PCDOE Optimizing service performance Practice Test 5 — 15 Questions

Question 1

A team wants to optimize a batch processing job that is CPU-bound. Which Compute Engine machine family should they use?

Accepted Answer

C2. C2 is the correct machine family because it is specifically designed for compute-intensive, CPU-bound workloads. It offers the highest clock speed and per-core performance among Compute Engine machine families, making it ideal for batch processing jobs that are limited by CPU throughput rather than memory or I/O.

Answer

E2

Answer

N2

Answer

M2

Question 2

A DevOps team wants to optimize resource utilization for their GKE deployment. Which built-in Kubernetes resource can automatically adjust CPU and memory requests based on historical usage?

Accepted Answer

VerticalPodAutoscaler. The VerticalPodAutoscaler (VPA) is the correct choice because it automatically adjusts CPU and memory resource requests (and limits) for pods based on historical usage data, optimizing resource utilization without manual intervention. Unlike the HorizontalPodAutoscaler, which scales the number of pods, the VPA modifies the resource specifications of existing pods to match observed demand.

Answer

HorizontalPodAutoscaler

Answer

ResourceQuota

Answer

PodDisruptionBudget

Question 3

Which service should be used to monitor the health of HTTP endpoints from multiple locations?

Accepted Answer

Cloud Monitoring. Cloud Monitoring (formerly Stackdriver Monitoring) includes uptime checks that can be configured to probe HTTP endpoints from multiple global locations, measuring latency, availability, and response content. This makes it the correct service for monitoring the health of HTTP endpoints from diverse geographic regions.

Answer

Cloud Trace

Answer

Cloud Logging

Answer

Cloud Debugger

Question 4

Which Cloud Run setting controls the maximum number of requests a container can handle concurrently?

Accepted Answer

concurrency. The `concurrency` setting in Cloud Run defines the maximum number of simultaneous requests that a single container instance can process at any given time. This directly controls how many requests are routed to a single container before Cloud Run spins up additional instances, optimizing resource utilization and preventing overload.

Answer

timeout

Answer

max-instances

Answer

min-instances

Question 5

Which tool can be used to capture and analyze latency spikes in a distributed application?

Accepted Answer

Cloud Trace. Cloud Trace is the correct tool for capturing and analyzing latency spikes in a distributed application because it provides end-to-end latency tracking by instrumenting requests as they traverse microservices. It collects trace spans with timing data, allowing you to identify bottlenecks and pinpoint the exact service or operation causing the spike.

Answer

Cloud Logging

Answer

Cloud Debugger

Answer

Cloud Monitoring

Question 6

Which storage class provides the lowest cost for data accessed less than once a year?

Accepted Answer

Archive. Archive storage class is the correct answer because it is specifically designed for long-term data retention where access is extremely infrequent, such as less than once a year. It offers the lowest storage cost among Google Cloud Storage classes, but with higher retrieval costs and a minimum storage duration of 365 days, making it ideal for data that is rarely accessed.

Answer

Nearline

Answer

Standard

Answer

Coldline

Question 7

Which service is commonly used for time-series data and real-time analytics?

Accepted Answer

Bigtable. Bigtable is a fully managed, scalable NoSQL database optimized for large analytical and operational workloads, including time-series data and real-time analytics. It provides sub-10ms latency for high-throughput reads and writes, making it ideal for IoT, monitoring, and financial data streams. Its column-oriented storage and automatic sharding support efficient time-based queries and aggregation.

Answer

Cloud SQL

Answer

Firestore

Answer

Cloud Spanner

Question 8

A company runs a high-traffic web application on GKE. Which three practices can help optimize performance under load? (Select THREE.)

Accepted Answer

Use a global load balancer. A global load balancer (GLB) distributes incoming traffic across multiple GKE clusters and regions using Google's global network infrastructure. It terminates traffic at the edge, reducing latency by routing users to the closest healthy backend, and provides DDoS protection and SSL offloading. This is essential for high-traffic web applications to handle load spikes and improve response times.

Answer

Use a multi-cluster ingress

Answer

Increase the number of nodes to maximum

Question 9

A DevOps team wants to monitor the performance of a Cloud SQL database. Which two metrics should they track? (Select TWO.)

