Google PCA Analyze and optimize technical and business processes — 15 Questions

Question 1

A company is migrating its on-premises Oracle database to Cloud SQL for PostgreSQL. The database team wants to minimize downtime during migration. Which approach should they use?

Accepted Answer

Use Database Migration Service for PostgreSQL with continuous migration from Oracle via Homogeneous Migration.. Database Migration Service (DMS) for PostgreSQL with continuous migration is the correct approach because it supports ongoing change data capture (CDC) from Oracle to Cloud SQL for PostgreSQL, enabling near-zero downtime. DMS handles schema conversion and data replication continuously, allowing the target to stay synchronized until a cutover, which minimizes downtime compared to batch methods.

Answer

Set up Oracle GoldenGate to replicate to Cloud SQL.

Answer

Take a physical backup of Oracle and restore to Cloud SQL.

Answer

Export the database as a dump file, upload to Cloud Storage, and import into Cloud SQL.

Question 2

An e-commerce platform uses Cloud Spanner for order processing. Recently, latency spikes have occurred during flash sales. The team suspects hot spots due to monotonically increasing order IDs. Which table design change would best solve this?

Accepted Answer

Add a random prefix to the order ID primary key.. Monotonically increasing primary keys (like sequential order IDs) cause hot spots in Cloud Spanner because all writes are directed to a single split (tablet), overwhelming that node. Adding a random prefix (e.g., a hash of the customer ID) distributes writes across multiple splits, eliminating the hot spot and reducing latency spikes during high-throughput flash sales.

Answer

Remove the primary key and let Spanner auto-generate it.

Answer

Use interleaved tables to store orders under customers.

Answer

Create a secondary index on the timestamp column.

Question 3

A startup uses Cloud Functions with a Pub/Sub trigger to process incoming orders. They notice that the function sometimes fails to process messages, and those messages are lost. What is the most likely cause?

Accepted Answer

The Cloud Function is configured with retry on failure set to false.. Option D is correct because when a Cloud Function fails to process a Pub/Sub message and retry on failure is set to false, the message is not redelivered. Pub/Sub relies on the subscriber (the Cloud Function) to acknowledge messages; without retries, a failed execution causes the message to be dropped after the ack deadline expires, leading to message loss.

Answer

The subscription has an ackDeadlineSeconds of 600.

Answer

The Cloud Function has a timeout of 540 seconds.

Answer

The Pub/Sub topic has a retention duration of 10 minutes.

Question 4

A company uses BigQuery for analytics. They have a large partitioned table that is queried frequently. The query performance has degraded over time. Which optimization should they try first?

Accepted Answer

Apply clustering on frequently filtered columns.. Clustering on frequently filtered columns reorganizes the data within partitions based on the values of those columns, which allows BigQuery to prune blocks more effectively during queries. This directly addresses the performance degradation by reducing the amount of data scanned, without requiring additional storage or compute resources.

Answer

Create a materialized view for each frequent query.

Answer

Increase the number of slots for the project.

Answer

Denormalize the table to reduce joins.

Question 5

An organization runs a Kubernetes cluster on GKE with cluster autoscaling enabled. They notice that pods are frequently in 'Pending' state due to insufficient CPU, but the cluster autoscaler does not add nodes quickly enough. What is the most likely cause?

Accepted Answer

The node pool has reached the maximum node count limit.. Option D is correct because the cluster autoscaler cannot add new nodes if the node pool has already reached its maximum node count limit. This limit is configured at the node pool level in GKE, and once reached, the autoscaler will not scale up further, leaving pods in 'Pending' state due to insufficient CPU resources.

Answer

The cluster autoscaler is using the 'least-waste' expander.

Answer

The horizontal pod autoscaler (HPA) is misconfigured.

Answer

The pod disruption budget (PDB) is too restrictive.

Question 6

A company wants to reduce costs for a Cloud Storage bucket that stores infrequently accessed archival data. The data is accessed roughly once a quarter. Which storage class should they use?

Accepted Answer

Archive storage class.. The Archive storage class is the correct choice because it is designed for data accessed less than once a year, making it ideal for quarterly-accessed archival data. It offers the lowest storage cost among Google Cloud Storage classes, though it incurs higher retrieval costs and a minimum 365-day storage duration, which aligns with infrequent access patterns.

Answer

Nearline storage class.

Answer

Standard storage class.

Answer

Coldline storage class.

