Use GKE's built-in Secret object to store sensitive environment variables. -> Correct. Kubernetes' Secret object is specifically designed to store sensitive data, such as passwords, OAuth tokens, and ssh keys. This is the most secure method to provide sensitive environment variables to the application.

Embed the sensitive environment variables directly in the Dockerfile. -> Incorrect. Embedding the sensitive environment variables directly in the Dockerfile is a security risk and not a best practice, as this could expose sensitive data.

Store sensitive environment variables in a ConfigMap. -> Incorrect. ConfigMaps are designed to store non-sensitive configuration data in key-value pairs, and are not meant to securely store sensitive data.

Pass sensitive environment variables to the application using the command line. -> Incorrect. Pass sensitive environment variables to the application using the command line.

Cloud Storage with signed URLs -> Correct. Cloud Storage allows you to store, retrieve, and share data, and using signed URLs can provide time-limited access to objects.

Cloud Firestore -> Incorrect. Cloud Firestore is a NoSQL document database, and while it does offer the ability to store and retrieve documents, it doesn't provide built-in time-limited access to documents.

Cloud BigQuery -> Incorrect. Cloud BigQuery is a web service from Google that is used for handling and analyzing big data. It's not designed for storing documents or providing time-limited access to them.

SSL/TLS certificates with a supported load balancer. -> Correct. Using SSL/TLS certificates with a load balancer like the Google Cloud Load Balancer enables secure, encrypted connections from users to your application.

Cloud Armor -> Incorrect. Cloud Armor is a distributed denial of service (DDoS) and application defense service. Although it helps to secure your applications from certain types of threats, it doesn't provide the secure, encrypted connections you're looking for.

Cloud VPN -> Incorrect. Cloud VPN securely connects your on-premises network to your Google Cloud Virtual Private Cloud (VPC) network through an IPsec VPN connection, but it's not used to secure client-to-application communications.

Google Kubernetes Engine without additional configurations. -> Incorrect. Google Kubernetes Engine (GKE) provides a managed environment for deploying containerized applications but does not automatically provide SSL/TLS encryption for external connections.

Binary Authorization -> Correct. Binary Authorization is a Google Cloud service designed to ensure only trusted container images are deployed on GKE by enforcing signature validation when deploying the images.

Cloud Armor -> Incorrect. Cloud Armor is a distributed denial-of-service (DDoS) defense service, it does not directly deal with application binaries.

Cloud Identity-Aware Proxy -> Incorrect. Cloud Identity-Aware Proxy is used to control access to cloud applications running on Google Cloud, it does not deal with application binaries.

VPC Service Controls -> Incorrect. VPC Service Controls is a tool used to define security perimeters around Google Cloud resources to mitigate data exfiltration risks, it does not scan or secure application binaries.

Init Containers -> Correct. Init Containers can be used to separate setup scripts not present in the main application. They must run and complete successfully before the regular containers in a Pod are started. This makes them a useful tool for managing and troubleshooting the container lifecycle.

Namespaces -> Incorrect. Namespaces are a way to divide cluster resources between multiple users. While they're crucial for multi-tenant environments, they do not directly assist with container lifecycle management.

DaemonSets -> Incorrect. DaemonSets ensure that all (or some) nodes run a copy of a pod, which can be helpful for running background tasks but does not directly assist with container lifecycle management.

Persistent Volumes -> Incorrect. Persistent Volumes provide storage to a cluster, but they do not directly assist with container lifecycle management.

CPU and memory utilization of your Cloud Run service. -> Correct. CPU and memory utilization can directly impact the performance of your service. If your service is under-resourced, it could struggle during periods of high demand.

The number of active instances of your Cloud Run service. -> Incorrect. While it's important to know the number of active instances, it might not directly relate to the performance issues during peak times. The issue could be due to resource constraints, regardless of the number of instances.

The network latency between the Cloud Run service and its data sources. -> Incorrect. Network latency could be a cause of performance issues, but it would not typically be the primary focus in the case of peak usage times.

HTTP response codes of your Cloud Run service. -> Incorrect. While HTTP response codes can provide information about errors, they are not the primary metric to check when dealing with performance issues during peak usage times.

Traffic Director -> Correct. Traffic Director is a Google Cloud service for traffic control and configuration for service mesh. It provides global traffic management, allowing you to easily manage and control inter-service communication, making it the correct answer for this scenario.

Google Kubernetes Engine (GKE) -> Incorrect. Google Kubernetes Engine (GKE) is a powerful platform for running and managing containerized applications, but it doesn't inherently provide fine-grained control over inter-service communication.

Cloud Load Balancing -> Incorrect. Cloud Load Balancing is a scalable and programmable load-balancer, but it doesn't manage inter-service communication. It's used to distribute traffic to services based on rules.

Cloud Endpoints -> Incorrect. Cloud Endpoints is a tool for developing, deploying, and managing APIs on a secure and scalable infrastructure, but it doesn't manage communication between microservices.

Cloud Bigtable -> Correct. Cloud Bigtable provides high throughput for both read and write operations and offers eventual consistency. It allows users to see their own posts immediately (read-after-write consistency) but updates are propagated to all users with a slight delay (eventual consistency), which fits the requirements of this application.

Cloud Spanner -> Incorrect. While Google Cloud Spanner provides strong consistency, it may be overkill for this use case and could increase costs unnecessarily.

Cloud SQL -> Incorrect. Cloud SQL is a traditional relational database that may not be able to handle the scale of a large social media application without complex sharding strategies.

Cloud Datastore -> Incorrect. Cloud Datastore offers eventual consistency but it might not provide the high write throughput that is required for a large social media application.

cloudbuild.yaml -> Correct. Cloud Build uses a "cloudbuild.yaml" file to define the steps that it should perform during a build.

build.yaml -> Incorrect. While YAML is a common language for defining infrastructure, the correct file name for Cloud Build is "cloudbuild.yaml", not "build.yaml".

build.json -> Incorrect. While JSON could technically be used, it's not the conventional choice for Cloud Build.

build.xml -> Incorrect. XML is not typically used with Cloud Build.

Increased time spent on container orchestration -> Correct. While a CaaS platform automates many aspects of managing containers, the team still has to spend time managing the orchestration of those containers (i.e., configuring deployments, managing services, and maintaining the overall health of the environment). This additional responsibility can reduce the time available for developing new features.

Increased development velocity due to infrastructure abstraction -> Incorrect. Typically, a CaaS platform would enhance development velocity due to infrastructure abstraction, not reduce it. However, this can be offset if the team has to spend a lot of time on container orchestration.

Limited ability to implement DevOps practices -> Incorrect. CaaS platforms typically support and often encourage DevOps practices, providing integrated tooling and automation capabilities for continuous integration and deployment.

Reduced ability to handle high-traffic workloads -> Incorrect. CaaS platforms are designed to handle high-traffic workloads efficiently, given they're correctly configured, and shouldn't typically cause a slowdown in feature rollout.