PCDOE Practice Test 17 — 15 Questions

Question 1

A company uses BigQuery flat-rate pricing with 1000 slots. During peak hours, queries are queued, but during off-peak, many slots are idle. What is the most cost-effective way to handle the idle slots?

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

Accepted Answer

Use flex slots to add capacity only during peak. Flex slots allow you to add capacity temporarily during peak hours, avoiding the cost of idle slots during off-peak. Option A would increase idle slots and cost. Option B is wasteful because paying for idle slots is not cost-effective. Option D may increase costs for heavy usage due to per-scan pricing.

Answer

Purchase additional slots to reduce queuing

Answer

Do nothing; idle slots are already paid for

Answer

Change to on-demand pricing to pay only for data scanned

Question 2

Which Cloud Run setting controls the maximum number of requests a container can handle concurrently?

Accepted Answer

concurrency. The `concurrency` setting in Cloud Run defines the maximum number of simultaneous requests that a single container instance can process at any given time. This directly controls how many requests are routed to a single container before Cloud Run spins up additional instances, optimizing resource utilization and preventing overload.

Answer

timeout

Answer

max-instances

Answer

min-instances

Question 3

You are designing a monitoring strategy for a microservices application running on Google Kubernetes Engine (GKE). You need to create a custom metric that counts the number of failed login attempts from the application logs. The logs are in JSON format and contain a field 'status' with value 'FAILED'. Which approach should you use?

Accepted Answer

Configure a logs-based metric in Cloud Logging that filters for the condition and counts.. A logs-based metric in Cloud Logging directly counts occurrences of a specific log entry pattern (e.g., 'status' = 'FAILED') without requiring additional agents or data exports. This approach is purpose-built for deriving metrics from log data and integrates seamlessly with Cloud Monitoring for alerting and dashboards.

Answer

Use Cloud Monitoring's Metrics Explorer to create a metric from logs using a filter.

Answer

Install the Ops Agent on GKE nodes to collect application metrics directly.

Answer

Export logs to BigQuery and then create a custom metric from the exported data.

Question 4

You are using Cloud CDN with an external HTTPS load balancer. Users in Asia report slow load times for static assets. The origin is in us-central1. What should you do to improve performance?

Accepted Answer

Enable Cloud CDN and configure cache modes for static content.. Cloud CDN caches static assets at Google's global edge locations, reducing latency for users in Asia by serving content from a nearby point of presence instead of the us-central1 origin. Enabling Cloud CDN and configuring cache modes for static content (e.g., setting Cache-Control headers or using origin cache policies) ensures that frequently requested assets are served from cache, dramatically improving load times for geographically distant users.

Answer

Switch the load balancer to an internal HTTPS load balancer with gRPC.

Answer

Use premium tier networking for the load balancer.

Answer

Configure a serverless NEG to route traffic to Cloud Functions.

Question 5

Your organization is bootstrapping a new Google Cloud environment for a DevOps team. The team consists of 15 engineers who will be working on multiple microservices deployed across several projects. You have created a folder called 'devops' under the organization node. Within this folder, you plan to create three projects: 'devops-dev', 'devops-staging', and 'devops-prod'. You want to enforce that all resources in these projects are created in a specific region (us-central1) and that no external IP addresses can be assigned to Compute Engine instances. Additionally, you want to ensure that all service accounts used by the applications have minimal permissions. After setting up the organization policies, you notice that a developer was able to create a Compute Engine instance with an external IP in the 'devops-dev' project. You check the organization policy constraints and find that the constraint 'compute.vmExternalIpAccess' is set to 'Deny' at the organization level, but the developer bypassed it. What is the most likely reason?

Accepted Answer

The project 'devops-dev' has a policy that overrides the organization-level deny.. Organization policies can be overridden at a lower level in the resource hierarchy. Even though the constraint 'compute.vmExternalIpAccess' is set to 'Deny' at the organization level, a policy at the project level (or folder level) with a higher priority or a different binding can allow external IPs. In Google Cloud, organization policies are inherited by default, but a child policy can override the parent if it is explicitly set to 'Allow' or if the deny list is not enforced. The developer likely had a project-level policy that allowed external IPs, bypassing the organization-level deny.

