You are on-call for an infrastructure service that has a large number of dependent systems. You receive an alert indicating that the service is failing to serve most of its requests and all of its dependent systems with hundreds of thousands of users are affected. As part of your Site Reliability Engineering (SRE) incident management protocol, you declare yourself Incident Commander (IC) and pull in two experienced people from your team as Operations Lead (OLJ and Communications Lead (CL). What should you do next?
You support a user-facing web application. When analyzing the application's error budget over the previous six months, you notice that the application has never consumed more than 5% of its error budget in any given time window. You hold a Service Level Objective (SLO) review with business stakeholders and confirm that the SLO is set appropriately. You want your application's SLO to more closely reflect its observed reliability. What steps can you take to further that goal while balancing velocity, reliability, and business needs? (Choose two.)
https://sre.google/sre-book/service-level-objectives/
You want the application's SLO to more closely reflect it's observed reliability. The key here is error budget never goes over 5%. This means they can have additional downtime and still stay within their budget.
You use a multiple step Cloud Build pipeline to build and deploy your application to Google Kubernetes Engine (GKE). You want to integrate with a third-party monitoring platform by performing a HTTP POST of the build information to a webhook. You want to minimize the development effort. What should you do?
Your team is designing a new application for deployment both inside and outside Google Cloud Platform (GCP). You need to collect detailed metrics such as system resource utilization. You want to use centralized GCP services while minimizing the amount of work required to set up this collection system. What should you do?
The easiest way to collect detailed metrics such as system resource utilization is to import the Stackdriver Profiler package, and configure it to relay function timing data to Stackdriver for further analysis. This way, you can use centralized GCP services without modifying your code or setting up additional tools.
As part of your company's initiative to shift left on security, the infoSec team is asking all teams to implement guard rails on all the Google Kubernetes Engine (GKE) clusters to only allow the deployment of trusted and approved images You need to determine how to satisfy the InfoSec teams goal of shifting left on security. What should you do?
The best option for implementing guard rails on all GKE clusters to only allow the deployment of trusted and approved images is to use Binary Authorization to attest images during your CI/CD pipeline. Binary Authorization is a feature that allows you to enforce signature-based validation when deploying container images. You can use Binary Authorization to create policies that specify which images are allowed or denied in your GKE clusters. You can also use Binary Authorization to attest images during your CI/CD pipeline by using tools such as Container Analysis or third-party integrations. An attestation is a digital signature that certifies that an image meets certain criteria, such as passing vulnerability scans or code reviews. By using Binary Authorization to attest images during your CI/CD pipeline, you can ensure that only trusted and approved images are deployed to your GKE clusters.
