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Analyze and optimize technical and business processes practice questions

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Read each scenario carefully and choose your answer before revealing the explanation. Then check why your choice was right or wrong. Repeat until the reasoning feels automatic.

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Practise Google Professional Cloud Architect Analyze and optimize technical and business processes practice questions — original exam-style scenarios with answer choices, explanations, and analysis of common mistakes.

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Reviewed byJohnson Ajibi· MSc IT Security

20 questionsDomain: Analyze and optimize technical and business processes

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What the exam tests

What to know about Analyze and optimize technical and business processes

Analyze and optimize technical and business processes questions test whether you can apply the concept in context, not just recognise a definition.

How the topic appears in realistic exam-style scenarios.

Which detail in the question changes the correct answer.

How to eliminate plausible but wrong options.

How to connect the question back to the wider exam objective.

Watch out for

Common Analyze and optimize technical and business processes exam traps

▸Answering from memory before reading the full scenario.
▸Missing a constraint such as cost, availability, security, scope or command context.
▸Choosing a broad answer when the question asks for the most specific fix.
▸Ignoring why the wrong options are tempting.

Practice set

Analyze and optimize technical and business processes questions

20 questions · select your answer, then reveal the explanation

Question 1mediummultiple choice

Read the full Analyze and optimize technical and business processes explanation →

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?

Trap 1: Set up Oracle GoldenGate to replicate to Cloud SQL.

GoldenGate is not directly supported for Cloud SQL PostgreSQL.

Trap 2: Take a physical backup of Oracle and restore to Cloud SQL.

Physical backups are not compatible with Cloud SQL PostgreSQL.

Trap 3: Export the database as a dump file, upload to Cloud Storage, and…

This approach requires application downtime during export and import.

Study all Analyze and optimize technical and business processes common traps →

A
Set up Oracle GoldenGate to replicate to Cloud SQL.
Why wrong: GoldenGate is not directly supported for Cloud SQL PostgreSQL.
B
Use Database Migration Service for PostgreSQL with continuous migration from Oracle via Homogeneous Migration.
DMS supports minimal downtime via continuous replication.
C
Take a physical backup of Oracle and restore to Cloud SQL.
Why wrong: Physical backups are not compatible with Cloud SQL PostgreSQL.
D
Export the database as a dump file, upload to Cloud Storage, and import into Cloud SQL.
Why wrong: This approach requires application downtime during export and import.

Full breakdown with real-world context →

Question 2hardmultiple choice

Read the full Analyze and optimize technical and business processes explanation →

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?

Trap 1: Remove the primary key and let Spanner auto-generate it.

Spanner requires a primary key; auto-generation would still be monotonically increasing.

Trap 2: Use interleaved tables to store orders under customers.

Interleaving doesn't solve key hotspotting.

Trap 3: Create a secondary index on the timestamp column.

Secondary indexes don't affect write distribution.

Study all Analyze and optimize technical and business processes common traps →

A
Remove the primary key and let Spanner auto-generate it.
Why wrong: Spanner requires a primary key; auto-generation would still be monotonically increasing.
B
Use interleaved tables to store orders under customers.
Why wrong: Interleaving doesn't solve key hotspotting.
C
Add a random prefix to the order ID primary key.
Randomizing the first part of the key distributes writes across splits.
D
Create a secondary index on the timestamp column.
Why wrong: Secondary indexes don't affect write distribution.

Full breakdown with real-world context →

Question 3easymultiple choice

Read the full Analyze and optimize technical and business processes explanation →

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?

Trap 1: The subscription has an ackDeadlineSeconds of 600.

High ack deadline is not a problem.

Trap 2: The Cloud Function has a timeout of 540 seconds.

Long timeout is fine.

Trap 3: The Pub/Sub topic has a retention duration of 10 minutes.

Even with short retention, messages are not lost if acknowledged quickly.

Study all Analyze and optimize technical and business processes common traps →

A
The subscription has an ackDeadlineSeconds of 600.
Why wrong: High ack deadline is not a problem.
B
The Cloud Function has a timeout of 540 seconds.
Why wrong: Long timeout is fine.
C
The Pub/Sub topic has a retention duration of 10 minutes.
Why wrong: Even with short retention, messages are not lost if acknowledged quickly.
D
The Cloud Function is configured with retry on failure set to false.
If retry is disabled, failed messages are dropped.

Full breakdown with real-world context →

Question 4mediummultiple choice

Read the full Analyze and optimize technical and business processes explanation →

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?

Trap 1: Create a materialized view for each frequent query.

Materialized views help but are not the first optimization.

Trap 2: Increase the number of slots for the project.

More slots improve concurrency but not per-query performance.

Trap 3: Denormalize the table to reduce joins.

Denormalization may reduce joins but increases storage.

Study all Analyze and optimize technical and business processes common traps →

A
Create a materialized view for each frequent query.
Why wrong: Materialized views help but are not the first optimization.
B
Increase the number of slots for the project.
Why wrong: More slots improve concurrency but not per-query performance.
C
Apply clustering on frequently filtered columns.
Clustering sorts data, reducing scanned data for filters.
D
Denormalize the table to reduce joins.
Why wrong: Denormalization may reduce joins but increases storage.

