Cloud Digital Leader Scaling with Google Cloud operations Practice Test 2 — 15 Questions

Question 1

A company runs an e-commerce platform on Google Kubernetes Engine (GKE) using autoscaling. They have a baseline workload and occasional traffic spikes during promotions. They configured a Horizontal Pod Autoscaler (HPA) for their web application pods and a Cluster Autoscaler for the node pool. The HPA targets 70% CPU utilization. During a recent sales event, traffic exceeded expectations. The operations team observed that the HPA increased the desired number of replicas to 50, but only 20 pods were running. The remaining 30 pods were in 'Pending' status. The Cluster Autoscaler logs show repeated messages: 'no capacity to scale up node pool'. The node pool is configured with a maximum of 10 nodes, each with 4 vCPUs, and currently all 10 nodes are running. The team checked the node pool's current utilization and found that nodes are near capacity. What should the team do to ensure the application scales correctly during future events?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Increase the maximum number of nodes in the node pool to allow more capacity.. The HPA requested 50 replicas, but only 20 could be scheduled because the existing 10 nodes are near capacity and the node pool is already at its maximum size. The Cluster Autoscaler cannot add more nodes because the node pool's maximum node limit prevents it from provisioning the needed additional capacity. Increasing the maximum number of nodes in the node pool (Option C) allows the Cluster Autoscaler to add nodes and accommodate the pending pods, enabling the HPA to scale as needed.

Answer

Increase the HPA target CPU utilization to 90% to reduce the number of replicas needed.

Answer

Reduce the pod resource requests for CPU so that more pods can fit on existing nodes.

Answer

Enable extra capacity by creating a second node pool with preemptible VMs.

Question 2

An e-commerce platform uses Compute Engine instances in a managed instance group behind a Cloud Load Balancer. During a flash sale, the load balancer reports increased error rates. The operations team suspects the instances are overwhelmed. Which two steps should they take to troubleshoot the issue? (Choose TWO.)

Accepted Answer

Check the CPU utilization of the instance group in Cloud Monitoring.. Checking CPU utilization in Cloud Monitoring directly reveals whether the instance group is resource-constrained. High CPU utilization indicates that the instances are overwhelmed, which aligns with the increased error rates reported by the load balancer. This metric is a primary indicator of compute capacity issues and helps validate the team's suspicion before taking corrective action. Additionally, reviewing load balancer logs in Cloud Logging can reveal specific error codes and patterns, helping to confirm whether the errors are due to backend capacity issues or other causes such as configuration problems.

Answer

Switch to a Network Load Balancer for higher throughput.

Answer

Increase the size of the instance group without investigation.

Answer

Enable HTTP health checks on the load balancer.

Question 3

A gaming company runs a real-time multiplayer game server on Google Kubernetes Engine. They want to optimize costs while ensuring low latency for players across different regions. Which three strategies should they implement? (Choose THREE.)

Accepted Answer

Use committed use discounts (CUDs) for sustained resource usage.. Committed use discounts (CUDs) are ideal for sustained resource usage because they offer significant cost savings (up to 70%) in exchange for a 1- or 3-year commitment to a minimum level of compute resources. For a gaming server that runs continuously, CUDs reduce the per-hour cost of the underlying GKE nodes, directly optimizing long-term operational expenses without impacting latency. Node auto-provisioning automatically scales the cluster based on pod resource requests, ensuring you only pay for the compute you actually need and reducing over-provisioning. Deploying GKE clusters in multiple regions and using a multi-cluster ingress routes player traffic to the nearest available cluster, reducing latency and providing resilience.

Answer

Use spot VMs with a node taint and toleration for game server pods.

Answer

Use preemptible VMs for game server pods.

Question 4

A startup has deployed a Node.js application on Cloud Run. They are seeing a higher-than-expected bill for Cloud Run usage. The application is accessed by users worldwide, and traffic patterns show occasional spikes. They want to reduce costs while maintaining performance. They currently have no concurrency management and use the default Cloud Run settings. What should they do first?

Accepted Answer

Set a maximum number of concurrent requests per container instance to reduce over-provisioning.. Cloud Run bills for container instance time during request processing. The default maximum concurrency is 80, not unlimited. To reduce costs, increase the max concurrency setting to a value your application can handle, allowing each instance to serve more simultaneous requests and reducing the number of instances needed. This directly lowers costs while maintaining performance.

Answer

Implement a caching layer with Cloud CDN.

Answer

Move the application to Compute Engine with a smaller machine type.

Answer

Reduce the container memory limit to the minimum required.

Question 5

Which TWO statements about resource monitoring and scaling on Google Cloud are correct?

