PCDOE Optimizing service performance Practice Test 8 — 15 Questions

Question 1

A backend service receives bursts of requests that cause timeouts. The team wants to smooth out the load while ensuring all requests are processed eventually. Which strategy should they use?

Accepted Answer

Use Cloud Tasks to queue incoming requests and process at a controlled rate. Cloud Tasks is designed to decouple request processing by queuing incoming requests and then dispatching them to a target handler at a controlled rate. This allows the backend to process requests smoothly, preventing timeouts during bursts, while ensuring every request is eventually processed through retry and dead-letter mechanisms.

Answer

Implement client-side rate limiting

Answer

Use Cloud Load Balancing with connection draining

Answer

Increase the number of backend instances

Question 2

A team deploys a Cloud Function that processes user requests. They notice cold starts cause high latency for the first request after a period of inactivity. What is the most effective way to reduce cold starts?

Accepted Answer

Set the minimum instances to 1. Setting minimum instances to 1 pre-warms a function instance, keeping it idle and ready to serve requests immediately. This eliminates the cold start latency for the first request after inactivity because the runtime environment is already initialized and loaded into memory.

Answer

Use a larger function timeout

Answer

Increase the memory allocation

Answer

Deploy the function in multiple regions

Question 3

A team is running a stateful application on Compute Engine with high disk I/O. They want to optimize disk performance. Which TWO actions should they take? (Choose two.)

Accepted Answer

Use SSD persistent disk for data. Option C is correct because SSD persistent disks deliver significantly higher IOPS and lower latency than standard persistent disks, which directly addresses the high disk I/O requirement of the stateful application. Option D is correct because local SSDs provide the highest IOPS and lowest latency of any Compute Engine disk option, making them ideal for temporary, high-throughput data such as caches or scratch space. Option A is incorrect because standard persistent disks are HDD-backed and offer much lower IOPS, which would worsen performance for an I/O-intensive workload. Option B is incorrect because disk encryption is a security feature and does not improve disk I/O performance. Option E is incorrect because increasing disk size can raise IOPS and throughput limits on persistent disks, but it is not one of the two best actions for optimizing performance compared to choosing SSD persistent disks and local SSDs.

Answer

Use standard persistent disk for cost savings

Answer

Enable disk encryption

Answer

Increase disk size

Question 4

An application running on GKE experiences high tail latency. The team is optimizing performance. Which THREE techniques should they consider? (Choose three.)

Accepted Answer

Use pod anti-affinity to spread pods across nodes. Option A is correct because pod anti-affinity spreads replicas across different nodes, preventing multiple latency-sensitive pods from competing for the same node's CPU, memory, and network resources, which reduces tail latency caused by noisy-neighbor effects. Option B is correct because setting accurate resource requests and limits ensures pods get guaranteed CPU/memory and are not throttled or evicted, directly avoiding resource contention that inflates tail latency. Option E is correct because scaling stateless services horizontally with more replicas distributes traffic and load, reducing per-pod queueing and improving tail latency under high concurrency. Option C is not correct because injecting an Istio sidecar adds a proxy hop and overhead that can increase latency rather than reduce it, and it is not a performance optimization technique. Option D is not correct because pod affinity packs pods onto the same node, which increases resource contention and worsens tail latency, the opposite of what is needed.

Answer

Enable Istio sidecar injection for all pods

Answer

Use pod affinity to pack pods on same node

Question 5

A team uses Cloud Load Balancing with backend NEGs. Users report intermittent high latency. How should they diagnose the root cause effectively?

Accepted Answer

Use Cloud Trace to analyze per-request latency spans. Cloud Trace provides end-to-end latency analysis by capturing per-request spans as they traverse the load balancer, backend NEGs, and other services. This allows you to pinpoint exactly which hop (e.g., load balancer processing, backend queuing, or application code) is causing the intermittent high latency, rather than relying on aggregate metrics or caching assumptions.

