PCDOE Optimizing service performance Practice Test 7 — 15 Questions

Question 1

A Cloud Spanner database is experiencing slow query performance. Which approach should be taken to optimize read performance without compromising consistency?

Accepted Answer

Add secondary indexes and use read-only transactions for read queries. Secondary indexes in Cloud Spanner allow efficient lookup of rows by non-key columns, avoiding full table scans. Read-only transactions provide a consistent snapshot of data without locking, which optimizes read performance while maintaining strong consistency.

Answer

Increase the number of Spanner nodes to boost throughput

Answer

Use interleaved tables to store related rows together

Answer

Migrate the data to Cloud Bigtable for better read performance

Question 2

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 3

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 4

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 5

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 6

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 7

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. Implementing autoscaling based on CPU utilization allows the application to dynamically add compute instances during peak hours when CPU load increases, thereby distributing the request load and reducing latency. This aligns with Google Cloud's managed instance groups and autoscaling policies that scale out based on metrics like CPU utilization, ensuring sufficient resources are available to handle traffic spikes without manual intervention.

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 8

A company runs a microservices architecture on GKE and notices high network latency between services. Which THREE actions can improve inter-service communication performance?

Accepted Answer

Implement caching at the API gateway to reduce redundant requests. Implementing caching at the API gateway reduces redundant requests, which directly decreases network round trips and lowers latency for repeated data retrievals. This is a common performance optimization in microservices architectures, as it offloads backend services and minimizes inter-service chatter.

Answer

Use Istio mTLS for all service-to-service communication

Answer

Increase the number of nodes in the cluster

Question 9

A DevOps team wants to optimize the performance of a Cloud Run service that experiences sporadic traffic. Which TWO strategies should they implement?

Accepted Answer

Reduce container image size and use startup probes. Reducing the container image size decreases the time required to pull and start the container, directly mitigating cold start latency. Startup probes allow Cloud Run to defer sending traffic until the container is ready, preventing premature request failures and improving perceived performance. Together, these optimizations address the root causes of cold starts in a serverless environment.

Answer

Set min-instances to 5 to avoid cold starts

Answer

Use Cloud Run for Anthos on GKE for better performance

Answer

Use Cloud Scheduler to trigger the service periodically

Question 10

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 11

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 12

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 13

A company runs a microservices application on GKE. The checkout service has high tail latency. Using Cloud Profiler, the team finds that most time is spent in database queries. Which action should they take to improve performance?

Accepted Answer

Add database connection pooling using a sidecar proxy.. Database connection pooling reduces the overhead of establishing new connections for each request, which is a common cause of high tail latency in microservices. By using a sidecar proxy (e.g., Envoy or a dedicated connection pooler like PgBouncer), the checkout service can reuse existing database connections, minimizing latency spikes from connection setup and teardown. This directly addresses the root cause identified by Cloud Profiler—time spent in database queries—without requiring a database migration or scaling the service itself.

Answer

Migrate the database to Cloud Spanner.

Answer

Increase the number of replicas of the checkout service.

Answer

Enable Cloud CDN for the checkout API.

Question 14

Which TWO actions should a DevOps engineer take to reduce latency for a global user base accessing a web application hosted on Compute Engine?

Accepted Answer

Configure instance groups in multiple regions with a global load balancer.. Deploying instance groups in multiple regions and using a global load balancer (e.g., Google Cloud External HTTP(S) Load Balancer) allows user requests to be routed to the closest healthy backend, reducing network round-trip time. This geo-distribution minimizes latency by serving content from the nearest regional endpoint rather than a single centralized location.

Answer

Enable HTTP/2 on the load balancer.

Answer

Increase the machine type of the instances.

Answer

Use Cloud Load Balancing with global anycast IP.

Question 15

A company runs a web application on Compute Engine behind a regional HTTP Load Balancer. Users report slow page load times during peak hours. CPU utilization on instances is under 60%, but network egress is near the instance's bandwidth limit. Which action should the engineer take?

Accepted Answer

Use a larger machine type with higher network throughput.. The bottleneck is network egress bandwidth, not CPU. Increasing the instance size to a machine type with higher network throughput (e.g., n2-highmem-4 vs. n2-standard-2) directly raises the per-instance egress cap, alleviating the bandwidth limit. Option D is correct because it addresses the root cause—insufficient network I/O capacity per instance.

Answer

Increase the number of instances in the instance group.

Answer

Switch to a global external HTTP Load Balancer.

Answer

Enable Cloud CDN.