Accepted Answer

You can use Stackdriver Monitoring to set up alerting policies that trigger scaling actions in managed instance groups.. Options B and C are correct. B is correct because Google Cloud's operations suite (formerly Stackdriver Monitoring) provides alerting policies that can trigger scaling actions in managed instance groups. C is correct because autoscaling policies can be configured to use metric thresholds and cooldown periods to prevent thrashing. Option A is incorrect because managed instance groups can autoscale based on multiple metrics, not only CPU utilization. Option D is incorrect because standard metrics from Google Cloud Monitoring can be used; custom metrics are not mandatory for autoscaling. Option E is incorrect because load balancers do not directly trigger scaling actions; the autoscaler handles scaling decisions.

Answer

Managed instance groups automatically scale based on CPU utilization only.

Answer

To scale based on custom metrics, you must use the autoscaler with a custom metric from Stackdriver.

Answer

Load balancers can directly trigger scaling actions without autoscaling.

Question 6

A company's web service has a Service Level Objective (SLO) of 99.9% monthly availability. In a 30-day month, how many minutes of downtime are allowed before the SLO is violated?

Accepted Answer

~43.2 minutes. The SLO of 99.9% monthly availability means the service can be unavailable for 0.1% of the total monthly time. In a 30-day month, total minutes are 30 × 24 × 60 = 43,200 minutes. 0.1% of 43,200 minutes is 43.2 minutes, so option B is correct.

Answer

~4.3 minutes

Answer

~7.2 hours

Answer

~8.6 hours

Question 7

A SRE team wants to alert when their service is consuming error budget faster than expected, rather than alerting only when the SLO threshold is crossed. Which Cloud Monitoring alerting strategy supports this approach?

Accepted Answer

SLO burn rate alerting — alert when error budget is being consumed faster than the measurement window allows.. B is correct because SLO burn rate alerting is specifically designed to detect when error budget is being consumed faster than the measurement window allows, enabling proactive alerts before the SLO threshold is breached. This approach uses a burn rate (e.g., 2x, 10x) to trigger alerts when the error budget depletion rate exceeds a predefined multiple of the expected rate, allowing the team to respond early. It directly addresses the requirement of alerting on error budget consumption speed rather than waiting for a hard SLO violation.

Answer

Threshold alerting — alert when error rate exceeds 0.1%.

Answer

Uptime check alerting — alert when health checks fail.

Answer

Log-based alerting — alert when specific error messages appear in logs.

Question 8

A company has deployed a critical application on Google Cloud and wants to understand what happens to their workloads during a Google Cloud data center maintenance event (e.g., host system upgrades). What Google Compute Engine feature handles this automatically for most VMs?

Accepted Answer

Live migration transparently moves VMs to healthy hosts during maintenance with no VM downtime.. Google Compute Engine uses Live Migration to automatically move running VMs from a host undergoing maintenance (e.g., host system upgrades) to a healthy host without interrupting the VM. This process preserves the VM's memory, network connections, and disk state, resulting in zero VM downtime. It is enabled by default for most VM instances, except those with GPUs or certain machine types that explicitly opt out.

Answer

VMs are terminated and restarted automatically on new hardware, causing a few minutes of downtime.

Answer

VMs are snapshotted, the snapshot is restored on new hardware, and the VM is restarted.

Answer

Customers must subscribe to Google Cloud support to receive advance notice and schedule their own maintenance windows.

Question 9

A company's application experiences traffic spikes every weekday morning when employees log in at 9 AM. The team wants their infrastructure to automatically handle these spikes without manual intervention and without over-provisioning resources all day. Which Google Cloud capability addresses this?

Accepted Answer

Configure autoscaling on the application's infrastructure to automatically scale up for load and scale down during off-peak hours.. Google Cloud's managed instance groups (MIGs) with autoscaling can automatically adjust the number of VM instances based on load metrics (e.g., CPU utilization, requests per second). This handles the 9 AM traffic spike without manual intervention and avoids over-provisioning during off-peak hours by scaling down when demand decreases.

Answer

Purchase reserved capacity for peak load and configure it to be active only on weekdays.

Answer

Deploy additional VMs manually each weekday morning and terminate them at night.

Answer

Use Cloud Monitoring to send an email alert when CPU exceeds 80% so the team can manually scale.

Question 10

A digital media company hosts video content globally. They want to reduce origin server load and deliver content faster to viewers worldwide. Their current architecture routes all viewer requests directly to the origin servers in `us-central1`, causing high latency for viewers in Asia and Europe. Which Google Cloud networking capability addresses this?