Answer

Increase the number of backend instances immediately

Answer

Enable Cloud Monitoring latency histogram for the load balancer

Answer

Check Cloud CDN cache hit ratio

Question 6

An application running on App Engine standard environment has high instance startup latency, leading to slow first requests. What is the most effective configuration change to reduce cold starts?

Accepted Answer

Set min_idle_instances to a value greater than 0. Setting min_idle_instances to a value greater than 0 ensures that a baseline number of instances are always running and ready to serve requests, eliminating the need to spin up new instances on the first request. This directly reduces cold start latency because the application does not have to wait for instance initialization, which includes loading dependencies and running startup scripts.

Answer

Use manual scaling instead of automatic scaling

Answer

Use Cloud Endpoints for request authentication

Answer

Increase the memory limit per instance

Question 7

A Cloud Run service experiences high cold start latency. The team has already set min-instances to 1. Which additional optimization can further reduce cold start impact?

Accepted Answer

Reduce container image size and enable CPU boost during startup. Reducing the container image size decreases the time required to pull and unpack the container on a cold start, and enabling CPU boost during startup temporarily allocates additional CPU resources to accelerate the initialization process. Together, these directly address the root causes of cold start latency by minimizing both the image loading time and the application initialization time, even when min-instances is already set to 1.

Answer

Use HTTP/2 to speed up request handling

Answer

Place the service on Cloud Run for Anthos on GKE

Answer

Increase the container memory limit

Question 8

A latency-sensitive web application uses Cloud CDN. What configuration change would most directly reduce cache miss rates?

Accepted Answer

Enable cache static content and set appropriate cache modes. Enabling cache static content and setting appropriate cache modes directly reduces cache miss rates by ensuring that more requests are served from the Cloud CDN cache rather than forwarded to the origin. This configuration allows the CDN to store and serve static assets (e.g., images, CSS, JavaScript) based on cache keys and TTLs, minimizing the number of cache misses for eligible content.

Answer

Enable Cloud Armor to filter malicious traffic

Answer

Use signed URLs to restrict access

Answer

Set TTL to 0 to ensure content is always fresh

Question 9

A large stateful service running on Compute Engine experiences variable performance due to CPU throttling from noisy neighbors. Which solution provides the most consistent performance?

Accepted Answer

Use sole-tenant nodes to isolate the VMs. Sole-tenant nodes ensure that your VMs are the only ones running on the underlying physical server, eliminating resource contention from other tenants (noisy neighbors). This provides consistent CPU performance because the vCPUs are not oversubscribed and the full physical core capacity is dedicated to your instances.

Answer

Enable live migration for the VMs

Answer

Use preemptible VMs for stateful workloads

Answer

Purchase committed use discounts for lower cost

Question 10

A batch data processing job on Cloud Dataflow is running slower than expected. Which action will most directly increase throughput?

Accepted Answer

Increase the number of workers in the pipeline. Increasing the number of workers directly increases the parallelism of the pipeline, allowing more data to be processed concurrently. In Cloud Dataflow, throughput is limited by the number of available worker slots; adding workers raises the total processing capacity. This is the most direct action to increase throughput when the pipeline is CPU-bound or I/O-bound and underutilizing existing resources.

Answer

Enable Streaming Engine for the pipeline

Answer

Enable autoscaling

Answer

Use FlexRS pricing model

Question 11

A web application experiences high latency during peak hours. Which TWO actions should the team take to optimize performance?

Accepted Answer

Implement autoscaling based on CPU utilization. Option A is correct because implementing autoscaling based on CPU utilization allows the application to dynamically add instances during peak hours, distributing the load and reducing latency caused by resource saturation. Option C is correct because Cloud Memorystore provides a managed in-memory cache (Redis or Memcached) that stores frequently accessed data, reducing database round-trips and significantly lowering response times under high traffic. Option B is not appropriate here because Cloud CDN with a backend bucket is designed for serving static content from Cloud Storage, not for optimizing a dynamic web application's backend latency. Option D is not ideal because vertically scaling instances provides only a fixed capacity increase and does not elastically handle variable peak loads as effectively as autoscaling. Option E is incorrect because reducing backend services simplifies routing but does not address the underlying performance bottleneck causing high latency.

