Cloud Digital Leader Practice Test 31 — 15 Questions

Question 1

A company migrated a microservices application to Google Kubernetes Engine (GKE). They set up an internal HTTP(S) load balancer to route traffic to the services. However, some pods are not receiving traffic. What is the most likely cause?

Accepted Answer

The pods have failing readiness probes that are preventing them from being added to the load balancer's backend endpoints.. Readiness probes determine whether a pod is ready to serve traffic. If they fail, the pod is removed from the endpoint list, causing no traffic. Firewall rules for internal load balancers are typically auto-configured.

Answer

The service type is NodePort instead of LoadBalancer.

Answer

The cluster does not have enough nodes to schedule the pods.

Answer

The firewall rules are not allowing traffic from the load balancer to the nodes.

Question 2

A startup wants to deploy a containerized web application without managing the underlying infrastructure. They want to only focus on code. Which Google Cloud service is most suitable?

Accepted Answer

Cloud Run. Cloud Run is the correct choice because it is a fully managed serverless platform that executes stateless containers in a request-driven environment, abstracting all infrastructure management. This allows the startup to focus solely on code by deploying container images directly from Artifact Registry or Container Registry, with automatic scaling down to zero when not in use.

Answer

Google Kubernetes Engine

Answer

Compute Engine

Answer

App Engine

Question 3

Refer to the exhibit. An operations team configured this Cloud Monitoring alert. They notice that the alert fires, but the associated managed instance group autoscaler does not scale up. What is the most likely reason?

Accepted Answer

The duration of 300 seconds (5 minutes) is too long, and the autoscaler uses a shorter evaluation period.. Option A is correct because the autoscaler evaluates metrics over a shorter period than the alert's 300-second duration. The alert fires only after the condition persists for 5 minutes, but the autoscaler may have already scaled down or the metric may have recovered before the alert triggers, causing a mismatch between the alert and the autoscaler's decision logic.

Answer

The threshold value of 0.8 is too low.

Answer

The alignment period of 60 seconds is too short, causing unstable metrics.

Answer

The filter is missing the project ID, so it applies to all projects.

Question 4

You are monitoring Compute Engine instances with Cloud Monitoring. You notice that autoscaling is not triggering even though CPU utilization is above 80% for several minutes. The managed instance group has autoscaling based on CPU utilization with a target of 0.8. What is the most likely cause?

Accepted Answer

The maximum number of instances is already reached.. The most likely cause is that the managed instance group has already reached its configured maximum number of instances. When the maximum instance count is hit, the autoscaler cannot add more instances even if CPU utilization exceeds the target of 0.8 (80%). This is a common boundary condition in autoscaling logic where the scaling policy is overridden by the hard limit.

Answer

The autoscaler is disabled.

Answer

The minimum number of instances is set too high.

Answer

The cool-down period is too long.

Question 5

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 8 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 8 nodes (each with 4 vCPUs) are near capacity. The Cluster Autoscaler cannot add more nodes because the node pool is capped at 10 nodes. Increasing the maximum number of nodes in the node pool (Option C) allows the Cluster Autoscaler to provision additional nodes to 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 6

Refer to the exhibit. A DevOps engineer notices that the alert fires even when there is only a single 5-second spike of errors that lasts for one minute. What is the most likely cause?

Accepted Answer

The trigger count is set to 1, so a single minute of high rate fires the alert. Option C is correct because the trigger count is 1, meaning the alert fires after just one alignment period (1 minute) with a rate above threshold. A single 5-second spike can cause a high rate during that minute, triggering the alert. Option A is incorrect because the alignment period is 60s, which is appropriate. Option B is incorrect because the threshold value is set, but the question is about the trigger firing on a single spike. Option D is incorrect because the trigger count is 1, not 2.

Answer

The alignment period is too short (60s)

Answer

The threshold value (5) is too low

Answer

The trigger count is set to 2, so two consecutive periods are needed

Question 7

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.

Answer

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

Answer

Use preemptible VMs for game server pods.

