PCDOE Optimizing service performance — 20 Questions

Question 1

You are troubleshooting a performance issue with a Compute Engine instance that is part of a managed instance group serving a web application. Users report intermittent high latency. You run the command shown in the exhibit. Based on the output, what is the most likely cause of the performance issue?

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

Accepted Answer

The instance is under-provisioned for CPU.. The output shows high CPU utilization (e.g., 95%+), which directly correlates with the reported intermittent high latency. In a managed instance group, if the instance is under-provisioned for CPU, it cannot handle the workload spikes, causing queuing and increased response times. This is the most common cause of performance degradation in Compute Engine instances serving web applications.

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The instance is hitting the network egress bandwidth limit.

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The service account lacks the necessary scopes for Cloud Monitoring and Cloud Trace.

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The boot disk is too small, causing I/O contention.

Question 2

An e-commerce platform uses Cloud SQL for its database. The team notices that read queries are slow. They want to improve read performance without significant cost increase. Which action should they take?

Accepted Answer

Add a read replica. Adding a read replica is the most cost-effective way to improve read query performance in Cloud SQL. A read replica offloads SELECT queries from the primary instance, reducing contention and latency for read-heavy workloads, without requiring a more expensive primary instance. This approach scales read capacity horizontally while keeping the primary instance's compute and storage unchanged.

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Increase the number of vCPUs on the instance

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Increase the storage size

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Enable binary logging

Question 3

A financial services company uses Spanner for their core database. They notice that some transactions are taking longer than expected, especially during cross-region writes. They have set up Spanner with regional configuration. What is the most likely cause?

Accepted Answer

The transaction is experiencing contention due to a hot spot. A is correct because cross-region writes in a regional Spanner configuration can lead to increased latency due to hot spotting. A hot spot occurs when many writes are concentrated on a single split (e.g., a monotonically increasing key), causing contention and serialization delays. This is especially pronounced in cross-region scenarios because Spanner's TrueTime and Paxos-based replication require consensus across zones, amplifying the impact of contention.

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The transaction is using stale reads

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The transaction is not using a read-write transaction

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The transaction is too large

Question 4

A team is optimizing a Cloud Run service. Which two actions can reduce request latency? (Select TWO.)

Accepted Answer

Reduce container image size. Reducing the container image size decreases the time required to pull the image from the registry to the compute instance, which directly reduces cold-start latency. Enabling min-instances keeps a specified number of instances always warm, eliminating cold-start delays for baseline traffic. Both actions address the most common sources of latency in serverless Cloud Run deployments.

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Increase max-instances

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Enable HTTP/2

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Use a regional endpoint

Question 5

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.. 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.

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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 6

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.. 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.

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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 7

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.. 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.

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

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The application is using stale reads for write transactions.

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The number of splits is too low, causing hotspots.

Question 8

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.. 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.

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Increase the size of the persistent disk.

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Migrate to local SSDs for better performance.

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Configure a snapshot schedule to offload I/O.

Question 9

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.. 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.

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Configure the node pool to use spot VMs.

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Use local SSDs for faster file access.

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Increase the CPU request for the job pods.

Question 10

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.

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Switch the load balancer to an internal HTTPS load balancer with gRPC.

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Use premium tier networking for the load balancer.

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Configure a serverless NEG to route traffic to Cloud Functions.

Question 11

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.

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Configure automatic failover to the replica.

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Decrease the memory of the primary instance.

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Promote the replica to a standalone instance.

Question 12

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.

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Increase the instance size to store more data.

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Migrate to Memorystore for Memcached for lower latency.

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Set a longer TTL for all cache entries.

Question 13

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.. 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.

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Use a single large machine type for all services.

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Enable session affinity to keep users on the same pod.

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Increase the batch size for processing requests.

Question 14

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.. 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.

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Use HTTP/2 for faster connection reuse.

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Configure VPC egress through Cloud NAT for lower latency.

Question 15

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. `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.

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node/cpu/usage_time

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container/memory/usage_bytes

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container/accelerator/duty_cycle

Question 16

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.. 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.

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Increase the number of instances in each region.

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Use premium tier networking for lower latency.

Question 17

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

The cache hit ratio is too low for Asian users due to different traffic patterns.. While Cloud CDN has edge caches in Asia (e.g., Tokyo, Mumbai, Singapore), the global cache hit ratio of 95% does not guarantee a high hit ratio for Asian users. If their traffic patterns differ (e.g., first requests for new products), they may experience cache misses, forcing requests to the backend bucket in us-central1, resulting in high latency. Options A and B are not likely: the load balancer uses premium tier by default, and max-age=3600 is reasonable. Option C is incorrect because Cloud CDN does have edge caches in Asia.

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

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

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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.

Question 18

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.

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Scale out the deployment by increasing the number of replicas.

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Reduce the number of replicas to concentrate load.

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Increase the CPU limit to allow faster processing.

Question 19

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.. 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.

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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 20

A DevOps engineer is optimizing a Cloud Run service that experiences cold starts. The service is written in Python and uses several large libraries. Which change is most effective to reduce cold start latency?

Accepted Answer

Set a minimum number of instances to keep containers warm.. Setting a minimum number of instances (option B) ensures that a baseline of container instances is always warm and ready to serve requests, eliminating cold starts for those instances. Cold starts occur when a new container must be initialized, including loading large Python libraries, which adds significant latency. By keeping a minimum number of instances running, the service avoids the initialization delay for the first request to each instance.

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Increase the maximum number of concurrent requests per container.

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Set a longer request timeout.

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Increase the CPU allocation for the service.