Question 7

A company is designing a disaster recovery plan for a critical application running on Compute Engine with a regional persistent disk. They want to minimize recovery time objective (RTO) and recovery point objective (RPO). Which TWO strategies should they implement? (Choose two.)

Accepted Answer

Use snapshot replication to a secondary region.. Option A is correct because snapshot replication to a secondary region allows you to create and store disk snapshots in a different region, enabling rapid recovery of the application in that secondary region. This minimizes RTO by having the snapshots readily available for creating new disks and instances, and minimizes RPO by scheduling frequent, automated snapshots that capture incremental changes, ensuring data loss is limited to the snapshot interval.

Answer

Take manual snapshots after a failure occurs.

Answer

Create an instance template in the same region.

Answer

Store backups in Cloud Storage with a lifecycle policy.

Question 8

A company is using Cloud Bigtable for time-series data from IoT devices. They are experiencing high latency for queries that scan a large range of rows. Which THREE actions can improve query performance? (Choose three.)

Accepted Answer

Reduce the size of row keys.. Reducing the size of row keys (A) improves query performance because Bigtable stores rows sorted by key, and smaller keys reduce the amount of data that must be scanned and transferred during range scans. This directly lowers I/O and network overhead, which is critical for time-series data where row keys often include timestamps and device IDs.

Answer

Use Key Visualizer to analyze access patterns.

Answer

Switch from SSD storage to HDD storage.

Question 9

A developer ran the command `gcloud compute instances describe instance-1 --zone us-central1-a` and received the above output. They want to create another instance with the same configuration, except with a different external IP. Which action should they take?

Accepted Answer

Create a new instance without specifying a static IP, so it receives an ephemeral IP.. The `gcloud compute instances describe` output shows the instance has an external IP that is ephemeral (not reserved). To create a new instance with the same configuration but a different external IP, the simplest approach is to create a new instance without specifying a static IP, which will automatically assign a new ephemeral IP. This avoids the overhead of reserving and managing a static IP when only a temporary, different address is needed.

Answer

Reserve a new static external IP address and assign it to the new instance.

Answer

Use the same static IP address by releasing and reassigning it.

Answer

None of the above.

Question 10

A developer is deploying a containerized application to Cloud Run. The deployment fails with the error above. What is the most likely cause?

Accepted Answer

The container is listening on port 3000 instead of 8080.. Cloud Run requires containers to listen on the port defined by the `PORT` environment variable, which defaults to 8080. If the container is hardcoded to listen on port 3000, Cloud Run's health checks and routing will fail because the runtime cannot reach the application on the expected port, causing the deployment to fail.

Answer

The container health check is misconfigured.

Answer

The container startup script fails.

Answer

The container does not have a web server.

Question 11

Your company runs a multi-tier web application on Google Kubernetes Engine (GKE). The application consists of a frontend service, a backend API service, and a PostgreSQL database deployed using a StatefulSet with persistent volumes. The backend service exposes a gRPC endpoint. Recently, the team noticed that the backend service experiences intermittent high latency and occasional timeouts. The frontend service is stateless and scales well. The backend service is CPU-bound. The database is not the bottleneck. The cluster has three nodes of type n1-standard-4. The backend service is deployed with 10 replicas, each requesting 1 CPU and 2 Gi memory. Node utilization is around 70% CPU. The team suspects the network is the issue. However, after reviewing the GKE monitoring dashboard, they see that the network bytes sent/received per second for the backend pods is well below the node's network bandwidth limit. The latency spikes seem correlated with periods of high CPU throttling on the backend pods. The backend service's gRPC requests are small (under 1 KB), and the responses are also small. The team has already optimized the application code. What should the team do to reduce latency?

Accepted Answer

Increase the CPU request for the backend pods to 2 CPUs.. The correct answer is C because the latency spikes correlate with CPU throttling, and increasing the CPU request to 2 CPUs ensures that each backend pod receives a guaranteed CPU share, reducing throttling under load. Since the backend is CPU-bound and node utilization is 70%, the current 1 CPU request may be insufficient, causing the Kubernetes CPU manager to throttle the pods when the node's CPU is contended. This directly addresses the root cause without adding unnecessary replicas or memory.

Answer

Increase the number of nodes in the cluster to reduce network contention.

Answer

Increase the number of backend replicas to 20.

Answer

Increase the memory request for the backend pods to 4 Gi.

