PCD Designing highly scalable, available, and reliable cloud-native applications Practice Test 4 — 15 Questions

Question 1

A company wants to deploy a stateless web application that needs to handle unpredictable traffic spikes with minimal operational overhead. Which Google Cloud compute service is most cost-effective and operationally simple?

Accepted Answer

Cloud Run. Cloud Run is the most cost-effective and operationally simple choice for a stateless web application with unpredictable traffic spikes because it automatically scales from zero to thousands of containers based on request load, charges only for resources used during request processing (down to 100ms increments), and eliminates infrastructure management. It supports any language or framework via container images, making it ideal for stateless HTTP workloads without the cold-start latency concerns of Cloud Functions or the cluster management overhead of GKE.

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App Engine Standard

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Cloud Functions

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Google Kubernetes Engine (GKE)

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Compute Engine with Managed Instance Group

Question 2

An e-commerce company relies on a Compute Engine backend serving content to global users. They notice high latency for users outside the primary region. Which service should they add to reduce latency by caching content at edge locations?

Accepted Answer

Cloud CDN. Cloud CDN (Content Delivery Network) uses Google's global edge cache locations to serve cached content closer to users, reducing latency for requests that would otherwise travel to the origin Compute Engine backend in a single region. By caching static or dynamic content at edge nodes, Cloud CDN minimizes round-trip time and offloads traffic from the backend instance.

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Cloud Armor

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Cloud NAT

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Cloud Endpoints

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Cloud Load Balancing

Question 3

A startup expects low and predictable traffic initially but wants to use containers with minimal operational overhead. Which compute service should they choose?

Accepted Answer

Cloud Run. Cloud Run is the correct choice because it runs containers in a fully managed, serverless environment that automatically scales from zero, requires no cluster management, and charges only for resources used during request processing. This matches the startup's need for minimal operational overhead and low, predictable traffic, as Cloud Run abstracts away infrastructure management entirely.

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App Engine Flexible Environment

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Google Kubernetes Engine (GKE)

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Compute Engine

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Cloud Functions

Question 4

A company runs a stateful microservice that requires read-after-write consistency but can tolerate some latency for writes. They are currently using a single Cloud SQL instance and want to scale read traffic. Which approach should they take?

Accepted Answer

Enable Cloud SQL read replicas. Cloud SQL read replicas are the correct choice because they provide an asynchronous read-only copy of the primary instance, which can scale read traffic without compromising the read-after-write consistency required by the stateful microservice. The primary instance handles all writes, ensuring strong consistency for writes, while replicas serve stale reads that eventually become consistent, which aligns with the tolerance for write latency.

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Use Cloud Memorystore to cache reads

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Shard the database manually

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Use Cloud Bigtable

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Migrate to Cloud Spanner

Question 5

A company uses Cloud Functions to process events from Pub/Sub. They notice that occasionally the same message is processed more than once. What can they do to ensure idempotent processing?

Accepted Answer

Make the function idempotent using a deduplication field. Option A is correct because making the function idempotent using a deduplication field ensures that even if the same Pub/Sub message is delivered more than once (Pub/Sub offers at-least-once delivery), the function processes it only once. By checking a unique message ID or a custom deduplication key before processing, the function can skip or safely reapply the operation, preventing duplicate side effects.

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Configure retry policies to only retry once

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Use Cloud Tasks instead

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Use Cloud Scheduler

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Increase the acknowledgement deadline for the subscription

Question 6

A company runs a batch job daily that processes large files from Cloud Storage and stores results in BigQuery. The job requires significant compute for about 10 minutes and is fault-tolerant. Which compute option is most cost-effective?

Accepted Answer

Preemptible VM. Option D is correct because Preemptible VMs offer the same compute capacity as regular VMs at a significantly lower cost (up to 80% discount), and since the batch job runs for only 10 minutes daily and is fault-tolerant, it can handle the occasional preemption without data loss. The job's short duration and fault tolerance make preemptible instances ideal, as they can be restarted if terminated.

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Cloud Run Jobs

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Always-on Compute Engine VM

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GKE cluster with a single node

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Cloud Functions (9-minute timeout)

Question 7

A financial services company has a critical application that must survive a regional outage. They deployed on Compute Engine across multiple zones within a single region and now want to redirect traffic to a secondary region if the primary region becomes unavailable. Which load balancing solution should they use?

Accepted Answer

External HTTP(S) Load Balancer. The External HTTP(S) Load Balancer is the correct choice because it supports global load balancing across multiple regions, enabling traffic failover to a secondary region when the primary region becomes unavailable. It uses anycast IP addresses and is designed for HTTP/S traffic, making it suitable for a critical application that must survive a regional outage.

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SSL Proxy Load Balancer

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Proxy Network Load Balancer

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Internal TCP/UDP Load Balancer

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Network Load Balancer

Question 8

A company uses GKE with cluster autoscaling and node auto-upgrade. During a traffic spike, new pods are unschedulable even though the cluster autoscaler adds nodes. What is the most likely cause?

Accepted Answer

The pods have tolerations that don't match node taints. Option D is correct because if pods have tolerations that do not match the taints on the nodes, the scheduler will not place them on those nodes, even if the cluster autoscaler has added new nodes. This mismatch prevents scheduling, leading to unschedulable pods despite sufficient node capacity.