Answer

The organization policy has not propagated to all projects yet.

Answer

The developer used the wrong constraint name; the correct constraint is 'compute.restrictExternalIp'.

Answer

The developer tagged the instance with a tag that exempts it from the organization policy.

Question 6

A team notices that Cloud SQL read replicas are not handling read traffic efficiently, causing high latency for read-heavy queries. What is the best approach to improve read performance?

Accepted Answer

Use Cloud Memorystore to cache frequent query results. Cloud Memorystore (Redis) caches the results of frequent read queries, reducing the load on Cloud SQL read replicas and lowering latency for repeated queries. This directly addresses the root cause of inefficient read traffic by serving cached data from in-memory storage, which is orders of magnitude faster than querying a replica. It is the best approach because it offloads read-heavy workloads without requiring additional replicas or relying on deprecated features like MySQL query cache.

Answer

Use a connection pooling proxy like ProxySQL

Answer

Enable MySQL query cache

Answer

Increase the number of read replicas

Question 7

Refer to the exhibit. A team uses these Compute Engine instances to run a batch processing job. The job frequently gets killed on instance-3. What is the most likely cause?

Accepted Answer

Instance-3 is preemptible and gets terminated by Google Cloud.. Instance-3 is a preemptible VM, which means Google Cloud can terminate it at any time if its resources are needed elsewhere. Preemptible instances have a maximum runtime of 24 hours and are subject to termination with a 30-second warning. The batch processing job being frequently killed on instance-3 is a classic symptom of preemptible VM termination, not a resource exhaustion or disk issue.

Answer

Instance-3 has insufficient CPU.

Answer

Instance-3 has a corrupted disk.

Answer

Instance-3 has insufficient memory.

Question 8

A DevOps team is setting up SLOs for a service with two critical metrics: availability and latency. They want to measure over a 30-day window. Which approach correctly defines an SLO?

Accepted Answer

Use Cloud Monitoring to create a custom SLI based on request latency metrics and set an SLO with a 30-day rolling window. It uses Cloud Monitoring to create a custom SLI based on request latency metrics, which is a valid SLI type, and sets an SLO with a 30-day rolling window, matching the requirement. Cloud Monitoring's SLO feature natively supports rolling windows, and latency is a standard metric for defining service-level objectives.

Answer

Use Cloud Monitoring to create a custom SLI based on logs and set an SLO with a 7-day rolling window

Answer

Use Cloud Tasks to schedule a cron job that calculates availability

Answer

Use Stackdriver Monitoring (deprecated) to set an SLO with a fixed 30-day window

Question 9

When bootstrapping a Google Cloud organization for DevOps, which THREE steps are essential to set up a secure CI/CD foundation using Cloud Build?

Accepted Answer

Create a Cloud Source Repository for each application's code.. Creating a Cloud Source Repository for each application's code is essential because it provides a dedicated, version-controlled repository that integrates natively with Cloud Build triggers. This ensures that each application has its own isolated codebase, enabling precise CI/CD pipelines without cross-application interference.

Answer

Disable default service accounts in all projects.

Answer

Configure VPC Service Controls for all projects.

Question 10

Refer to the exhibit. The output shows a recommendation from the Cloud Cost Optimization recommender for an instance in us-central1-a. The instance is a production web server that consistently runs at 25% CPU utilization during peak hours. What should the DevOps engineer do to implement this recommendation with minimal risk?

Accepted Answer

Stop the instance, change the machine type to n2-standard-4, and start it again.. The Cloud Cost Optimization recommender has identified that the current instance is over-provisioned for its actual workload (25% CPU utilization during peak hours). By stopping the instance, changing the machine type to n2-standard-4 (which has 4 vCPUs and 16 GB RAM, matching the recommended 4 vCPUs and 15 GB memory), and starting it again, the engineer rightsizes the instance without incurring downtime during the change itself. This approach minimizes risk because the instance is stopped only briefly for the machine type change, and the new machine type is a standard, well-tested configuration that aligns with the recommender's analysis.

