Google PCA Analyze and optimize technical and business processes Practice Test 5 — 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 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 4

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.. 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 5

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 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 6

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.. 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 7

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 8

Drag and drop the steps to implement a disaster recovery plan using Cloud Storage and Cloud Functions in the correct order.

Accepted Answer

Enable versioning on the primary bucket, then create a Cloud Function to copy new objects to the DR bucket, then test the procedure.. Versioning protects against accidental deletion. The Cloud Function copies objects to the DR bucket.

Answer

Create a Cloud Function to copy new objects to the DR bucket, then enable versioning on the primary bucket, then test.

Answer

Create a Cloud Function, then test, then enable versioning.

Answer

Enable versioning, then test, then create the Cloud Function.

Question 9

Match each GCP migration term to its description.

Accepted Answer

Migrate for Compute Engine: Migrates virtual machines to Compute Engine with minimal downtime.. Correct matches: Migrate for Compute Engine migrates VMs; Cloud Storage Transfer Service handles online data transfers; Transfer Appliance is a physical device for large datasets; Database Migration Service migrates databases to Cloud SQL. Common confusions include swapping the roles of these services.

Answer

Migrate for Compute Engine: Transfers data from online sources to Cloud Storage.

Answer

Cloud Storage Transfer Service: Physical device to transfer large datasets to Google Cloud.

Question 10

A startup deploys a web application on Compute Engine instances behind an HTTP load balancer. They need to handle unpredictable spikes in traffic with minimal operational overhead. What is the simplest scaling approach?

Accepted Answer

Use a managed instance group with autoscaling based on CPU utilization. Using a managed instance group with autoscaling automatically adds/removes instances based on demand, requiring minimal manual intervention. Other options either require more complex setup or are not optimal.

Answer

Set up a Kubernetes cluster with horizontal pod autoscaling

Answer

Migrate the application to Cloud Run

Answer

Add more instances manually during peak hours

Question 11

A company stores backup data in Cloud Storage. They observe high egress costs when clients download backups. Additionally, they must retain backups for 7 years for compliance. Which optimization should they implement first?

Accepted Answer

Enable requester pays on the bucket. High egress costs occur when clients download backups, and enabling requester pays shifts these costs to the clients. This directly addresses the cost issue without changing storage class or retention. Requester pays is the first optimization because it resolves the immediate cost problem while lifecycle rules or storage class changes address separate concerns like retention or storage cost.

Answer

Use lifecycle rules to transition to Archive after 30 days and delete after 7 years

Answer

Set up a Cloud CDN for backup downloads

Answer

Move the backup data to Archive storage class

Question 12

A company runs a streaming data pipeline using Dataflow to process real-time data and insert into BigQuery. Recently, workers are frequently failing with out-of-memory errors and the pipeline latency is increasing. What should they do to resolve the issue?

Accepted Answer

Enable Dataflow Streaming Engine. Dataflow Streaming Engine offloads the streaming data processing state and shuffle data from worker memory to a backend service, reducing memory pressure on workers. This directly addresses out-of-memory errors and latency increases without requiring manual scaling or machine type changes. It is the recommended solution for streaming pipelines experiencing memory bottlenecks.

Answer

Increase the worker machine type and memory

Answer

Use Cloud Pub/Sub for buffering and then load into BigQuery in batches

Answer

Enable autoscaling and increase the maximum number of workers

Question 13

A developer is migrating a stateful application to GKE. The application requires persistent storage with high IOPS for a database. Which storage option is most suitable?

Accepted Answer

Persistent Disk SSD. Persistent Disk SSD (pd-ssd) is the most suitable option for a stateful database on GKE requiring high IOPS because it provides block storage with consistent, high-performance IOPS and can be dynamically provisioned via PersistentVolumeClaims. Unlike Local SSD, pd-ssd persists data independently of the node lifecycle, ensuring data durability during pod rescheduling or node failures.

Answer

Local SSD

Answer

Cloud Storage Fuse

Answer

Persistent Disk Standard

Question 14

A company uses Cloud Composer to manage Apache Airflow workflows. They want to optimize costs. Which practice is most effective?

Accepted Answer

Configure auto scaling for the Cloud Composer environment. Cloud Composer supports autoscaling for its workers, which dynamically adjusts the number of worker pods based on the Airflow task queue depth. This directly optimizes costs by scaling down during low-load periods and scaling up only when needed, avoiding over-provisioning.

Answer

Use preemptible VMs for Airflow schedulers

Answer

Replace Cloud Composer with Cloud Functions for all workflows

Answer

Use small machine types for all Composer components

Question 15

A company uses Cloud Armor to protect their HTTP load balancer. They need to block traffic from a specific set of IP addresses and also prevent SQL injection attacks. Which two configurations should they use? (Choose TWO.)

Accepted Answer

Security policies with IP deny rules. Option D is correct because Cloud Armor security policies support IP deny rules (e.g., a rule with action 'deny(403)' and a srcIpRanges match condition) that block traffic from a specified set of source IP addresses at the HTTP(S) load balancer edge. Option E is correct because Cloud Armor provides preconfigured WAF rules, including the 'sqli' (SQL injection) rule set based on ModSecurity CRS signatures, which detect and block SQL injection attempts when attached to the backend service. Options A, B, and C are incorrect: IAM roles govern identity and API access rather than filtering malicious HTTP traffic, VM firewall rules and VPC ingress rules operate at Layers 3/4 on instances or subnets and cannot inspect HTTP payloads for SQL injection, and they are not the Cloud Armor mechanism for protecting an HTTP(S) load balancer.

Answer

IAM roles to restrict access

Answer

Firewall rules on the VM instances

Answer

Ingress rules on the VPC network