PCDOE Optimizing service performance Practice Test 6 — 15 Questions

Question 1

A web application frequently reads the same set of reference data from Cloud SQL. This causes high database load and slow responses. Which design change would most improve performance?

Accepted Answer

Implement caching with Memorystore for Redis. Implementing caching with Memorystore reduces database load by serving repeated reads from memory, which is faster than SQL queries. Increasing machine size or adding read replicas helps but still involves database I/O; Cloud Spanner is overkill for reference data.

Answer

Increase Cloud SQL machine size

Answer

Add a read replica

Answer

Use Cloud Spanner for higher throughput

Question 2

A team uses Cloud Spanner for a global application. Query performance degrades as data grows. They notice that most queries filter on a column 'customer_id' but the primary key is a UUID. What is the best approach to optimize performance?

Accepted Answer

Reorder the primary key to start with customer_id. In Cloud Spanner, the primary key determines the physical ordering of rows on storage tablets. When queries filter on `customer_id` but the primary key starts with a UUID, Spanner must perform a full table scan because the filter cannot leverage the key order. Reordering the primary key to start with `customer_id` allows Spanner to use efficient key-range scans, dramatically reducing the number of rows read per query.

Answer

Enable query optimization hints

Answer

Use interleaved tables

Answer

Create a secondary index on customer_id

Question 3

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 4

A data engineering team runs frequent aggregation queries on a large BigQuery table. Query performance is slow and costs are high. Which optimization technique would best improve performance and reduce cost?

Accepted Answer

Use materialized views for pre-aggregated results. Materialized views pre-compute and store the results of frequent aggregation queries, allowing BigQuery to serve subsequent queries directly from the cached results rather than scanning the entire base table. This drastically reduces the amount of data processed, lowering both query latency and cost (since BigQuery charges by bytes processed). For repeated aggregation patterns, this is the most effective optimization.

Answer

Convert to non-partitioned table

Answer

Use clustering on the partition key

Answer

Increase the number of slots in the reservation

Question 5

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 6

A team wants to simulate real-world user traffic to identify performance bottlenecks before a launch. Which tool should they use to generate load from multiple regions?

Accepted Answer

Cloud Load Testing (Distributed Load Testing on GCP). Cloud Load Testing (Distributed Load Testing on GCP) is the correct choice because it is a managed service that can generate synthetic traffic from multiple geographic regions simultaneously, simulating real-world user distribution. This allows the team to identify performance bottlenecks across different network paths and regional endpoints before launch.

Answer

Cloud Monitoring

Answer

gcloud beta load test

Answer

ab (Apache Benchmark)

Question 7

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 provide higher IOPS and lower latency than standard persistent disks, making them suitable for high disk I/O workloads. Option D is correct because local SSDs offer even higher performance by attaching directly to the host VM, but their data is ephemeral, making them ideal for temporary data like caches or scratch space.

Answer

Use standard persistent disk for cost savings

Answer

Enable disk encryption

Answer

Increase disk size

Question 8

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. Pod anti-affinity spreads pods across different nodes, reducing the risk of a single node becoming a hotspot and causing contention for resources like CPU, memory, or network bandwidth. By distributing pods, you minimize queuing delays and improve tail latency, as no single node is overloaded with too many pods competing for the same resources.

Answer

Enable Istio sidecar injection for all pods

Answer

Use pod affinity to pack pods on same node

Question 9

A company uses Cloud SQL for their transactional database. They are experiencing slow read performance. Which THREE actions can improve read throughput? (Choose three.)

Accepted Answer

Add a read replica. Adding a read replica offloads read queries from the primary Cloud SQL instance, distributing the read workload and improving read throughput. This is a common pattern for scaling read-heavy workloads in Cloud SQL, as replicas serve read traffic asynchronously without impacting the primary instance's write performance.

Answer

Enable query caching

Answer

Use Cloud SQL Proxy for connections

Question 10

Refer to the exhibit. The team observes that some requests are fast while others are slow. Both requests have identical payload and response. What is the most likely cause of the latency difference?

Accepted Answer

The fast request hit a cached response. The fast request hit a cached response, meaning the reverse proxy or CDN served the response from its cache without forwarding the request to the origin server. This eliminates the round-trip time to the backend and the processing time on the origin, resulting in significantly lower latency. Since both requests have identical payloads and responses, caching is the most plausible explanation for the observed difference.

Answer

The slow request had a larger response size

Answer

The fast request used a different load balancer

Answer

The slow request used a different HTTP method

Question 11

Refer to the exhibit. A GKE node shows MemoryPressure condition. What should the team do to improve performance of pods scheduled on this node?

Accepted Answer

Enable cluster autoscaler to scale up new nodes. When a GKE node reports a MemoryPressure condition, it means the node's kubelet is actively evicting pods to free memory, which degrades performance. Enabling cluster autoscaler allows the cluster to automatically provision new nodes when existing nodes are under memory pressure, redistributing pods and alleviating the condition without manual intervention.

Answer

Increase the node's memory by changing the machine type

Answer

Adjust pod resource requests to leave more allocatable memory

Answer

Evict pods and delete the node

Question 12

Refer to the exhibit. The team wants to reduce the service's p50 latency from 2 seconds to under 500ms. Which optimization would have the most impact?

Accepted Answer

Optimize getCustomerData() by caching customer data. The exhibit shows that getCustomerData() is the most time-consuming operation, taking 1.2 seconds out of the total 2-second p50 latency. Caching customer data eliminates repeated expensive lookups (e.g., database queries or external API calls), directly reducing the critical path latency. This optimization targets the largest bottleneck, making it the most impactful for achieving sub-500ms p50.

Answer

Increase the number of service instances

Answer

Optimize processOrder() by reducing logging

Answer

Optimize saveToDatabase() by using batch writes

Question 13

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 14

A microservices application on GKE with Istio service mesh experienced performance degradation after a recent update. Which optimization technique is most effective for improving inter-service communication performance?

Accepted Answer

Enable gRPC for inter-service communication. Option D is correct because gRPC uses HTTP/2 as its transport protocol, which enables multiplexed streams over a single TCP connection, reducing latency and improving throughput for inter-service communication. In a GKE environment with Istio, gRPC also leverages Istio's native support for HTTP/2-based traffic, allowing efficient load balancing and connection reuse. This directly addresses performance degradation caused by chatty or high-frequency service calls.

Answer

Increase Istio sidecar resource limits

Answer

Implement request collapsing to merge identical requests

Answer

Use Istio traffic mirroring to offload requests

Question 15

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 a minimum number of idle instances ensures that instances are always ready to serve traffic, eliminating startup delays. Manual scaling (A) requires manual capacity planning; increasing memory (C) may not reduce startup time; Cloud Endpoints (D) is for API management.

Answer

Use manual scaling instead of automatic scaling

Answer

Use Cloud Endpoints for request authentication

Answer

Increase the memory limit per instance