Cloud Digital Leader Scaling with Google Cloud operations Practice Test 7 — 15 Questions

Question 1

A company has a stateful application running on Compute Engine. They want to scale horizontally while preserving state. Which configuration should they use?

Accepted Answer

Managed instance group with stateful configuration.. A managed instance group (MIG) with stateful configuration preserves instance-specific state (such as disks, hostnames, and metadata) across autohealing and rolling updates. This allows the stateful application to scale horizontally while maintaining its persistent data, as each instance retains its unique state even when the group is resized or instances are recreated.

Answer

Use Cloud Run with volumes.

Answer

Unmanaged instance group.

Answer

Managed instance group with autoscaling and no stateful configuration.

Question 2

A team deploys microservices on GKE with Horizontal Pod Autoscaler (HPA). They want to scale based on custom metrics from third-party monitoring. What must they do first?

Accepted Answer

Install the custom metrics API adapter.. B is correct because Horizontal Pod Autoscaler (HPA) in GKE relies on the custom.metrics.k8s.io API to retrieve custom metrics from external monitoring systems. To expose these metrics to the HPA, you must install a custom metrics API adapter (e.g., the Prometheus Adapter or Google Cloud's custom-metrics-stackdriver-adapter) that translates the third-party monitoring data into the format the Kubernetes API server expects. Without this adapter, the HPA cannot query the custom metrics and will fail to scale.

Answer

Use Cluster Autoscaler.

Answer

Enable Cloud Monitoring and configure custom metrics.

Answer

Use Vertical Pod Autoscaler.

Question 3

A company uses Cloud Functions (2nd gen) to process events from Pub/Sub. During traffic spikes, function instances scale but latency increases. They want to maximize throughput per instance. What should they configure?

Accepted Answer

Increase the concurrency setting.. Increasing the concurrency setting allows each Cloud Functions (2nd gen) instance to handle multiple requests simultaneously, maximizing throughput per instance during traffic spikes. By default, concurrency is 1, meaning each instance processes one event at a time; raising this value enables parallel processing within a single instance, reducing the need to scale out and lowering latency.

Answer

Allocate more memory.

Answer

Increase the max instances limit.

Answer

Increase the function timeout.

Question 4

Which TWO statements correctly describe Cloud Run scaling behavior?

Accepted Answer

You can define a target concurrency to control how many requests each container instance handles.. Cloud Run allows you to set a target concurrency (the number of simultaneous requests a single container instance can handle). This is a key scaling parameter that controls how many requests are routed to each instance before Cloud Run spins up additional instances. By default, concurrency is set to 80, but you can adjust it up to 1000 or set it to 1 for sequential processing.

Answer

The maximum number of instances can be set to 'default' which is unlimited.

Answer

You can set a minimum number of instances to ensure zero cold starts.

Answer

Autoscaling uses CPU and memory utilization to make decisions.

Question 5

Refer to the exhibit. A team deployed this Cloud Run service. During a load test, the service receives high traffic, but the number of container instances never exceeds 10. What is the most likely cause?

Accepted Answer

The maxScale annotation limits the maximum number of instances to 10.. The `maxScale` annotation in Cloud Run directly caps the maximum number of container instances that can be created. When the service receives high traffic but never exceeds 10 instances, it indicates that the `maxScale` annotation is set to 10, preventing further scaling even if demand increases. This is the most direct and likely cause among the options.

Answer

The minScale of 2 forces at least two instances, but not the max.

Answer

The containerConcurrency of 80 limits the number of concurrent requests per instance.

Answer

The CPU limit of 1 vCPU is too low to handle the traffic.

Question 6

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?

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 7

A company runs a web application on Google Kubernetes Engine (GKE) that experiences sudden traffic spikes. The operations team notices that the application's response time increases significantly during these spikes despite having Horizontal Pod Autoscaler (HPA) configured. They want to ensure consistent performance. What should they do?

Accepted Answer

Configure the HPA to use custom metrics based on request latency.. Configuring the HPA to use custom metrics based on request latency allows the autoscaler to react directly to the application's performance degradation. Unlike CPU-based metrics, which may not reflect actual user-facing latency during traffic spikes, custom metrics like request latency provide a more accurate signal for scaling decisions, ensuring consistent response times.

Answer

Increase the CPU request limit for all pods.

