PCDOE Managing Google Cloud costs — Timed 10 Questions

Question 1

A company is using Google Kubernetes Engine (GKE) with multiple node pools. They notice that their monthly costs are higher than expected. Upon review, they find that several preemptible VMs are being recreated frequently, leading to sustained usage costs. What is the most cost-effective solution to reduce costs?

Accepted Answer

Migrate to Spot VMs, which have a lower price and no maximum runtime.. Spot VMs are the recommended replacement for preemptible VMs, offering the same low price but without the 24-hour maximum runtime limit. This eliminates the frequent recreation and sustained usage costs caused by preemptible VMs being terminated and restarted, directly reducing monthly expenses.

Answer

Purchase committed use discounts for the preemptible VMs.

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Increase the number of preemptible VMs to spread the workload.

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Enable sustained use discounts for the existing VMs.

Question 2

A company runs a batch processing workload on Compute Engine that runs for 3 hours every night. They want to minimize costs while ensuring the job completes reliably. Which recommendation should they follow?

Accepted Answer

Use preemptible VMs and design the job to handle interruptions gracefully.. Preemptible VMs cost about 60-80% less than standard VMs and are ideal for batch workloads that can tolerate interruptions. Since the job runs for only 3 hours nightly, it can be designed to checkpoint progress and restart from the last checkpoint if a preemptible VM is terminated (which can happen at any time within 24 hours). This minimizes cost while ensuring reliability through graceful interruption handling.

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Use sole-tenant nodes to isolate the workload.

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Use standard (on-demand) VMs and enable sustained use discounts.

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Purchase 1-year committed use discounts for the VMs.

Question 3

A company uses Cloud Storage to store archival data. They want to minimize storage costs while maintaining availability. Which storage class should they use?

Accepted Answer

Archive storage class.. The Archive storage class is the correct choice because it offers the lowest storage cost for archival data that is accessed less than once per year, with a 365-day minimum storage duration and retrieval costs that are higher than other classes. This aligns with the requirement to minimize storage costs while maintaining availability, as Archive data is still available for retrieval (though with a longer latency) and is replicated for durability.

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Nearline storage class.

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Standard storage class.

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Coldline storage class.

Question 4

A company is using BigQuery for analytics and wants to control costs. They have many queries that scan large amounts of data. Which approach is most effective in reducing query costs?

Accepted Answer

Partition tables by date and use partition pruning in queries.. Partitioning tables by date and using partition pruning in queries directly reduces the amount of data scanned by BigQuery, which is the primary driver of on-demand query costs. By filtering on the partition column, BigQuery can skip entire partitions that do not match the query criteria, minimizing the bytes processed. This is the most effective cost-control measure because it addresses the root cause of high costs—excessive data scanning—without requiring a pricing model change or additional resource commitments.

Answer

Switch to flat-rate pricing to cap costs.

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Reserve BigQuery slots for dedicated capacity.

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Use clustering to organize data within partitions.

Question 5

A company uses Cloud CDN to deliver content globally. They notice increasing egress costs. Which change will most effectively reduce egress costs?

Accepted Answer

Configure Cloud CDN to cache more content and increase cache hit ratio.. Increasing the cache hit ratio reduces the number of requests that reach the origin server, which directly lowers the volume of data transferred from the origin to the CDN edge. Since egress costs are primarily driven by data served from the CDN edges to users, caching more content at the edge minimizes the need to fetch and serve data from the origin, thereby reducing overall egress traffic and associated costs.

Answer

Switch to a premium tier network for lower egress rates.

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Enable gzip compression for all responses.

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Use Cloud Armor to block malicious traffic.

Question 6

A company is using Google Cloud and wants to monitor and control costs. Which TWO actions should they take? (Choose two.)

Accepted Answer

Set up budget alerts to notify when spending exceeds thresholds.. Setting up budget alerts (option A) is a core cost control action because it allows you to define a budget amount and receive notifications when actual or forecasted spending exceeds a specified threshold (e.g., 50%, 90%, 100%). This enables proactive intervention before costs spiral out of control. Using labels (option B) to categorize resources (e.g., by team, project, or environment) allows you to track and allocate costs accurately in billing reports, making it easier to identify cost drivers and enforce accountability.

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Disable all unnecessary APIs at the organization level.

