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| Section | Weight | Objectives |
|---|---|---|
| Security and Compliance Automation | 13% | - Security automation in CI/CD and infrastructure
|
| Incident and Event Management | 18% | - Operational response and recovery
|
| Resilient Cloud Solutions | 15% | - High availability and fault tolerance design
|
| SDLC Automation | 22% | - CI/CD pipeline design and implementation
|
| Monitoring and Logging | 15% | - Observability and metrics
|
| Configuration Management and Infrastructure as Code | 17% | - Infrastructure provisioning and automation
|
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NEW QUESTION # 290
A company has a public application that uses an Amazon API Gateway REST API, an AWS Lambda function, and an Amazon RDS for PostgreSQL DB cluster. Users have recently received error messages as application demand increased.
The company's DevOps engineer discovered that the errors were caused by RDS connection limits being reached. The DevOps engineer also discovered that more than 90% of the API requests are GET requests that read from the DB cluster.
How should the DevOps engineer solve this problem with the LEAST development effort?
Answer: C
Explanation:
The root cause of the problem is that the RDS PostgreSQL instance is exceeding its database connection limit due to high volumes of concurrent GET requests. The most efficient and lowest-effort remediation is to reduce the number of connections that reach the database and serve as many reads as possible from a cache layer. Option B accomplishes both goals with minimal changes to the existing architecture.
First, RDS Proxy manages database connections efficiently by pooling and reusing them. This reduces connection churn and prevents the Lambda function from opening excessive concurrent connections during traffic spikes. RDS Proxy is natively integrated with RDS and requires only minor configuration changes without altering application logic.
Second, enabling API Gateway caching allows caching GET responses directly at the API layer. Because over
90% of requests are GET operations, enabling caching drastically reduces traffic hitting both Lambda and RDS, significantly improving performance and lowering connection pressure.
Option C also improves read scaling, but adding ElastiCache introduces additional infrastructure and requires modifying application code to query Redis before querying the database. This is more complex than enabling API Gateway caching. Options A and D require full database migration, which is far more labor-intensive.
Option B is the simplest, most effective, and most aligned with AWS best practices for reducing RDS connection saturation.
NEW QUESTION # 291
A company is building a serverless application that uses AWS Lambda functions to process data.
A BeginResponse Lambda function initializes data in response to specific application events. The company needs to ensure that a large number of Lambda functions are invoked after the BeginResponse Lambda function runs. Each Lambda function must be invoked in parallel and depends on only the outputs of the BeginResponse Lambda function. Each Lambda function has retry logic for invocation and must be able to fine-tune concurrency without losing data.
Which solution will meet these requirements with the MOST operational efficiency?
Answer: C
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
To invoke many Lambda functions in parallel and allow each function to have independent retry logic and concurrency management, using SQS queues for each Lambda function is recommended.
* The BeginResponse Lambda publishes a message to an SNS topic, which fans out to multiple SQS queues (one per Lambda).
* Each Lambda function polls its own SQS queue, allowing fine-grained control of concurrency and retry behavior.
* SNS alone (Option A) invokes Lambda functions but lacks the queue's buffering and retry durability.
* Step Functions (Option D) would invoke Lambdas sequentially, not in parallel, and add complexity.
* Option C reverses SNS and SQS in an inefficient manner.
References:
Using SNS with SQS for fan-out and Lambda processing
Lambda retry behavior with SQS triggers
NEW QUESTION # 292
A company detects unusual login attempts in many of its AWS accounts. A DevOps engineer must implement a solution that sends a notification to the company's security team when multiple failed login attempts occur.
The DevOps engineer has already created an Amazon Simple Notification Service (Amazon SNS) topic and has subscribed the security team to the SNS topic.
Which solution will provide the notification with the LEAST operational effort?
Answer: A
Explanation:
Failed interactive logins such as ConsoleLogin are part of CloudTrail management events, not data events.
The most direct, low-overhead design is to stream management events to CloudWatch Logs, then create a metric filter that matches failed ConsoleLogin events (for example, $.eventName = "ConsoleLogin" and $.
responseElements.ConsoleLogin = "Failure" or $.errorMessage). That metric is then used as the basis for a CloudWatch alarm. When the alarm threshold (e.g., N failures in a period) is breached, the alarm triggers and sends a notification to the already created SNS topic, which alerts the security team.
Option A follows this recommended pattern: CloudTrail # CloudWatch Logs # Metric Filter # Alarm # SNS.
It is fully managed, near real-time, and requires minimal custom logic.
Option B uses Athena with EventBridge to periodically query CloudTrail logs in S3. This introduces more moving parts, more configuration, higher latency, and more operational overhead.
Options C and D incorrectly reference CloudTrail data events and S3 event notifications, which are not the right mechanisms to detect ConsoleLogin failures. Console logins are not S3 events, and using data events would miss these management actions.
Therefore, Option A is the correct and simplest solution.
NEW QUESTION # 293
A DevOps administrator is responsible for managing the security of a company's Amazon CloudWatch Logs log groups. The company's security policy states that employee IDs must not be visible in logs except by authorized personnel. Employee IDs follow the pattern of Emp-XXXXXX, where each X is a digit.
An audit discovered that employee IDs are found in a single log file. The log file is available to engineers, but the engineers are not authorized to view employee IDs. Engineers currently have an AWS IAM Identity Center permission that allows logs:* on all resources in the account.
The administrator must mask the employee ID so that new log entries that contain the employee ID are not visible to unauthorized personnel.
Which solution will meet these requirements with the MOST operational efficiency?
Answer: A
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
Amazon CloudWatch Logs now supports data protection policies that can mask sensitive data in logs at ingestion time using custom data identifiers. By creating a custom data identifier matching the employee ID pattern (Emp-\d{6}), the administrator can mask those patterns in new log entries automatically.
Assigning an IAM policy that denies the logs:Unmask action to engineers prevents them from seeing unmasked sensitive data, enforcing least privilege access.
This approach is operationally efficient because it uses built-in CloudWatch Logs data protection capabilities without the need for complex custom code or manual log filtering.
Option C requires writing and managing Lambda functions for log transformation, increasing operational overhead. Option D is more complex and reactive rather than preventing the sensitive data exposure. Option B incorrectly references NotAction and managed identifiers that may not match the custom pattern.
Reference:
Amazon CloudWatch Logs Data Protection Policies:
"You can create data protection policies with custom data identifiers to mask sensitive information such as employee IDs in log data." (AWS Documentation on CloudWatch Logs Data Protection)
NEW QUESTION # 294
A DevOps engineer needs to apply a core set of security controls to an existing set of AWS accounts. The accounts are in an organization in AWS Organizations. Individual teams will administer individual accounts by using the AdministratorAccess AWS managed policy. For all accounts. AWS CloudTrail and AWS Config must be turned on in all available AWS Regions. Individual account administrators must not be able to edit or delete any of the baseline resources. However, individual account administrators must be able to edit or delete their own CloudTrail trails and AWS Config rules.
Which solution will meet these requirements in the MOST operationally efficient way?
Answer: B
NEW QUESTION # 295
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