PCDOE Practice Test 3 — 10 Questions

Question 1

A company runs a critical application on Compute Engine instances behind a TCP/UDP Network Load Balancer. They notice intermittent high latency for a subset of users. The application logs show no errors, and instance CPU is below 50%. Which next step is most effective to diagnose the latency?

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

Accepted Answer

Analyze Cloud Monitoring metrics for the load balancer, including backend latency and request counts.. Cloud Monitoring provides detailed metrics for TCP/UDP Network Load Balancers, including backend latency and request counts, which directly help identify whether the latency originates from the load balancer itself or the backend instances. Since instance CPU is below 50% and application logs show no errors, the issue is likely at the network or load balancer level, and these metrics offer the most targeted diagnostic data without changing the architecture.

Answer

Increase the number of instances behind the load balancer.

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Enable VPC Flow Logs and analyze for dropped packets.

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Switch to an HTTP(S) Load Balancer for better visibility.

Question 2

A media streaming service uses Cloud Storage to store video files and serves them via Cloud CDN. Users in Asia report buffering issues. The team notices that the cache hit ratio is low in that region. The origin is a single Cloud Storage bucket in us-central1. Which set of actions would best improve performance for Asian users?

Accepted Answer

Create a new Cloud Storage bucket in an Asian region and use dual-region bucket with Cloud CDN.. Creating a new Cloud Storage bucket in an Asian region and using a dual-region bucket with Cloud CDN reduces latency by serving content from a geographically closer origin, improving cache hit ratios for Asian users. Cloud CDN caches content at edge locations, but if the origin is far away (us-central1), the first miss still incurs high latency. A dual-region bucket provides a local origin for cache misses, significantly reducing round-trip time.

Answer

Use Cloud Load Balancing with Cloud Armor to protect the origin.

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Enable HTTP/2 on Cloud CDN and increase the TTL for video content.

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Configure a custom domain on Cloud CDN with SSL and enable request collapsing.

Question 3

Which Google Cloud service provides a fully managed, private Git repository that integrates with Cloud Build for continuous integration?

Accepted Answer

Cloud Source Repositories. Cloud Source Repositories is the correct answer because it provides fully managed, private Git repositories hosted on Google Cloud. It integrates natively with Cloud Build, enabling automatic triggers for continuous integration (CI) pipelines whenever code is pushed to a repository branch or tag, without requiring external Git hosting.

Answer

Cloud Deployment Manager

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Cloud Storage

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Container Registry

Question 4

Which TWO of the following are essential elements of a comprehensive incident post-mortem document according to Google's Site Reliability Engineering (SRE) best practices?

Accepted Answer

A timeline of events leading to and during the incident.. A timeline of events is a core element of an incident post-mortem in Google SRE practice, as it provides a chronological reconstruction of the incident's progression, enabling teams to understand the sequence of failures and responses. This timeline is essential for identifying contributing factors and evaluating the effectiveness of mitigation actions, not just the root cause.

Answer

Exact code line numbers and commits that caused the incident.

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A detailed analysis of the root cause only.

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An attribution of blame to the individual or team responsible.

Question 5

Your team is running a high-traffic web application on Google Kubernetes Engine (GKE) and has configured Horizontal Pod Autoscaling (HPA) based on CPU utilization. Recently, the application experienced intermittent latency spikes during traffic bursts. You suspect that the HPA is not scaling quickly enough. Which TWO actions would most effectively improve the autoscaling responsiveness?

Accepted Answer

Configure a custom metric in HPA based on the application's request latency (e.g., p99 latency).. Using a custom metric based on request latency (e.g., p99 latency) allows HPA to react to application performance degradation directly, rather than relying solely on CPU utilization which may lag behind traffic bursts. This provides a more immediate signal for scaling, as latency spikes often precede CPU saturation in web applications. Option D is correct because reducing the --horizontal-pod-autoscaler-upscale-stabilization window (default 5 minutes) to a lower value like 30 seconds decreases the time HPA waits before acting on scale-up recommendations, enabling faster response to sudden load increases.