Answer

Ignore the recommendation because the instance is production and any change might cause downtime.

Answer

Add a second n2-standard-4 instance behind a load balancer to distribute load.

Answer

Use a custom machine type with 4 vCPUs and 32 GB memory to ensure enough RAM.

Question 11

You are debugging a production issue where a Cloud Function occasionally throws a 'memory limit exceeded' error. You want to inspect the memory usage at the time of the error. What should you do?

Accepted Answer

Enable Cloud Profiler and analyze the snapshot.. Cloud Profiler provides continuous, low-overhead profiling of CPU and memory usage, and its snapshot analysis can pinpoint memory allocation patterns at the time of a memory limit exceeded error. Unlike other tools, Profiler captures the call stack and memory consumption per function invocation, enabling you to identify the specific code path causing the spike.

Answer

Check Cloud Logging for memory metrics.

Answer

Use Cloud Trace to trace the invocations.

Answer

Use Cloud Debugger to set a breakpoint.

Question 12

A team wants to monitor a Google Cloud Run service for application crashes. Which Google Cloud tool automatically captures and notifies on application errors?

Accepted Answer

Error Reporting. Error Reporting (D) is the correct answer because it is a Google Cloud service specifically designed to automatically capture, aggregate, and notify on application errors, including crashes in Cloud Run services. It ingests error events from Cloud Logging and provides real-time alerts and dashboards, making it the dedicated tool for this use case.

Answer

Cloud Logging

Answer

Cloud Monitoring

Answer

Cloud Console

Question 13

Refer to the exhibit. What is the effect of the metricRelabelings section in this ServiceMonitor?

Accepted Answer

It drops all metrics whose name starts with 'container_'.. The `metricRelabelings` section in a ServiceMonitor uses Prometheus relabeling rules to modify or filter metrics before ingestion. The `action: drop` directive, combined with a `regex: 'container_.*'` pattern matching the metric name, causes all metrics whose names start with 'container_' to be dropped (i.e., not scraped). This is a common pattern to exclude unwanted metrics and reduce cardinality.

Answer

It adds a label called 'container_' to all metrics.

Answer

It only keeps metrics whose name starts with 'container_'.

Answer

It renames all metrics with the prefix 'container_' to remove the prefix.

Question 14

Which TWO are best practices for implementing CI/CD on Google Cloud?

Accepted Answer

Use Artifact Registry for storing container images.. Artifact Registry is the recommended service for storing, managing, and securing container images and other artifacts in Google Cloud. It integrates natively with Cloud Build, Cloud Run, and Kubernetes, providing vulnerability scanning and IAM-based access control, which are essential for a secure CI/CD pipeline.

Answer

Use Cloud Run for all services.

Answer

Use Cloud Build for all deployments, including infrastructure changes.

Answer

Use GitHub Actions instead of Cloud Build.

Question 15

Your team deploys a microservice on Google Kubernetes Engine (GKE) that serves an API with low latency requirements. Users report that the API occasionally times out during peak hours. You check the GKE metrics and see that CPU utilization is below 50% but memory is near 100% on the nodes. What is the most likely cause and what should you do?

Accepted Answer

The application is memory-constrained; increase memory resource limits for the pod.. The symptoms—low CPU utilization but near 100% memory usage on nodes, with API timeouts during peak hours—indicate that the application is hitting memory limits. When a pod exceeds its memory request, the kernel can OOM-kill it, causing request failures and timeouts. Increasing the memory resource limits for the pod allows it to allocate more heap or cache, preventing OOM kills and stabilizing latency.

Answer

The nodes are under-provisioned; add more nodes to the cluster.

Answer

The application is CPU-bound; increase CPU resource limits for the pod.

Answer

The network bandwidth is insufficient; increase the machine type for nodes.