Case Study 2 - HipLocal
Company Overview
HipLocal is a community application designed to facilitate communication between people in close proximity. It is used for event planning and organizing sporting events, and for businesses to connect with their local communities. HipLocal launched recently in a few neighborhoods in Dallas and is rapidly growing into a global phenomenon. Its unique style of hyper-local community communication and business outreach is in demand around the world.
Executive Statement
We are the number one local community app; it's time to take our local community services global. Our venture capital investors want to see rapid growth and the same great experience for new local and virtual communities that come online, whether their members are 10 or 10000 miles away from each other.
Solution Concept
HipLocal wants to expand their existing service, with updated functionality, in new regions to better serve their global customers. They want to hire and train a new team to support these regions in their time zones. They will need to ensure that the application scales smoothly and provides clear uptime data, and that they analyze and respond to any issues that occur.
Existing Technical Environment
HipLocal's environment is a mix of on-premises hardware and infrastructure running in Google Cloud Platform. The HipLocal team understands their application well, but has limited experience in global scale applications. Their existing technical environment is as follows:
- Existing APIs run on Compute Engine virtual machine instances hosted in GCP.
- State is stored in a single instance MySQL database in GCP.
- Release cycles include development freezes to allow for QA testing.
- The application has no logging.
- Applications are manually deployed by infrastructure engineers during periods of slow traffic on weekday evenings.
- There are basic indicators of uptime; alerts are frequently fired when the APIs are unresponsive.
Business Requirements
HipLocal's investors want to expand their footprint and support the increase in demand they are seeing. Their requirements are:
- Expand availability of the application to new regions.
- Support 10x as many concurrent users.
- Ensure a consistent experience for users when they travel to different regions.
- Obtain user activity metrics to better understand how to monetize their product.
- Ensure compliance with regulations in the new regions (for example, GDPR).
- Reduce infrastructure management time and cost.
- Adopt the Google-recommended practices for cloud computing.
Develop standardized workflows and processes around application lifecycle management.
Define service level indicators (SLIs) and service level objectives (SLOs).
Technical Requirements
- Provide secure communications between the on-premises data center and cloud-hosted applications and infrastructure.
- The application must provide usage metrics and monitoring.
- APIs require authentication and authorization.
- Implement faster and more accurate validation of new features.
- Logging and performance metrics must provide actionable information to be able to provide debugging information and alerts.
- Must scale to meet user demand.
For this question, refer to the HipLocal case study.
Which Google Cloud product addresses HipLocal's business requirements for service level indicators and objectives?

• A. Cloud Trace
• B. Cloud Monitoring
• C. Cloud Profiler
• D. Cloud Logging

You are building a mobile application that will store hierarchical data structures in a database.
The application will enable users working offline to sync changes when they are back online. A backend service will enrich the data in the database using a service account. The application is expected to be very popular and needs to scale seamlessly and securely. Which database and IAM role should you use?

• A. Use Cloud SQL, and assign the roles/cloudsql.editor role to the service account.
• B. Use Bigtable, and assign the roles/bigtable.viewer role to the service account.
• C. Use Firestore in Native mode and assign the roles/datastore.user role to the service account.
• D. Use Firestore in Datastore mode and assign the roles/datastore.viewer role to the service account.

You work for a financial services company that has a container-first approach. Your team develops microservices applications. You have a Cloud Build pipeline that creates a container image, runs regression tests, and publishes the image to Artifact Registry. You need to ensure that only containers that have passed the regression tests are deployed to GKE clusters. You have already enabled Binary Authorization on the GKE clusters. What should you do next?

• A. Create an attestor and a policy. Run a vulnerability scan to create an attestation for the container image as a step in the Cloud Build pipeline.
• B. Deploy Voucher Server and Voucher Client components. After a container image has passed the regression tests, run Voucher Client as a step in the Cloud Build pipeline.
• C. Create an attestor and a policy. Create an attestation for the container images that have passed the regression tests as a step in the Cloud Build pipeline.
• D. Set the Pod Security Standard level to Restricted for the relevant namespaces. Digitally sign the container images that have passed the regression tests as a step in the Cloud Build pipeline.

One of your deployed applications in Google Kubernetes Engine (GKE) is having intermittent performance issues. Your team uses a third-party logging solution. You want to install this solution on each node in your GKE cluster so you can view the logs. What should you do?

• A. Use SSH to connect to the GKE node, and install the software manually
• B. Deploy the third-party solution using Terraform and deploy the logging Pod as a Kubernetes Deployment
• C. Modify your container image to include the monitoring software
• D. Deploy the third-party solution as a DaemonSet

You are developing a discussion portal that is built on Cloud Run. Incoming external requests are routed through a set of microservices before a response is sent. Some of these microservices connect to databases. You need to run a load test to identify any bottlenecks in the application when it is under load. You want to follow Google-recommended practices. What should you do?

• A. Expose the latency metrics per service for each request. Configure Google Cloud Managed Service for Prometheus, and use it to scrape and analyze the metrics.
• B. Configure Cloud Trace to capture the requests from the load testing clients. Review the timings in Cloud Trace.
• C. Modify the response to include a time series that shows elapsed time per service. Use Log Analytics in Cloud Logging to create a heatmap that exposes any service that could be a bottleneck.
• D. Add log statements that capture elapsed time. Analyze the logs and metrics by using BigQuery.