Domain 5: Managing Implementation and Ensuring Reliability

**CASE STUDY: TechStream Gaming** **Company Overview:** TechStream Gaming is a global gaming company with 500 employees and $100M in annual revenue. They develop multiplayer online games. **Current Technical Environment:** - On-premises data centers in US and EU - 200 servers (mix of Windows and Linux) - MySQL databases (5 TB total) - Peak concurrent users: 2 million - Current monthly infrastructure cost: $100K **Business Requirements:** - Reduce infrastructure costs by 40% - Support 5x user growth over 2 years - Launch in 3 new regions (APAC, SA, Africa) - Improve deployment speed (current: 1 week -> target: daily) **Executive Statements:** - CEO: "We need to scale rapidly to compete with larger gaming companies. Cloud migration is critical to our growth strategy." - CFO: "Cost reduction is paramount. We cannot exceed $60K/month in cloud costs. ROI must be achieved within 18 months." - CTO: "Our team has limited cloud experience. We need a solution that doesn't require extensive retraining. Reliability is non-negotiable - 99.95% uptime minimum." **Technical Requirements:** - Sub-100ms latency for players globally - Real-time analytics on player behavior - Seasonal traffic spikes (5x during holidays) - DDoS protection - CI/CD pipeline for daily deployments **Constraints:** - Migration must complete in 12 months - Cannot exceed 4-hour downtime during cutover - Development team: 20 engineers (Java, MySQL expertise) - Operations team: 5 engineers (limited cloud experience) **QUESTION:** To meet the CFO's requirement of reducing costs to under $60K/month while handling seasonal traffic spikes, which cost optimization strategy should you implement?

**CASE STUDY: TrendWear Apparel** **Company Overview:** TrendWear Apparel is a global clothing retailer with an e-commerce platform and 500 physical stores. **Current Technical Environment:** - On-premises VMware environment - Legacy IBM Mainframe for core inventory management - Monolithic e-commerce application running on VMs **Business Requirements:** - Modernize the e-commerce platform to handle Black Friday (10x normal traffic) - Unify online and in-store inventory data in real-time - Avoid major capital expenditure (CapEx) for data center refreshes **Executive Statements:** - CEO: "We need an omnichannel experience. Customers should see accurate store inventory online." - CFO: "We must shift from CapEx to OpEx. No more buying hardware." - CTO: "We want to move to microservices, but we cannot retire the mainframe for at least 3 years due to complex legacy dependencies." **Technical Requirements:** - Hybrid architecture connecting GCP and on-premises - Microservices architecture for the new e-commerce platform - PCI-DSS compliance for all payment processing - Consistent management plane across on-prem and cloud **Constraints:** - Mainframe must remain on-premises - E-commerce migration must be completed before the next holiday season (8 months) **QUESTION:** To handle the Black Friday traffic (10x normal load), the operations team is concerned about the GKE cluster scaling fast enough. What combination of GCP features should you implement to ensure the platform remains responsive?

**CASE STUDY: AutoMakers Inc** **Company Overview:** AutoMakers Inc is a global vehicle manufacturer. They have recently launched a line of connected cars. **Current Technical Environment:** - 1 million connected cars currently on the road - Cars send telemetry data (speed, engine temp, location) every 5 seconds - Current on-premises MQTT brokers are crashing under the load **Business Requirements:** - Enable predictive maintenance to alert drivers before parts fail - Provide real-time fleet tracking for commercial customers - Support over-the-air (OTA) software updates **Executive Statements:** - CEO: "Data is our new revenue stream. We need to monetize this telemetry data." - CTO: "We expect to have 10 million connected cars in 3 years. The architecture must scale infinitely without manual intervention." - CFO: "The cost of ingesting and storing this data must be strictly controlled. We cannot pay for idle capacity." **Technical Requirements:** - Ingest up to 100,000 messages per second - Low-latency processing for real-time alerts - Time-series data storage for historical analysis - Handle variable network connectivity (cars driving through tunnels) **Constraints:** - Strict budget for data ingestion - Small data engineering team **QUESTION:** To address the CFO's concern about the cost of ingesting millions of messages, how should you optimize the data transmission from the cars to Cloud Pub/Sub?

A complex microservices application running on GKE is experiencing intermittent high latency. The application consists of 15 different services written in Go and Java. Users report that the checkout process sometimes takes 5 seconds instead of the usual 200ms. You need to identify exactly which microservice is causing the bottleneck. Which GCP observability tool should you use?

The CFO of your company wants to create custom dashboards to analyze Google Cloud spending across different departments. They want to write SQL queries to find out exactly how much the 'Marketing' department spent on BigQuery last month. How should you configure the billing data to enable this?

You manage a GKE cluster that runs a mix of critical web services and fault-tolerant batch processing jobs. The CFO has asked you to drastically reduce the compute costs of the cluster. How should you optimize the cluster architecture?

Your data analytics team runs massive SQL queries on BigQuery. The CFO has noticed that BigQuery costs are spiraling out of control because analysts are running `SELECT *` queries on petabyte-scale tables. Which THREE strategies should you implement to optimize BigQuery costs? (Select THREE)

You are defining the reliability metrics for a new API service according to Google's SRE practices. Which TWO statements correctly define the relationship between Service Level Indicators (SLIs) and Service Level Objectives (SLOs)? (Select TWO)

A startup is running their application on Compute Engine. They have a predictable baseline load that runs 24/7, but they also have a unique workload that requires exactly 5 vCPUs and 12 GB of RAM. They want to minimize their monthly compute bill. Which TWO features should they utilize? (Select TWO)