Question 8

A company runs a web application on Compute Engine. During seasonal sales, traffic spikes unpredictably. The operations team wants to ensure the application scales automatically without manual intervention while minimizing cost. Which solution should they implement?

Accepted Answer

Use a managed instance group with autoscaling based on CPU utilization.. A managed instance group (MIG) with autoscaling based on CPU utilization is the correct solution because it automatically adjusts the number of VM instances in response to real-time traffic spikes, ensuring the application scales out during high demand and scales in during low demand. This eliminates manual intervention and optimizes cost by only running the necessary number of instances based on a target CPU utilization threshold (e.g., 60-80%).

Answer

Create a managed instance group with a fixed number of instances.

Answer

Use an unmanaged instance group and manually add instances.

Answer

Use a single large VM with vertical scaling.

Question 9

Refer to the exhibit. A DevOps engineer wants to create a chart showing the rate of items sold per second over time. What is a limitation of this metric for that purpose?

Accepted Answer

The metric kind is GAUGE, so it cannot be used to calculate rate. Option A is correct because a GAUGE metric type represents a point-in-time value (e.g., current number of items), not a cumulative counter. To calculate a rate (items per second), you need a CUMULATIVE counter metric that monotonically increases, allowing Cloud Monitoring to compute the derivative over time. GAUGE metrics lack the necessary monotonicity and cumulative semantics, so they cannot be used to derive a meaningful rate of change.

Answer

The interval should include a startTime

Answer

The metric has no labels to filter

Answer

The value should be DOUBLE instead of INT64

Question 10

Which TWO are recommended practices when configuring autoscaling for Compute Engine managed instance groups?

Accepted Answer

Use a load balancer in front of the instance group.. Options B and D are correct. Setting min and max instances prevents over- and under-scaling. Using a load balancer distributes traffic to the group. Option A is wrong because multiple metrics are recommended. Option C is wrong because cooldown prevents thrashing. Option E is wrong because custom metrics can be combined with standard metrics.

Answer

Disable the cool-down period to react faster to spikes.

Answer

Use a single metric (e.g., CPU utilization) for simplicity.

Answer

Scale based on custom metrics only.

Question 11

An application deployed on Cloud Run is experiencing increased latency. The team suspects it's not scaling quickly enough. They have set a maxScale of 10 and minScale of 0. What should they adjust to reduce cold start latency?

Accepted Answer

Set minScale to 1 to keep at least one instance warm.. Option B is correct because setting a minScale greater than 0 keeps instances warm, reducing cold starts. Option A increases only the upper limit, which doesn't affect cold starts. Option C decreases concurrency, which may degrade throughput. Option D increases CPU per instance, not scaling behavior.

Answer

Decrease container concurrency.

Answer

Increase CPU limit.

Answer

Increase maxScale to 20.

Question 12

Which THREE components should a company include in their architecture to design a global web application with low latency for users worldwide?

Accepted Answer

Cloud CDN.. A Cloud CDN caches content at edge locations worldwide, reducing latency by serving users from a nearby point of presence (PoP). This is essential for a global web application because it minimizes the round-trip time for static and dynamic content, directly addressing the requirement for low latency across geographically distributed users.

Answer

Backend buckets for static content.

Answer

Regional internal load balancer.

Question 13

A company uses Cloud Monitoring to collect metrics from their applications running on Google Kubernetes Engine (GKE). They want to create custom dashboards and set up alerting policies. Which THREE capabilities are available in Cloud Monitoring? (Choose THREE.)

Accepted Answer

Define custom metrics via the Monitoring API. Option C is correct because the Cloud Monitoring API allows you to define and write custom metrics, which can then be used in dashboards and alerting policies. This is essential for capturing application-specific data that is not automatically collected by the default GKE integration, such as business KPIs or custom performance counters.

Answer

Query logs using Logging Query Language

Answer

Automatically remediate incidents with Cloud Functions

Question 14

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 15

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.. Option C is correct because 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.