Answer

Enable Cloud CDN with the origin as the backend bucket

Answer

Increase the size of the instances serving the application

Answer

Reduce the number of backend services to simplify routing

Question 12

A financial services company runs a real-time trading application on GKE with 10 microservices. The application uses Cloud Spanner as the database. Recently, the team noticed increased latency during peak trading hours. Cloud Monitoring shows high CPU utilization on the Spanner nodes (averaging 80%) and increased locking contention. The team has already added secondary indexes and tuned queries. The application's latency budget is 50ms for writes and 20ms for reads. The team must reduce latency while maintaining strong consistency and meeting the budget. What should they do?

Accepted Answer

Increase the number of Spanner nodes to reduce contention and CPU load. Increasing the number of Spanner nodes directly addresses the root cause: high CPU utilization (80%) and locking contention. More nodes distribute the read/write load, reducing per-node CPU and contention, which lowers latency. This maintains strong consistency and meets the 50ms write / 20ms read budget without architectural changes.

Answer

Change the application to use eventual consistency for read operations

Answer

Migrate the database to Cloud Bigtable for higher throughput

Answer

Implement a write buffer using Cloud Pub/Sub and batch writes to Spanner

Question 13

An e-commerce platform uses Cloud Load Balancing with backend services running on Compute Engine managed instance groups. During Black Friday sales, the application experiences high latency and some 503 errors. The team uses autoscaling based on average CPU utilization, but scaling is too slow—Cloud Monitoring shows CPU rises to 90% before new instances are added. The team needs to reduce latency and eliminate 503 errors. What should they do?

Accepted Answer

Change the autoscaling metric to 'requests per second' and set a lower target value. Switching the autoscaling metric from average CPU utilization to 'requests per second' (RPS) with a lower target value allows the autoscaler to react more quickly to traffic spikes. CPU utilization is a lagging indicator that rises only after requests have already been queued and processed, whereas RPS directly reflects incoming load. By setting a lower target RPS, the autoscaler can add instances before the backend becomes saturated, reducing latency and eliminating 503 errors.

Answer

Use HTTP load balancing with a larger backend timeout

Answer

Enable Cloud CDN for all dynamic content

Answer

Increase the cooldown period for the autoscaling policy

Question 14

A startup runs a mobile app backend on App Engine standard environment. They recently added new features, and the app's response time increased significantly. The team suspects instance startup time is causing cold starts for new users. They have already reduced code size and enabled warmup requests. What is the best next step to improve performance?

Accepted Answer

Increase the number of idle instances using automatic scaling settings. Warmup requests reduce cold starts by initializing the app before live traffic arrives, but they don't eliminate startup latency for new instances. Increasing the number of idle instances via automatic scaling settings ensures that pre-warmed, ready-to-serve instances are always available, so new users never trigger a cold start. This directly addresses the root cause—instance startup time—without changing the environment or adding complexity.

Answer

Migrate to App Engine flexible environment

Answer

Implement a latency-based health check to redirect traffic

Answer

Use Cloud Endpoints to limit traffic and reduce load

Question 15

A company runs a batch processing pipeline on Dataflow that reads from Pub/Sub and writes to BigQuery. The pipeline is falling behind due to high volume, and messages are backing up in Pub/Sub. Autoscaling is enabled and workers are running but utilization is only 30%. The streaming engine is off. What should the engineer do to increase throughput?

Accepted Answer

Enable Streaming Engine for Dataflow.. B is correct because enabling Streaming Engine offloads the heavy lifting of shuffle and state management from worker VMs to the Dataflow service backend, reducing the per-worker overhead. With only 30% utilization, the bottleneck is not compute capacity but the per-worker throughput limit caused by the streaming pipeline's shuffle and state operations. Streaming Engine allows the existing workers to process more data per second by removing these bottlenecks, directly addressing the Pub/Sub backlog without adding more resources.

Answer

Use a larger machine type for workers.

Answer

Increase the number of worker machines.

Answer

Increase the worker disk size.