Answer

Increase the target CPU utilization percentage to 90% so that pods are less likely to be added.

Answer

Set the maxReplicas of the HPA to a value lower than the expected peak traffic to force faster scaling.

Answer

Increase the --horizontal-pod-autoscaler-sync-period flag to 30 seconds and increase the --horizontal-pod-autoscaler-upscale-delay flag to 5 minutes.

Question 6

A GKE cluster node fails, causing pods to be rescheduled. However, some pods remain in 'CrashLoopBackOff' state. After examining logs, you find the application has a dependency on local SSD that was ephemeral. What is the best long-term solution?

Accepted Answer

Use PersistentVolumes with ReadWriteOnce access mode.. The application's dependency on local SSD (ephemeral storage) means that when the node fails, the data is lost, causing the pods to crash. PersistentVolumes (PVs) with ReadWriteOnce (RWO) access mode provide durable, node-independent storage that survives node failures, ensuring pods can be rescheduled on any node and access their data. This is the best long-term solution because it decouples storage from the node lifecycle, preventing CrashLoopBackOff due to missing local data.

Answer

Configure pod anti-affinity.

Answer

Increase the node pool size.

Answer

Use a DaemonSet to run the application.

Question 7

A company uses BigQuery on-demand pricing. They want to reduce costs without changing usage patterns. Which THREE strategies are effective? (Choose three.)

Accepted Answer

Use clustered tables.. Partitioning and clustering reduce data scanned per query. Materialized views precompute results to reduce scanned data on repeated queries.

Answer

Use legacy SQL instead of standard SQL.

Answer

Use flat-rate pricing.

Question 8

A team uses Spanner for a global database. They notice increased read latency and high CPU utilization on some nodes. The workload is read-heavy with occasional writes. Which action is most likely to improve performance?

Accepted Answer

Add more nodes to the instance and ensure read requests are distributed evenly.. In a read-heavy Spanner workload with high CPU utilization on some nodes, adding more nodes and ensuring read requests are distributed evenly (Option D) directly addresses the bottleneck by increasing the instance's compute capacity and spreading the load across all nodes. Spanner's architecture uses a shared-nothing design where each node handles a portion of the data and traffic; uneven distribution can cause hot spots. Adding nodes scales out processing power, and ensuring even distribution (e.g., via proper key design or using Spanner's built-in load balancing) reduces latency and CPU spikes on individual nodes.

Answer

Create read-only replicas in each region.

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Split the most frequently read tables into smaller tables.

Answer

Increase the number of nodes in the instance.

Question 9

A team is troubleshooting a slow response time on an App Engine standard environment application. The application uses Cloud SQL as its database. Which TWO actions should the team take to identify the bottleneck?

Accepted Answer

Examine App Engine request logs for latency patterns.. Examining App Engine request logs reveals latency patterns, such as which endpoints or operations are slow, helping to pinpoint whether the bottleneck is in the application code, network, or database. Option C is correct because enabling Cloud SQL slow query logging identifies long-running SQL queries that could be causing database contention or inefficient data retrieval, directly addressing a common performance bottleneck in App Engine applications using Cloud SQL.

Answer

Increase the number of App Engine instances.

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Enable Cloud CDN to cache responses.

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Disable caching to ensure fresh data.

Question 10

A team uses Cloud SQL for PostgreSQL. They receive an alert that the database's CPU utilization is above 95% for the past 30 minutes. Queries are taking longer than usual. They want to investigate without causing further impact. What should they do first?

Accepted Answer

Use Cloud SQL Query Insights to find the most time-consuming queries. Cloud SQL Query Insights is a managed monitoring tool that automatically captures and analyzes query performance metrics, including CPU consumption, latency, and execution plans. In this scenario, it allows the team to identify the specific queries causing high CPU utilization without making any changes to the instance, thus avoiding further impact. This is the first and safest diagnostic step before any remediation.

Answer

Increase the number of vCPUs of the Cloud SQL instance

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Restart the Cloud SQL instance to clear the cache

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Migrate the database to Cloud Spanner