Answer

Create multiple node pools with different machine types.

Answer

Manually scale the deployment during expected spikes.

Question 8

A financial services company is migrating its on-premises monitoring system to Google Cloud. They need to collect metrics, logs, and traces from multiple projects and provide a unified view for their operations team. Security requires that logs containing sensitive data be stored with additional encryption and access controls. Which combination of services should they use?

Accepted Answer

Cloud Monitoring, Cloud Logging, and Cloud Trace with a custom sink to a BigQuery dataset that uses CMEK.. The company needs to collect metrics, logs, and traces (requiring Cloud Monitoring, Cloud Logging, and Cloud Trace) and must store logs containing sensitive data with additional encryption and access controls. A custom sink to BigQuery with CMEK provides customer-managed encryption keys for the BigQuery dataset, and BigQuery's native access controls (IAM, row-level security) satisfy the requirement for additional access controls beyond the default Logging buckets.

Answer

Cloud Monitoring, Cloud Logging, and Cloud Trace with Logging's _Required and _Default buckets.

Answer

Cloud Monitoring and Cloud Logging with Log Analytics.

Answer

Cloud Monitoring, Cloud Logging, and Cloud Trace with Cloud Audit Logs.

Question 9

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.. Option D is correct because checking CPU utilization of the instance group in Cloud Monitoring directly tests the hypothesis that the instances are overwhelmed; sustained high CPU across the MIG is the key signal of saturation and helps confirm whether the error rate spike is caused by insufficient compute capacity. Option E is correct because reviewing the load balancer logs in Cloud Logging exposes the actual HTTP error codes and messages (for example 502/503 responses or backend timeouts), which distinguishes backend overload from other causes such as misconfigured health checks or client errors. Option A is not appropriate because switching to a Network Load Balancer is a design change, not a troubleshooting step, and it would not diagnose why the current load balancer is reporting errors. Option B is not appropriate because blindly increasing the size of the instance group without investigation may mask the symptom, waste resources, and leave the root cause unidentified. Option C is not appropriate because enabling HTTP health checks is a configuration change rather than a diagnostic action, and health checks alone would not reveal the cause of the elevated error rates.

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 10

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.. Option A is correct because committed use discounts (CUDs) provide significant discounts (up to 57% for 3-year commitments) on sustained GKE compute usage, which is ideal for a real-time game server that runs continuously and predictably. Option C is correct because node auto-provisioning dynamically creates node pools based on pending pod resource requests, avoiding over-provisioning and reducing costs while still meeting the latency needs of game server pods. Option D is correct because deploying GKE clusters in multiple regions and using Multi Cluster Ingress (MCI) places players on the nearest healthy cluster, minimizing latency across regions while also improving availability. Option B is not appropriate because spot VMs can be preempted at any time, which would abruptly terminate real-time game sessions and harm player experience. Option E is likewise incorrect because preemptible VMs are short-lived (max 24 hours) and can be reclaimed with only 30 seconds' notice, making them unsuitable for latency-sensitive, stateful multiplayer game servers.

Answer

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

Answer

Use preemptible VMs for game server pods.

Question 11

A large enterprise runs a critical application on Google Cloud consisting of Compute Engine instances behind a TCP load balancer. The application experiences intermittent slow response times that last for about 10 minutes before returning to normal. This pattern has been occurring every few days at random times. The operations team has configured Cloud Monitoring alerts for CPU and memory, but no alerts have fired. They have also reviewed the load balancer logs and see no errors, but the latency spikes. The application logs show no errors during these periods. The team suspects a resource bottleneck but cannot find it. Further investigation reveals that the application makes synchronous calls to an external authentication service for each request. What is the most likely cause and corrective action?

Accepted Answer

The application is making synchronous calls to an external authentication service that occasionally has latency spikes; implement caching and asynchronous processing.. The intermittent latency spikes lasting ~10 minutes, with no errors in application or load balancer logs and no CPU/memory alerts, point to an external dependency issue. The synchronous calls to the external authentication service are the likely bottleneck: if that service experiences transient latency, every request is blocked, causing the application's response time to spike. Caching authentication tokens and using asynchronous processing (e.g., a queue or background refresh) decouples the application from the external service's variability, eliminating the cascading latency.

Answer

The TCP load balancer is experiencing connection draining issues; switch to a proxy-based load balancer.