Accepted Answer

Enable Cloud CDN to cache video content at Google's global edge PoPs, serving viewers from the nearest location.. Cloud CDN uses Google's global edge Points of Presence (PoPs) to cache video content closer to viewers, reducing latency and offloading origin servers. When a viewer requests content, Cloud CDN serves it from the nearest edge cache if available, avoiding a direct trip to the origin in us-central1. This directly addresses the high latency for viewers in Asia and Europe without requiring server replication or bandwidth increases.

Answer

Deploy identical origin servers in every Google Cloud region globally.

Answer

Use Cloud VPN to route viewer traffic through a direct tunnel to the origin servers.

Answer

Increase the origin servers' network bandwidth to handle more simultaneous viewer connections.

Question 11

Which Google Cloud service provides a centralized view of an application's performance metrics, logs, and traces — enabling teams to monitor system health, set up alerts, and diagnose issues from a single platform?

Accepted Answer

Cloud Monitoring (part of Google Cloud's operations suite). Cloud Monitoring (part of Google Cloud's operations suite) is the correct answer because it provides a unified platform for collecting and visualizing metrics, logs, and traces from applications and infrastructure. It enables teams to set up alerting policies, create dashboards, and diagnose performance issues using a single interface, integrating with services like Cloud Logging and Cloud Trace for end-to-end observability.

Answer

Cloud Security Command Center

Answer

BigQuery

Answer

Cloud Asset Inventory

Question 12

A company runs a mission-critical application that must be available 24/7. They want to ensure that if a Google Cloud region becomes unavailable (e.g., due to a natural disaster), the application automatically continues to serve users from another region. Which architecture pattern achieves this?

Accepted Answer

Deploy the application in multiple regions with a Global Load Balancer for automated failover.. Deploying the application in multiple regions behind a Global Load Balancer (GLB) enables automated failover. The GLB uses health checks to detect regional failures and routes traffic only to healthy backends, ensuring continuous availability even if an entire region goes down. This aligns with the requirement for a multi-region active-passive or active-active architecture for disaster recovery.

Answer

Deploy in a single region with a Managed Instance Group using 3 availability zones.

Answer

Enable Cloud Armor on the load balancer to protect against regional failures.

Answer

Use Cloud Storage multi-region buckets for application data.

Question 13

A company's application is composed of 15 microservices. When a performance issue occurs, the team struggles to determine which service is causing latency since request traces span multiple services. Which Google Cloud service helps identify which specific service in a microservices chain is causing slowdowns?

Accepted Answer

Cloud Trace — captures distributed request traces showing end-to-end latency across all microservices.. Cloud Trace is designed specifically for distributed tracing in microservices architectures. It captures end-to-end latency data for each request as it traverses multiple services, allowing you to pinpoint which service in the chain is introducing the most delay. This directly addresses the problem of identifying the specific service causing slowdowns in a 15-service application.

Answer

Cloud Logging — search logs for error messages across all 15 services.

Answer

Cloud Monitoring dashboards — create per-service CPU utilization graphs.

Answer

Security Command Center — scan for misconfigurations causing performance issues.

Question 14

What is the difference between a Service Level Indicator (SLI), a Service Level Objective (SLO), and a Service Level Agreement (SLA)?

Accepted Answer

SLI is the measured metric; SLO is the internal target for that metric; SLA is the contractual customer commitment.. It accurately defines the relationship: an SLI is a specific metric (e.g., request latency at the 99th percentile), an SLO is the internal target for that metric (e.g., 99.9% of requests under 200ms), and an SLA is the contractual commitment to a customer (e.g., 99.9% uptime with financial penalties). This aligns with Google Cloud's Site Reliability Engineering (SRE) practices, where SLIs are measured, SLOs are internal goals, and SLAs are legal agreements.

Answer

SLI is the contract with customers; SLO is the internal target; SLA is the measurement.

Answer

SLI, SLO, and SLA are all the same thing — different names for uptime guarantees.

Answer

SLA is measured in milliseconds; SLO is measured in percentage; SLI has no unit.

Question 15

After a major production outage, the engineering team conducts a review of what happened, why it happened, and how to prevent it in the future. This document is shared with all engineering teams. What is this practice called, and why does Google's SRE culture emphasize it?

Accepted Answer

Blameless postmortem — documenting the incident, root causes, and preventive actions to drive systemic learning without individual blame.. A blameless postmortem is a core SRE practice that focuses on documenting incidents, root causes, and preventive actions without assigning individual blame. Google's SRE culture emphasizes this to foster psychological safety, enabling teams to openly share failures and drive systemic improvements, which is essential for maintaining high reliability in large-scale distributed systems.

Answer

Performance review — identifying which engineers caused the outage for disciplinary action.

Answer

Capacity planning review — ensuring enough servers are provisioned to prevent future outages.

Answer

Change advisory board (CAB) review — approving that the outage fix is safe to deploy.