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| Section | Weight | Objectives |
|---|---|---|
| Monitoring and Logging | 15% | - Implement logging and analysis
|
| Configuration Management and Infrastructure as Code | 17% | - Implement infrastructure as code
|
| Security, Governance, and Compliance | 17% | - Automate security and compliance
|
| Resilient Cloud Solutions | 15% | - Ensure reliability and scalability
|
| SDLC Automation | 22% | - Automate software release processes
|
| Incident and Event Response | 14% | - Automate event management
|
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NEW QUESTION # 361
A company manages environments for its application in multiple AWS accounts. Each environment account is in a different OU in AWS Organizations.
A DevOps team is responsible for the application deployment process across the environments. The deployment process uses an AWS CodePipeline pipeline in a Shared Services account. The DevOps team members are in the same user group. The team members have administrative access to all accounts through AWS IAM Identity Center.
A recent deployment problem in the development environment required the DevOps team to perform manual steps. The deployment to the production environment then resulted in an incident that caused the pipeline to fail, blocking new deployments for several hours.
A DevOps engineer needs to ensure that only the pipeline can perform deployments in the production environment. The DevOps engineer must have access to the environment in case of an emergency.
Which solution will meet these requirements with the MOST operational efficiency?
Answer: C
Explanation:
The requirement is to restrict production deployments strictly to the pipeline, while still allowing emergency access to a specific engineer.
* The best approach is to restrict the DevOps team to read-only access in production accounts, minimizing risk of manual changes (Option A).
* The DevOps engineer can have an admin permission set but assume the pipeline IAM role for deployment, enforcing strict control.
* Applying an SCP to deny modification by anyone other than the pipeline role enforces this at the organization level. Option B is similar but unnecessarily creates separate IAM users, increasing management overhead. Option C grants the DevOps engineer broader permissions that may conflict with controls. Option D complicates management with tagging and SCPs, increasing operational overhead.
Reference:
AWS Organizations Service Control Policies (SCPs): " SCPs can restrict what actions identities in member accounts can perform, even for administrators. " (AWS Organizations SCP Documentation) IAM Identity Center Role Assumption Best Practices: " Use role assumption for limited elevated permissions instead of broad admin access. " (AWS IAM Best Practices)
NEW QUESTION # 362
A company's application has an API that retrieves workload metrics. The company needs to audit, analyze, and visualize these metrics from the application to detect issues at scale.
Which combination of steps will meet these requirements? (Select THREE).
Answer: B,D,F
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The recommended architecture to audit, analyze, and visualize application workload metrics at scale involves:
Using Amazon EventBridge to schedule AWS Lambda invocations that call the application API and fetch metrics (Option A). The data is stored in Amazon S3, which is ideal for scalable, cost-effective storage of large datasets.
Cataloging the stored data with AWS Glue crawlers, enabling schema discovery and making data queryable via Amazon Athena (Option C).
Visualizing the data by creating Amazon QuickSight datasets from Athena views and building dashboards for analysis (Option E).
Option B and D introduce DynamoDB, which is less suitable for large-scale analytics and Athena querying. Option F suggests querying Athena via Lambda widgets in CloudWatch, which adds complexity without significant benefit over QuickSight.
Reference:
Serverless Analytics Architecture on AWS:
"Use Lambda + S3 + Glue + Athena + QuickSight for scalable data ingestion, cataloging, querying, and visualization." (AWS Analytics Reference Architecture) AWS Glue Crawlers and Athena:
"Glue crawlers catalog data stored in S3 and create Athena tables to enable SQL querying." (AWS Glue Documentation) Amazon QuickSight for Visualization:
"QuickSight enables creating interactive dashboards from Athena query results." (Amazon QuickSight Overview)
NEW QUESTION # 363
A company has an AWS account named PipelineAccount. The account manages a pipeline in AWS CodePipeline. The account uses an IAM role named CodePipeline_Service_Role and produces an artifact that is stored in an Amazon S3 bucket. The company uses a customer managed AWS KMS key to encrypt objects in the S3 bucket.
A DevOps engineer wants to configure the pipeline to use an AWS CodeDeploy application in an AWS account named CodeDeployAccount to deploy the produced artifact.
The DevOps engineer updates the KMS key policy to grant the CodeDeployAccount account permission to use the key. The DevOps engineer configures an IAM role named DevOps_Role in the CodeDeployAccount account that has access to the CodeDeploy resources that the pipeline requires. The DevOps engineer updates an Amazon EC2 instance role that operates within the CodeDeployAccount account to allow access to the S3 bucket and the KMS key that is in the PipelineAccount account.
Which additional steps will meet these requirements?
Answer: B
Explanation:
Comprehensive and Detailed Explanation From Exact Extract of DevOps Engineer Documents Only:
For cross-account deployments, CodePipeline must assume a role in the target account. Configure S3 bucket policy to allow the target account (CodeDeployAccount) access, trust relationship in DevOps_Role to allow assumption by the pipeline's account (PipelineAccount), and update CodePipeline_Service_Role with sts:AssumeRole permissions. This cross-account pipeline setup is documented in "Cross-Account Access for AWS CodePipeline."
NEW QUESTION # 364
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,C,D
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 # 365
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:
The correct answer is C. Configuring AWS CloudTrail to send log data events to an Amazon CloudWatch Logs log group and creating a CloudWatch logs metric filter to match failed ConsoleLogin events is the simplest and most efficient way to monitor and alert on failed login attempts. Creating a CloudWatch alarm that is based on the metric filter and configuring an alarm action to send messages to the SNS topic will ensure that the security team is notified when multiple failed login attempts occur. This solution requires the least operational effort compared to the other options.
Option A is incorrect because it involves configuring AWS CloudTrail to send log management events instead of log data events. Log management events are used to track changes to CloudTrail configuration, such as creating, updating, or deleting a trail. Log data events are used to track API activity in AWS accounts, such as login attempts. Therefore, option A will not capture the failed ConsoleLogin events.
Option B is incorrect because it involves creating an Amazon Athena query and two Amazon EventBridge rules to monitor and alert on failed login attempts. This is a more complex and costly solution than using CloudWatch logs and alarms. Moreover, option B relies on the query returning a failure, which may not happen if the query is executed successfully but does not find any failed logins.
Option D is incorrect because it involves configuring AWS CloudTrail to send log data events to an Amazon S3 bucket and configuring an Amazon S3 event notification for the s3:ObjectCreated event type. This solution will not work because the s3:ObjectCreated event type does not allow filtering by ConsoleLogin failed events. The event notification will be triggered for any object created in the S3 bucket, regardless of the event type. Therefore, option D will generate a lot of false positives and unnecessary notifications.
AWS CloudTrail Log File Examples
Creating CloudWatch Alarms for CloudTrail Events: Examples
Monitoring CloudTrail Log Files with Amazon CloudWatch Logs
NEW QUESTION # 366
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