Answer

The instance group's autoscaler is configured with a cooldown period that is too long; reduce the cooldown period.

Answer

The virtual machine instances are suffering from CPU throttling due to sustained use of burstable CPU; move to a machine type with more CPUs.

Question 12

A cloud operations engineer notices that the managed instance group 'my-mig' has been scaling up frequently, but the application performance is still degraded. The CPU utilization metric shows high values. What is most likely the issue?

Accepted Answer

The scaling metric is not appropriate; consider using a custom metric that better reflects application load.. The autoscaler is using CPU utilization as the scaling metric, but high CPU does not necessarily correlate with application performance degradation. If the application is bottlenecked on memory, I/O, or request queuing, CPU may remain high while throughput suffers. A custom metric (e.g., requests per second, latency, or queue depth) would better reflect actual application load and enable more accurate scaling decisions.

Answer

The target size is set to 10, which is lower than the current needed capacity.

Answer

The instance group is using preemptible VMs which are being reclaimed frequently.

Answer

The autoscaler is using a cooldown period that is too long, preventing it from scaling down.

Question 13

A large online retailer operates a microservices-based e-commerce platform on Google Kubernetes Engine (GKE) across multiple zones. The application consists of several stateless services that handle customer traffic, inventory, and order processing. Recently, the company migrated its relational database to Cloud Spanner to achieve global scalability and strong consistency. After the migration, during peak shopping periods (e.g., Black Friday), the application experiences significant performance degradation. The operations team monitors CPU utilization of the pods and finds it consistently below 60% even under heavy load. However, Cloud Spanner metrics show high query latency and increased number of transactions waiting for lock conflicts. The team suspects that the bottleneck is now the database, not the compute. The application is designed to scale horizontally by adding more pod replicas. The team wants to ensure that scaling decisions are based on the actual performance bottleneck. What should they do?

Accepted Answer

Modify the Horizontal Pod Autoscaler (HPA) to scale based on a custom metric that reflects Cloud Spanner query latency.. The Horizontal Pod Autoscaler (HPA) can be configured to scale based on custom metrics, such as Cloud Spanner query latency. Since the bottleneck is the database, scaling pods based on CPU utilization (which remains low) would not resolve the issue; instead, scaling based on Spanner latency ensures that the application adds replicas only when the database can handle more connections, reducing lock contention and improving overall performance.

Answer

Scale the GKE cluster to use larger node instances.

Answer

Increase the CPU request limit for the pods to allow higher CPU usage.

Answer

Reduce the number of pods to decrease Spanner load.

Question 14

A retail company runs its order-processing application on Compute Engine instances in a single zone. During a regional maintenance event, the zone becomes unavailable for several hours, and the application goes down. The operations team wants to improve resilience so that a single zone failure does not cause an outage, while keeping changes minimal. What should they do?

Accepted Answer

Create a managed instance group with instances spread across multiple zones in the same region, and configure an HTTP(S) load balancer to distribute traffic.. The most effective way to survive a single-zone failure is to distribute instances across multiple zones within a region using a managed instance group, and to use a load balancer to route traffic to healthy instances. This design removes the single point of failure and aligns with Google Cloud's regional resource model. Other options either do not address zone-level failures or add unnecessary components.

Answer

Enable live migration for all instances so that they automatically move to another zone when the current zone fails.

Answer

Configure a Cloud VPN tunnel between the affected zone and a second zone, and replicate the application state over that tunnel.

Answer

Move the application to a single, larger instance with a higher availability SLA so that it is less likely to fail.

Question 15

A startup's engineering team wants to monitor the CPU utilization of its Compute Engine instances and receive an alert when utilization exceeds 80% for 5 minutes. They want a fully managed Google Cloud solution that requires minimal setup and integrates with their existing dashboards. Which Google Cloud service should they use?

Accepted Answer

Cloud Monitoring. Cloud Monitoring is the Google Cloud service for collecting metrics, creating dashboards, and setting up alerts. It automatically collects CPU utilization from Compute Engine instances and allows you to define an alerting policy that triggers when the metric exceeds a threshold for a specified duration. This matches the team's need for a managed, low-effort monitoring solution.

Answer

Cloud Logging

Answer

Cloud Profiler

Answer

Cloud Trace