Question 12

A company runs a monolithic application on Compute Engine. They want to modernize by moving to microservices on Google Kubernetes Engine (GKE) to improve deployment frequency and resource utilization. However, they are concerned about the increased operational complexity. Which approach best balances modernization benefits with operational overhead?

Accepted Answer

Identify stateless components to migrate to Cloud Run, and keep stateful components on GKE with managed services like Cloud Spanner.. Option D is correct because it pragmatically balances modernization benefits with operational overhead by migrating only stateless components to Cloud Run (a fully managed serverless platform that reduces operational complexity) while keeping stateful components on GKE with managed services like Cloud Spanner. This approach improves deployment frequency and resource utilization without requiring a full rewrite, and it leverages Cloud Run's automatic scaling and zero infrastructure management to minimize operational burden.

Answer

Keep the monolithic application on Compute Engine and use Cloud Monitoring to optimize resource utilization.

Answer

Migrate all application components to Cloud Run and use Cloud Tasks for asynchronous communication.

Answer

Rewrite the entire application as microservices and deploy on GKE with Istio for service mesh.

Question 13

A company wants to optimize their cloud spending on Google Cloud. They have a mix of workloads including batch processing, real-time analytics, and web serving. Which TWO strategies should they implement to reduce costs without significant architectural changes? (Choose two.)

Accepted Answer

Use preemptible VMs for batch processing jobs that are fault-tolerant.. Preemptible VMs are short-lived, fault-tolerant instances that cost significantly less than standard VMs, making them ideal for batch processing jobs that can handle interruptions. This strategy directly reduces compute costs without requiring architectural changes, as the workloads are already designed to be resilient to failures.

Answer

Use sustained use discounts for short-lived instances.

Answer

Right-size all Compute Engine instances by analyzing utilization metrics.

Answer

Migrate all web serving workloads to Cloud Functions to benefit from pay-per-use pricing.

Question 14

Refer to the exhibit. A DevOps engineer created this Terraform configuration to deploy a Compute Engine instance. After applying, they notice the instance is not accessible from the internet. What is the most likely cause?

Accepted Answer

No firewall rule allows ingress traffic to the instance.. The most likely cause is that no firewall rule allows ingress traffic to the instance. By default, GCP instances are created with a VPC network that has implied deny-all ingress rules, and unless a specific firewall rule (e.g., allowing tcp:22 for SSH or tcp:80 for HTTP) is applied to the instance's network tags or service account, all inbound traffic from the internet is blocked. The Terraform configuration shown in the exhibit likely omitted a `google_compute_firewall` resource or did not assign the necessary network tags to the instance.

Answer

The machine type e2-medium does not support public IP addresses.

Answer

The instance is not attached to a VPC network.

Answer

The boot disk size is too small to run the operating system.

Question 15

You are a cloud architect for an e-commerce company. Their application runs on Google Kubernetes Engine (GKE) with a Regional cluster. The application consists of a frontend service, a backend service, and a Redis cache. Traffic is routed via an external HTTP(S) Load Balancer to the frontend. Recently, customers have reported intermittent 502 Bad Gateway errors during peak hours. The frontend logs show 'upstream connect error or disconnect/reset before headers. retried and limit reset' errors. The backend service is deployed with 3 replicas, each with resource requests of 1 CPU and 2 GB memory. The cluster autoscaler is enabled with a minimum of 3 nodes and a maximum of 10 nodes, using e2-standard-4 instances. The backend service's HPA is configured with CPU utilization target of 80%. During peak hours, CPU utilization on the backend pods reaches 90%, but the HPA does not scale up. The cluster has sufficient node capacity. What should you do to resolve the issue?

Accepted Answer

Lower the HPA CPU target to 60% and increase the number of replicas min to 5.. The HPA is configured with a CPU utilization target of 80%, but during peak hours, CPU utilization reaches 90% without triggering scale-up. This indicates that the HPA's target utilization is too high relative to the actual load, causing the HPA to not scale because the average CPU utilization across pods may still be below the target when considering the metric calculation. Lowering the HPA CPU target to 60% ensures that the HPA triggers scaling earlier, and increasing the minimum replicas to 5 provides a baseline capacity to absorb traffic spikes, preventing the upstream connect errors from the backend being overwhelmed.

Answer

Change the HPA to use memory utilization instead of CPU.

Answer

Increase the backend service's max connections per pod in the backendConfig.

Answer

Increase the maximum number of nodes in the cluster autoscaler to 20.