distinct metrics every one tenth of a second and produce a large amount of data The company needs to configure the application to write the logs to Amazon Time stream The company will configure a daily query against the Timestream table.
Which combination of steps will meet these requirements with the FASTEST query performance? (Select THREE.)
Answer: A,B,E
Explanation:
Explanation
A comprehensive and detailed explanation is:
Option A is correct because using batch writes to write multiple log events in a single write operation is a recommended practice for optimizing the performance and cost of data ingestion in Timestream. Batch writes can reduce the number of network round trips and API calls, and can also take advantage of parallel processing by Timestream. Batch writes can also improve the compression ratio of data in the memory store and the magnetic store, which can reduce the storage costs and improve the query performance1.
Option B is incorrect because writing each log event as a single write operation is not a recommended practice for optimizing the performance and cost of data ingestion in Timestream. Writing each log event as a single write operation would increase the number of network round trips and API calls, and would also reduce the compression ratio of data in the memory store and the magnetic store. This would increase the storage costs and degrade the query performance1.
Option C is incorrect because treating each log as a single-measure record is not a recommended practice for optimizing the query performance in Timestream. Treating each log as a single-measure record would result in creating multiple records for each timestamp, which would increase the storage size and the query latency. Moreover, treating each log as a single-measure record would require using joins to query multiple measures for the same timestamp, which would add complexity and overhead to the query processing2.
Option D is correct because treating each log as a multi-measure record is a recommended practice for optimizing the query performance in Timestream. Treating each log as a multi-measure record would result in creating a single record for each timestamp, which would reduce the storage size and the query latency. Moreover, treating each log as a multi-measure record would allow querying multiple measures for the same timestamp without using joins, which would simplify and speed up the query processing2.
Option E is incorrect because configuring the memory store retention period to be longer than the magnetic store retention period is not a valid option in Timestream. The memory store retention period must always be shorter than or equal to the magnetic store retention period. This ensures that data is moved from the memory store to the magnetic store before it expires out of the memory store3.
Option F is correct because configuring the memory store retention period to be shorter than the magnetic store retention period is a valid option in Timestream. The memory store retention period determines how long data is kept in the memory store, which is optimized for fast point-in-time queries.
The magnetic store retention period determines how long data is kept in the magnetic store, which is optimized for fast analytical queries. By configuring these retention periods appropriately, you can balance your storage costs and query performance according to your application needs3.
References:
1: Batch writes
2: Multi-measure records vs. single-measure records
3: Storage
NEW QUESTION # 356
A company built its serverless infrastructure on AWS. The infrastructure consists of an Amazon API Gateway REST API, multiple AWS Lambda functions, and Amazon EventBridge.
The company wants to be aware of any new supply chain attacks that the company ' s CI/CD pipelines do not catch. The company needs a solution to detect malicious activity in the deployed application.
Which solution meets these requirements?
Answer: B
Explanation:
The question focuses on detecting supply chain attacks that bypass CI/CD controls and result in malicious runtime behavior in the deployed serverless application. This requires runtime threat detection , not just static scanning or infrastructure validation.
Amazon GuardDuty is AWS's managed threat detection service. GuardDuty now includes Lambda Protection
, which analyzes Lambda function behavior, including network calls, IAM API usage, and other signals to detect compromised functions, crypto-mining, data exfiltration, and other malicious activity. GuardDuty automatically ingests CloudTrail, VPC Flow Logs (if enabled), and other telemetry without requiring code changes or agents. Findings are published and can be routed to Amazon EventBridge for near real-time notifications or automated remediation.
Option A (AWS WAF) only inspects HTTP requests to API Gateway and is focused on known bad patterns, not deeper behavioral anomalies. Option C (CloudFormation Guard) is focused on infrastructure-as-code validation in the pipeline, which explicitly does not help once malicious code is deployed. Option D (Network Firewall with Emerging Threats rules) protects VPC traffic, but serverless components like Lambda and API Gateway may not all traverse that firewall, and it is not targeted at function-level behavior.
Hence, enabling GuardDuty with Lambda Protection and using EventBridge notifications is the correct solution.
NEW QUESTION # 357
A company recently launched multiple applications that use Application Load Balancers. Application response time often slows down when the applications experience problems A DevOps engineer needs to Implement a monitoring solution that alerts the company when the applications begin to perform slowly The DevOps engineer creates an Amazon Simple Notification Semce (Amazon SNS) topic and subscribe the company's email address to the topic What should the DevOps engineer do next to meet the requirements?
Answer: D
Explanation:
Option A is incorrect because creating an Amazon EventBridge rule that invokes an AWS Lambda function to query the applications on a 5-minute interval is not a valid solution. EventBridge rules can only trigger Lambda functions based on events, not on time intervals. Moreover, querying the applications on a 5-minute interval might incur unnecessary costs and network overhead, and might not detect performance issues in real time.
Option B is correct because creating an Amazon CloudWatch Synthetics canary that runs a custom script to query the applications on a 5-minute interval is a valid solution. CloudWatch Synthetics canaries are configurable scripts that monitor endpoints and APIs by simulating customer behavior. Canaries can run as often as once per minute, and can measure the latency and availability of the applications. Canaries can also send notifications to an Amazon SNS topic when they detect errors or performance issues1.
Option C is incorrect because creating an Amazon CloudWatch alarm that uses the AWS/ApplicationELB namespace RequestCountPerTarget metric is not a valid solution. The RequestCountPerTarget metric measures the number of requests completed or connections made per target in a target group2. This metric does not reflect the application response time, which is the requirement. Moreover, configuring the CloudWatch alarm to send a notification when the number of connections becomes greater than the configured number of threads that the application supports is not a valid way to measure the application performance, as it depends on the application design and implementation.
Option D is incorrect because creating an Amazon CloudWatch alarm that uses the AWS/ApplicationELB namespace RequestCountPerTarget metric is not a valid solution, for the same reason as option C. The RequestCountPerTarget metric does not reflect the application response time, which is the requirement. Moreover, configuring the CloudWatch alarm to send a notification when the average response time becomes greater than the longest response time that the application supports is not a valid way to measure the application performance, as it does not account for variability or outliers in the response time distribution.
References:
1: Using synthetic monitoring
2: Application Load Balancer metrics
NEW QUESTION # 358
An online retail company based in the United States plans to expand its operations to Europe and Asia in the next six months. Its product currently runs on Amazon EC2 instances behind an Application Load Balancer. The instances run in an Amazon EC2 Auto Scaling group across multiple Availability Zones. All data is stored in an Amazon Aurora database instance.
When the product is deployed in multiple regions, the company wants a single product catalog across all regions, but for compliance purposes, its customer information and purchases must be kept in each region.
How should the company meet these requirements with the LEAST amount of application changes?
Answer: A
NEW QUESTION # 359
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