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The pods have resource requests that exceed available node capacity

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The cluster autoscaler is disabled

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The node pool has reached its maximum size limit

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The nodes are in unhealthy status

Question 9

A company uses Cloud SQL for MySQL and wants to achieve high availability with automatic failover across zones while minimizing data loss. Which configuration should they use?

Accepted Answer

Configure a regional Cloud SQL instance with automatic failover. A regional Cloud SQL instance with automatic failover provides synchronous replication of data between two zones within the same region, ensuring zero data loss (RPO=0) and automatic failover with minimal downtime (RTO typically under 60 seconds). This meets the requirement for high availability with automatic failover across zones while minimizing data loss.

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Enable read replicas in different zones

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Use external read replicas with a failover script

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Use Cloud SQL Enterprise Plus edition

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Enable point-in-time recovery

Question 10

A company uses Cloud Spanner for a global application. They want to improve read performance for point-reads (individual row lookups). Which TWO strategies should they adopt?

Accepted Answer

Create secondary indexes. Secondary indexes in Cloud Spanner allow point-reads to be served directly from the index table, avoiding a full table scan and reducing latency. Interleaved tables store child rows physically adjacent to their parent row, enabling efficient single-row lookups without cross-node coordination.

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Use read replicas

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Partition the table by time

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Use batch reads

Question 11

A company runs a microservices architecture on GKE with gRPC services. They want to implement traffic splitting for canary deployments. Which THREE components should they use?

Accepted Answer

Istio or Anthos Service Mesh. Option B is correct because Istio or Anthos Service Mesh provides fine-grained traffic splitting capabilities for canary deployments in a GKE environment. It uses Envoy sidecar proxies to route a percentage of traffic to different service versions based on HTTP headers or weight, enabling controlled rollouts without modifying application code.

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ClusterIP service

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Headless service

Question 12

A company uses Cloud Load Balancing to distribute traffic to HTTP backends. They want to protect against application-layer DDoS attacks (e.g., HTTP flood). Which TWO services should they combine?

Accepted Answer

Cloud Armor. Cloud Armor is correct because it provides Web Application Firewall (WAF) capabilities and DDoS protection at the application layer, allowing you to create security policies that filter HTTP/HTTPS traffic based on IP addresses, geo-locations, or custom rules (e.g., rate limiting) to mitigate HTTP flood attacks. Cloud CDN is correct because it caches content at edge locations, absorbing a significant portion of malicious traffic before it reaches the backend, reducing the load on origin servers and acting as a first line of defense against volumetric application-layer attacks.

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Cloud Firewall rules

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Cloud NAT

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Cloud Endpoints

Question 13

The developer runs the command above and sees both instances are unhealthy. The instances are running and serving traffic on port 80 when accessed directly. What is the most likely cause?

Accepted Answer

Firewall rules block the health check probe IP ranges. The most likely cause is that firewall rules are blocking the health check probe IP ranges. Google Cloud Platform (GCP) load balancers use specific, documented IP ranges for health check probes. If a firewall rule denies traffic from these ranges, the load balancer will mark the instances as unhealthy even though the instances are running and serving traffic on port 80 when accessed directly. This is a common misconfiguration because the health check probes originate from these special IP ranges, not from the load balancer's frontend IP.

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The instances have been deleted

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The instances are not running the specified health check port

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The load balancer is misconfigured

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The instances are out of memory and unable to respond

Question 14

A developer deploys this Cloud Run service. During a load test, each incoming request starts a new container instance, even though concurrency is set to 80. What is the reason?

Accepted Answer

The container is not designed to handle multiple concurrent requests (single-threaded). Option E is correct because Cloud Run's concurrency setting controls how many requests the runtime can send to a container instance, but the container itself must be capable of handling those requests concurrently. If the application is single-threaded or uses a blocking I/O model (e.g., a simple Flask or Express server without async workers), it can only process one request at a time. Cloud Run detects that the container is busy and starts a new instance for each incoming request, effectively ignoring the concurrency setting.

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The memory limit is too low

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The container is CPU-bound and cannot handle multiple requests concurrently

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The CPU limit is too low

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The concurrency setting of 80 is too high and Cloud Run ignores it

Question 15

A Cloud Function (background function, event-driven) consistently logs this timeout error. The function processes messages from Pub/Sub. After increasing the max instances from 10 to 100, the error rate increases. What is the most likely cause of the timeouts?

Accepted Answer

The function depends on an external service that is rate-limited; scaling up causes more calls and timeouts. Option A is correct because increasing the max instances from 10 to 100 amplifies the number of concurrent function invocations. If the function depends on an external service (e.g., a third-party API or database) that enforces rate limits, the higher concurrency causes more requests to be throttled or rejected, leading to increased timeouts. This is a classic scaling anti-pattern where horizontal scaling exacerbates a bottleneck instead of relieving it.

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Increase memory allocation to speed up processing

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Use a larger instance type (Cloud Functions does not have instance types)

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Migrate the function to Cloud Run for longer timeouts

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The function timeout is set too low; increase it to 9 minutes