PCDOE Optimizing service performance Practice Test 1 — 15 Questions

Question 1

Your team has deployed a microservices application on Google Kubernetes Engine (GKE). You notice that one service has high latency during peak hours. The service is CPU-bound and uses a HorizontalPodAutoscaler (HPA) based on CPU utilization. What is the most likely cause of the latency?

Accepted Answer

The HPA's target CPU utilization is set too high, causing the autoscaler to react slowly.. Option B is correct because when the HPA's target CPU utilization is set too high, the autoscaler waits until the average CPU utilization exceeds that threshold before scaling up. During peak hours, the service becomes CPU-bound and latency increases as pods are overwhelmed, but the HPA reacts slowly because it only triggers when the high threshold is breached, causing a delay in adding new pods to handle the load.

Answer

The GKE cluster uses preemptible nodes that are frequently reclaimed.

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The service uses a global external HTTP(S) load balancer with session affinity.

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The application does not implement request autoscaling at the application layer.

Question 2

A Cloud Run service is experiencing increased cold start latency. The service is written in Python and uses several large dependencies. Which action would most effectively reduce cold start latency?

Accepted Answer

Set a minimum number of instances to keep containers warm.. Option C is correct because setting a minimum number of instances ensures that the Cloud Run service always has a pool of warm containers ready to serve requests, eliminating the cold start penalty. Cold starts in Python are particularly severe due to the time required to import large dependencies (e.g., NumPy, TensorFlow) and initialize the runtime. By keeping containers alive, you bypass the entire initialization phase, directly addressing the root cause of increased latency.

Answer

Set concurrency to 1 to ensure each request gets a dedicated container.

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Increase the CPU allocation to 4 vCPUs.

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Increase memory to 2 GiB.

Question 3

You are designing a globally distributed application using Cloud Spanner. The application has a write-heavy workload. You notice that write latency increases as the number of nodes increases. What is the most likely cause?

Accepted Answer

The workload has many cross-node transactions due to split rows.. Option B is correct because in Cloud Spanner, write-heavy workloads with many cross-node transactions cause increased write latency as nodes are added. This occurs because Spanner splits rows across nodes, and transactions that span multiple splits require two-phase commit (2PC) coordination between nodes, which adds network overhead and latency. Adding more nodes increases the likelihood that a transaction touches multiple splits, exacerbating the coordination cost.

Answer

The instance is using a multi-region configuration with too many read-only replicas.

Answer

The application is using stale reads for write transactions.

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The number of splits is too low, causing hotspots.

Question 4

A company runs a stateful workload on Compute Engine VMs with persistent disks. They observe that disk I/O latency spikes periodically. The workload is sensitive to latency. What should they do to improve performance?

Accepted Answer

Use SSD persistent disks instead of standard persistent disks.. Option C is correct because SSD persistent disks provide consistent, low-latency I/O performance compared to standard persistent disks, which use spinning media and can exhibit periodic latency spikes under sustained load. For latency-sensitive stateful workloads, SSD persistent disks offer predictable IOPS and throughput, directly addressing the periodic spikes observed.

Answer

Increase the size of the persistent disk.

Answer

Migrate to local SSDs for better performance.

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Configure a snapshot schedule to offload I/O.

Question 5

Your GKE cluster runs a batch job that processes large files from Cloud Storage. The job uses CPUs inefficiently, with low utilization. You want to reduce cost while maintaining throughput. Which approach should you take?

Accepted Answer

Use Cloud Storage FUSE to stream files directly into containers, avoiding local storage.. Option A is correct because Cloud Storage FUSE allows containers to stream files directly from Cloud Storage without first downloading them to a local disk. This eliminates the I/O bottleneck of writing to local storage and reduces CPU overhead from disk operations, enabling the batch job to process files more efficiently and maintain throughput while using fewer CPU resources.

Answer

Configure the node pool to use spot VMs.

Answer

Use local SSDs for faster file access.

Answer

Increase the CPU request for the job pods.

Question 6

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.

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Configure a serverless NEG to route traffic to Cloud Functions.

Question 7

Your application uses Cloud SQL for MySQL and you notice that read replica lag is increasing. Which action would most likely reduce replica lag?

Accepted Answer

Increase the machine type of the replica.. Increasing the machine type of the replica (Option C) directly addresses read replica lag by providing more CPU and memory resources to the replica instance. This allows the replica to apply binary log (binlog) events from the primary faster, reducing the replication lag. Cloud SQL for MySQL uses asynchronous replication, so a replica with insufficient resources cannot keep up with the write throughput of the primary.

Answer

Configure automatic failover to the replica.

Answer

Decrease the memory of the primary instance.

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Promote the replica to a standalone instance.

Question 8

You are using Memorystore for Redis as a cache for a high-traffic web application. You observe that cache hit ratio is low, causing high database load. What is the most effective way to improve cache hit ratio?

Accepted Answer

Use the allkeys-lru eviction policy to keep frequently accessed keys.. The allkeys-lru eviction policy is the most effective way to improve cache hit ratio because it automatically evicts the least recently used keys across the entire keyspace when memory is full, retaining the most frequently accessed data. This ensures that the cache always contains the hottest data, directly increasing the likelihood of cache hits without requiring manual intervention or additional resources.