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Export billing data to BigQuery for detailed analysis.

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Grant billing account access to all project owners.

Question 7

A company runs a web application on Google Kubernetes Engine (GKE) with multiple services. They want to reduce costs without impacting performance. Which THREE actions should they take? (Choose three.)

Accepted Answer

Enable node auto-provisioning to automatically adjust node pools.. Option C is correct because node auto-provisioning in GKE automatically creates and scales node pools based on the resource requirements of pending pods. This eliminates the need for manual node pool management and ensures that only the necessary compute resources are provisioned, reducing costs without manual intervention or over-provisioning.

Answer

Enable cluster autoscaling and manually scale nodes based on peak load.

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Deploy a service mesh like Istio to optimize traffic routing.

Question 8

You are a DevOps engineer at a large e-commerce company that runs its production workloads on Google Kubernetes Engine (GKE) in the us-central1 region. The cluster has 500 nodes, each with 8 vCPUs and 32 GB of memory, and uses preemptible VMs for cost savings. Over the past month, the monthly GKE cost has increased by 30% unexpectedly. Upon reviewing the billing reports, you notice a significant spike in Compute Engine costs, specifically for 'Sustained Use Discount' line items, but the total cost is higher than expected. You also observe that the cluster's node utilization is inconsistent, with some nodes running at 90% CPU and memory while others are below 20%. Your team has been deploying stateless microservices and using Cluster Autoscaler with default settings. The application traffic is variable but predictable, with peaks on weekends. You need to reduce the GKE costs without impacting performance. What should you do?

Accepted Answer

Enable node auto-provisioning and migrate baseline workloads to nodes covered by committed use discounts (1-year or 3-year).. Option A is correct because enabling node auto-provisioning allows GKE to automatically select the most cost-effective node configurations for your workloads, reducing waste from over-provisioned nodes. Migrating baseline workloads to nodes covered by committed use discounts (CUDs) locks in lower prices for predictable usage, directly addressing the 30% cost spike caused by inconsistent utilization and unexpected sustained use discount charges. This combination optimizes both variable and steady-state workloads without sacrificing performance.

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Increase the minimum number of nodes in the cluster to 300 and use a larger machine type to reduce the number of pods per node.

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Switch the cluster to a single-zone configuration and reduce the number of nodes to 200 to lower base costs.

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Reduce the number of preemptible VMs to 30% and use only on-demand VMs for the remaining nodes to improve reliability.

Question 9

A company runs a critical application on Google Kubernetes Engine (GKE) with 3 nodes (e2-standard-4). To reduce costs, the team is considering right-sizing the nodes. The application is latency-sensitive and experiences periodic traffic spikes. What is the most cost-effective approach that maintains performance during spikes?

Accepted Answer

Create a node pool with smaller nodes (e2-standard-2) using preemptible VMs and enable cluster autoscaler.. Option C is the most cost-effective approach because it uses smaller e2-standard-2 nodes with preemptible VMs, which are significantly cheaper than regular VMs, and combines this with the cluster autoscaler to automatically add nodes during traffic spikes. This ensures that the latency-sensitive application maintains performance by scaling out horizontally when needed, while minimizing baseline costs with smaller, cheaper nodes.

Answer

Use a single larger node (n2-standard-8) to reduce node count and network overhead.

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Switch to 3 n2-standard-2 nodes to reduce vCPU and memory, and rely on horizontal pod autoscaling.

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Keep current node size but use committed use discounts for 1 year to reduce per-hour cost.

Question 10

A DevOps team is analyzing Google Cloud costs and notices that spending on BigQuery has increased significantly. They want to reduce costs without impacting ongoing analytical workloads. Which TWO actions should they take? (Choose two.)

Accepted Answer

Set custom cost controls like query quotas and maximum bytes billed per query.. Option C is correct because BigQuery allows you to set custom cost controls such as query quotas (e.g., concurrent queries per project) and maximum bytes billed per query. These controls cap resource usage at the query level, preventing runaway costs while still allowing analytical workloads to run within defined limits. This directly addresses cost spikes without blocking ongoing operations.

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Switch to on-demand pricing to pay only for queries run.

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Enable column-level security to restrict access to sensitive data.

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Delete unused datasets to reduce storage costs.