The correct answers are A and D.
Examine the wall-clock time and the CPU time of the application. If the difference is substantial, increase the CPU resource allocation. This is a good way to determine if the application is CPU-bound, meaning that it spends more time waiting for the CPU than performing actual computation.Increasing the CPU resource allocation can improve the performance of CPU-bound applications1.
Examine the latency time, the wall-clock time, and the CPU time of the application. If the latency time is slowly burning down the error budget, and the difference between wall-clock time and CPU time is minimal, mark the application for optimization. This is a good way to determine if the application is I/O-bound, meaning that it spends more time waiting for input/output operations than performing actual computation.
Increasing the CPU resource allocation will not help I/O-bound applications, and they may need optimization to reduce the number or duration of I/O operations2.
Answer B is incorrect because increasing the memory resource allocation will not help if the application is CPU-bound or I/O-bound. Memory allocation affects how much data the application can store and access in memory, but it does not affect how fast the application can process that data.
Answer C is incorrect because increasing the local disk storage allocation will not help if the application is CPU-bound or I/O-bound. Disk storage affects how much data the application can store and access on disk, but it does not affect how fast the application can process that data.
Answer E is incorrect because examining the heap usage of the application will not help to determine if the application needs performance tuning. Heap usage affects how much memory the application allocates for dynamic objects, but it does not affect how fast the application can process those objects. Moreover, low heap usage does not necessarily mean that the application is inefficient or unoptimized.

Comprehensive and Detailed Explanation:
To follow GitOps best practices and Google Cloud's recommended repository structure for Terraform (IaC), Config Sync, and application code, we should use a shared repository for Terraform and Config Sync while keeping application repositories separate.
Cloud Infrastructure (Terraform) repository is shared # This allows infrastructure teams to manage all environments in a single repository with different directories per environment (dev, QA, production). This is the standard approach to structuring Terraform repositories.
GKE Enterprise Infrastructure (Config Sync) repository is shared # Using Kustomize overlays per environment (instead of separate repositories) aligns with Config Sync's best practices and makes managing configurations easier.
Application repositories are separated, using different branches for features # This allows application teams to follow the Git branching model (feature branches, main branch, release branches, etc.) without affecting infrastructure.
#Official Reference:
Config Sync Best Practices
Terraform Structure Best Practices
GitOps Best Practices

Explanation
Create log views for each project team, and only show each project team their application logs. Grant the operations team access to the _AllLogs View in the central logging project1.
This approach aligns with the Google Cloud's recommended methodologies for Professional Cloud DevOps Engineers1. Log views allow you to create and manage access control at a finer granularity for your logs. By creating a separate log view for each project team, you can ensure that they only have access to their respective logs. The operations team, on the other hand, can be granted access to the _AllLogs view in the central logging project, allowing them to view all logs as required.
This solution not only meets the security team's requirements but also minimizes costs as it leverages built-in features of Google Cloud's logging and does not require exporting logs to another service like BigQuery (as suggested in option A), which could incur additional costs1.