Answer

Increase the instance size to store more data.

Answer

Migrate to Memorystore for Memcached for lower latency.

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Set a longer TTL for all cache entries.

Question 9

Which TWO actions can reduce tail latency in a microservices architecture deployed on GKE? (Choose 2)

Accepted Answer

Run multiple replicas of each service and use a load balancer with least-request algorithm.. Option A is correct because running multiple replicas and using a load balancer with a least-request algorithm distributes incoming requests to the pod with the fewest active connections, reducing queuing delay and preventing any single replica from becoming a hotspot. This directly lowers tail latency by ensuring that slow or overloaded pods are not overwhelmed, and the load balancer's algorithm minimizes the variance in response times across replicas.

Answer

Use a single large machine type for all services.

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Enable session affinity to keep users on the same pod.

Answer

Increase the batch size for processing requests.

Question 10

Which THREE factors should you consider when designing a Cloud Run service for optimal performance under unpredictable traffic patterns? (Choose 3)

Accepted Answer

Set a minimum number of instances to reduce cold starts.. Option B is correct because setting a minimum number of instances ensures that Cloud Run always keeps a baseline of warm instances ready to serve requests. This eliminates cold starts for the first requests during traffic spikes, which is critical for unpredictable traffic patterns where latency spikes from cold starts would degrade user experience.

Answer

Use HTTP/2 for faster connection reuse.

Answer

Configure VPC egress through Cloud NAT for lower latency.

Question 11

Which TWO metrics from Cloud Monitoring would best indicate that a GKE workload is experiencing CPU throttling due to a resource quota? (Choose 2)

Accepted Answer

container/cpu/throttled_time. Option B is correct because `container/cpu/throttled_time` directly measures the cumulative time a container's CPU usage was throttled due to exceeding its assigned CPU quota (CFS quota). Option D is correct because `container/cpu/usage_time` shows the actual CPU time used by the container; when compared against the quota limit, a high usage_time relative to the quota indicates that throttling is likely occurring. Together, these two metrics confirm both the occurrence and the cause of CPU throttling.

Answer

node/cpu/usage_time

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container/memory/usage_bytes

Answer

container/accelerator/duty_cycle

Question 12

Which THREE approaches can help reduce egress costs while improving performance for a multi-region application using Cloud Load Balancing? (Choose 3)

Accepted Answer

Optimize data transfer by compressing responses.. Option A is correct because compressing responses reduces the amount of data transferred over the network, which directly lowers egress costs charged by cloud providers. Smaller payloads also reduce latency and improve perceived performance for end users, as less data needs to travel across regions.

Answer

Increase the number of instances in each region.

Answer

Use premium tier networking for lower latency.

Question 13

Your company runs a multi-region e-commerce platform on Google Kubernetes Engine (GKE) with services in us-central1 and europe-west1. The application uses a global external HTTP(S) load balancer with Cloud CDN for static assets. Recently, users in Asia report that product images take 5-10 seconds to load, while users in the US and Europe experience sub-second load times. You check the Cloud CDN cache hit ratio and see it is 95% globally. You also notice that the images are served from a backend bucket in us-central1. The load balancer uses the default routing configuration. Your team has implemented client-side caching with Cache-Control headers set to public, max-age=3600. What is the most likely cause of the high latency for Asian users?

Accepted Answer

Cloud CDN does not have edge caches in Asia, so requests are served from the nearest available edge location, which may be far from users.. Option C is correct because Cloud CDN does not have edge caches in Asia; the nearest edge locations for Asian users are in the western United States (e.g., Los Angeles) or possibly Australia, resulting in higher latency. Even with a 95% global cache hit ratio, the physical distance from Asia to the serving edge increases round-trip time (RTT) significantly, causing 5-10 second load times. The default routing configuration of the global external HTTP(S) load balancer does not automatically optimize for regional cache presence.

Answer

The load balancer is not using premium tier networking, so traffic from Asia takes a longer path.

Answer

Client-side caching with max-age=3600 is too short, causing frequent revalidation.

Answer

The cache hit ratio is too low for Asian users due to different traffic patterns.

Question 14

A team deploys a microservice on Google Kubernetes Engine (GKE) that processes user uploads. The service latency has increased over time. Monitoring shows that CPU utilization is low, but memory usage is high and garbage collection (GC) pauses are frequent. Which action is most likely to reduce latency?

Accepted Answer

Increase the memory limit and requests for the container.. Frequent GC pauses and high memory usage with low CPU indicate the JVM heap is too small, causing the garbage collector to run more often. Increasing the memory limit and requests gives the JVM more headroom to reduce GC frequency, directly lowering latency. This is a classic JVM tuning scenario in containerized environments like GKE.

Answer

Scale out the deployment by increasing the number of replicas.

Answer

Reduce the number of replicas to concentrate load.

Answer

Increase the CPU limit to allow faster processing.

Question 15

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?

Accepted Answer

Analyze Cloud Monitoring metrics for the load balancer, including backend latency and request counts.. Option D is correct because 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.

Answer

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