Accepted Answer

CPU utilization. CPU utilization (C) is a critical metric for Cloud SQL because high CPU usage indicates that the database instance is struggling to process queries, often due to inefficient queries or insufficient compute capacity. Monitoring CPU utilization helps teams decide when to scale up or optimize query performance.

Answer

Auto-increment counter

Answer

Query error rate

Answer

Number of active connections

Question 10

Refer to the exhibit. A DevOps engineer observes that a GKE cluster's node performance is degraded during high I/O workloads. Based on the exhibit, which change would most likely improve disk I/O performance?

Accepted Answer

Change diskType to pd-ssd. The exhibit shows a GKE node pool using the default pd-standard disk type, which is backed by HDDs and has lower IOPS and throughput compared to SSDs. Changing diskType to pd-ssd directly improves disk I/O performance by providing higher IOPS and lower latency, which is critical for high I/O workloads.

Answer

Change machineType to n2-standard-4

Answer

Change imageType to UBUNTU

Answer

Change serviceAccount to a custom one

Question 11

Refer to the exhibit. A team is troubleshooting a pod crash loop. Based on the exhibit, which infrastructure change should be prioritized to resolve the issue and optimize service performance?

Accepted Answer

Mount a ConfigMap or volume containing the missing file. The exhibit indicates a pod crash loop caused by a missing file, which is a configuration issue rather than a resource or scheduling problem. Mounting a ConfigMap or volume that provides the missing file directly resolves the crash by ensuring the pod has the required configuration at startup. This fix also optimizes service performance by eliminating unnecessary restarts and allowing the pod to serve traffic consistently.

Answer

Increase the pod's CPU request

Answer

Increase the maximum number of pods per node

Answer

Enable pod anti-affinity

Question 12

Refer to the exhibit. A team runs a batch processing job on these instances. The job is CPU-bound and can tolerate interruptions. Which instance is the most cost-effective for this workload?

Accepted Answer

instance-1. Instance-1 is the most cost-effective because it is a preemptible (or spot) VM, which is significantly cheaper than standard on-demand instances. Since the batch processing job is CPU-bound and can tolerate interruptions, preemptible instances are ideal for this workload, offering up to 60-91% cost savings while still providing the necessary compute capacity.

Answer

instance-3

Answer

None, they should use a different machine type

Answer

instance-2

Question 13

A company serves static assets (images, CSS) to global users. Users in distant regions experience slow load times. Which service should they use to optimize delivery?

Accepted Answer

Cloud CDN. Cloud CDN (Content Delivery Network) caches static assets (images, CSS) at edge locations worldwide, reducing latency for distant users by serving content from a nearby point of presence (PoP). This directly addresses slow load times caused by geographic distance, as the origin server is no longer the sole source of delivery.

Answer

Cloud Load Balancing

Answer

Cloud NAT

Answer

Cloud Armor

Question 14

An application running on GKE experiences high latency during traffic spikes. The team wants to scale pods based on request latency. Which metric should they use in the HorizontalPodAutoscaler?

Accepted Answer

Custom metric: request latency percentile (e.g., p95). To scale based on request latency, the HorizontalPodAutoscaler (HPA) must use a custom metric that directly reflects the application's response time, such as the p95 latency percentile. CPU or memory utilization are system-level metrics that do not capture the user-perceived performance degradation caused by traffic spikes. Custom metrics like request count correlate with load but not directly with latency, making the p95 latency percentile the correct choice for latency-based autoscaling.

Answer

Custom metric: request count

Answer

Memory utilization

Answer

CPU utilization

Question 15

A team notices that a Cloud Run service occasionally has high latency. They suspect a memory leak or excessive CPU usage. Which tool should they use to identify the bottleneck during those periods?

Accepted Answer

Cloud Profiler. Cloud Profiler is the correct tool because it continuously gathers CPU and memory usage data from your Cloud Run service, allowing you to identify which functions or code paths are consuming excessive resources during high-latency periods. Unlike monitoring or logging, Profiler provides a flame graph that pinpoints the exact bottleneck (e.g., a memory leak or CPU spike) at the function level, which is essential for diagnosing performance issues in a serverless environment.

Answer

Cloud Trace

Answer

Cloud Monitoring

Answer

Cloud Logging