Amazon Web Services DOP-C02 - AWS Certified DevOps Engineer - Professional

Short Explanation: To meet the requirements, the DevOps engineer should update the SAML assertion to pass the user’s team name, update the IAM role’s trust policy to add an access-team session tag that has the team name, create an IAM permissions boundary in each account, and for each CodeCommit repository, add an access-team tag that has the value set to the name of the associated team.

Updating the SAML assertion to pass the user’s team name allows the DevOps engineer to use IAM tags to identify which team a user belongs to. This can help enforce fine-grained access control based on the user’s team membership1.

Updating the IAM role’s trust policy to add an access-team session tag that has the team name allows the DevOps engineer to use IAM condition keys to restrict access based on the session tag value2. For example, the DevOps engineer can use the aws:PrincipalTag condition key to match the access-team tag of the user with the access-team tag of the repository3.

Creating an IAM permissions boundary in each account allows the DevOps engineer to set the maximum permissions that an identity-based policy can grant to an IAM entity. An entity’s permissions boundary allows it to perform only the actions that are allowed by both its identity-based policies and its permissions boundaries4. For example, the DevOps engineer can use a permissions boundary policy to limit the actions that a user can perform on CodeCommit repositories based on their access-team tag5.

For each CodeCommit repository, adding an access-team tag that has the value set to the name of the associated team allows the DevOps engineer to use resource tags to identify which team manages a repository. This can help enforce fine-grained access control based on the resource tag value6.

The other options are incorrect because:

Creating an approval rule template for each team in the Organizations management account is not a valid option, as approval rule templates are not supported by AWS Organizations. Approval rule templates are specific to CodeCommit and can only be associated with one or more repositories in the same AWS Region where they are created7.

Creating an approval rule template for each account is not a valid option, as approval rule templates are not designed to restrict access to modify branches. Approval rule templates are designed to require approvals from specified users or groups before merging pull requests8.

Attaching an SCP to the accounts is not a valid option, as SCPs are not designed to restrict access based on tags. SCPs are designed to restrict access based on service actions and resources across all users and roles in an organization’s account9.

Step 1: Using Native Git in CodeBuildTo meet the requirement of running unit tests and tagging the most recent commit if the tests pass, the CodeBuild project should be configured to use native Git to clone the CodeCommit repository. Native Git support allows full functionality for managing the repository, including the ability to create and push tags.

Action: Configure the CodeBuild project to use native Git to clone the repository and run the tests.

Why: Using native Git provides flexibility for managing tags and other repository operations after the tests are successfully executed.

Step 2: Tagging the Most Recent CommitOnce the unit tests pass, the CodeBuild project can use native Git to create a tag for the most recent commit and push that tag to the repository. This ensures that the tagged commit is linked to the test results.

Action: Configure the project to use native Git to create and push a tag to the repository if the tests pass.

Why: This ensures the correct commit is tagged automatically, streamlining the workflow.

https://docs.aws.amazon.com/securityhub/latest/userguide/accounts-orgs-auto-enable.html

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.

Step 1: Handling Invalid Data with Failed Batch ItemsThe Lambda function is processing batches of messages, and some messages contain invalid data, causing processing delays. Lambda provides the capability to report failed batch items, which allows valid messages to be processed while skipping invalid ones. This functionality ensures that the valid messages are processed within the required timeline.

Action: Keep the Lambda function's batch size the same and configure it to report failed batch items.

Why: By reporting failed batch items, the Lambda function can skip invalid messages and continue processing valid ones, ensuring that they meet the processing timeline.

Comprehensive and Detailed Explanation From Exact Extract:

AWS CodeDeploy supports deployment of both Lambda functions and ECS services with native integration.

It provides deployment strategies such as canary deployments for Lambda and blue/green deployments for ECS, which minimize downtime and risk.

CodeDeploy automates the deployment process, which reduces manual effort and improves consistency.

AWS Systems Manager Parameter Store is a simple, managed solution for storing configuration data securely and at scale.

Option A uses CloudFormation stack updates with resource replacement, which can cause downtime and is less flexible for minimizing downtime compared to CodeDeploy’s deployment strategies.

Option C adds unnecessary orchestration complexity with Step Functions and cross-region deployments, increasing deployment effort.

Option D’s all-at-once deployments for Lambda and rolling updates for ECS do not minimize downtime as effectively as canary and blue/green strategies.Hence, option B meets the requirement with the least deployment effort and maximizes automation and availability.

Reference from AWS Official Documentation:

Deploying Lambda Functions with CodeDeploy:"AWS CodeDeploy supports canary and linear deployment strategies for AWS Lambda functions to reduce deployment risk."(CodeDeploy Lambda Deployment)

Deploying ECS Services with CodeDeploy Blue/Green:"CodeDeploy supports blue/green deployments for ECS to minimize downtime during service updates."(CodeDeploy ECS Blue/Green)

AWS Systems Manager Parameter Store:"Parameter Store provides secure, hierarchical storage for configuration data management."(Systems Manager Parameter Store)

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.

RDS Multi-AZ with cross-Region read replicas provides synchronous replication with near-zero data loss and supports fast failover with RPO < 1 min and RTO < 10 min.

Automation can promote replicas and update Route 53 CNAME to redirect traffic during failover.

Options B, C, and D rely on snapshots, backups, or DMS, which cannot guarantee such low RPO/RTO and require manual intervention or complex automation.