Full breakdown with real-world context →

Question 5hardmultiple choice

Read the full Analyze and optimize technical and business processes explanation →

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?

Trap 1: The cluster autoscaler is using the 'least-waste' expander.

That expander is fine.

Trap 2: The horizontal pod autoscaler (HPA) is misconfigured.

HPA scales pods, not nodes.

Trap 3: The pod disruption budget (PDB) is too restrictive.

PDB prevents voluntary disruptions, not scaling.

Study all Analyze and optimize technical and business processes common traps →

A
The cluster autoscaler is using the 'least-waste' expander.
Why wrong: That expander is fine.
B
The horizontal pod autoscaler (HPA) is misconfigured.
Why wrong: HPA scales pods, not nodes.
C
The pod disruption budget (PDB) is too restrictive.
Why wrong: PDB prevents voluntary disruptions, not scaling.
D
The node pool has reached the maximum node count limit.
Cluster autoscaler cannot exceed max node limit.

Full breakdown with real-world context →

Question 6easymultiple choice

Read the full Analyze and optimize technical and business processes explanation →

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?

Trap 1: Nearline storage class.

Nearline is for data accessed less than once a month.

Trap 2: Standard storage class.

Standard is for frequently accessed data.

Trap 3: Coldline storage class.

Coldline is for data accessed less than once a quarter.

Study all Analyze and optimize technical and business processes common traps →

A
Archive storage class.
Archive is for data accessed less than once a year.
B
Nearline storage class.
Why wrong: Nearline is for data accessed less than once a month.
C
Standard storage class.
Why wrong: Standard is for frequently accessed data.
D
Coldline storage class.
Why wrong: Coldline is for data accessed less than once a quarter.

Full breakdown with real-world context →

Question 7mediummulti select

Read the full Analyze and optimize technical and business processes explanation →

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.)

Trap 1: Take manual snapshots after a failure occurs.

Increases RPO because snapshots are taken after failure.

Trap 2: Create an instance template in the same region.

Doesn't help if the region fails.

Trap 3: Store backups in Cloud Storage with a lifecycle policy.

Backups in Cloud Storage are useful but not the fastest for recovery.

Study all Analyze and optimize technical and business processes common traps →

A
Use snapshot replication to a secondary region.
Snapshots can be replicated to another region for quick restore.
B
Take manual snapshots after a failure occurs.
Why wrong: Increases RPO because snapshots are taken after failure.
C
Create an instance template in the same region.
Why wrong: Doesn't help if the region fails.
D
Store backups in Cloud Storage with a lifecycle policy.
Why wrong: Backups in Cloud Storage are useful but not the fastest for recovery.
E
Create a custom image of the boot disk and copy it to another region.
Custom images can be used to recreate instances in another region quickly.

Full breakdown with real-world context →

Question 8hardmulti select

Read the full Analyze and optimize technical and business processes explanation →

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.)

Trap 1: Use Key Visualizer to analyze access patterns.

Key Visualizer helps identify hot spots but does not directly improve performance.

Trap 2: Switch from SSD storage to HDD storage.

HDD is slower than SSD.

Study all Analyze and optimize technical and business processes common traps →

A
Reduce the size of row keys.
Smaller row keys reduce I/O and improve scan performance.
B
Increase the number of nodes in the Bigtable cluster.
More nodes increase throughput and reduce latency.
C
Use Key Visualizer to analyze access patterns.
Why wrong: Key Visualizer helps identify hot spots but does not directly improve performance.
D
Switch from SSD storage to HDD storage.
Why wrong: HDD is slower than SSD.
E
Use application profiles to route to a single cluster if using replication.
Single-cluster routing avoids cross-cluster latency.

Full breakdown with real-world context →

Question 9mediummultiple choice

Read the full Analyze and optimize technical and business processes explanation →

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?

Exhibit

Refer to the exhibit.

```
project = my-project
zone = us-central1-a
name = instance-1
machineType = n1-standard-4
networkInterfaces[0].accessConfigs[0].natIP = 35.193.10.20
labels:
  env: prod
  app: web
metadata:
  startup-script: echo 'Hello'
```

Trap 1: Reserve a new static external IP address and assign it to the new…

This would assign a static IP, but it might still be different from the original, but the requirement is to get a different IP, not necessarily static. However, the simplest is to get an ephemeral IP.

Trap 2: Use the same static IP address by releasing and reassigning it.

That would use the same IP, not different.

Trap 3: None of the above.

Option B is correct.

Study all Analyze and optimize technical and business processes common traps →

A
Reserve a new static external IP address and assign it to the new instance.
Why wrong: This would assign a static IP, but it might still be different from the original, but the requirement is to get a different IP, not necessarily static. However, the simplest is to get an ephemeral IP.
B
Create a new instance without specifying a static IP, so it receives an ephemeral IP.
Ephemeral IPs are different from the static IP shown.
C
Use the same static IP address by releasing and reassigning it.
Why wrong: That would use the same IP, not different.
D
None of the above.
Why wrong: Option B is correct.

Full breakdown with real-world context →

Question 10hardmultiple choice

Read the full Analyze and optimize technical and business processes explanation →

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

Exhibit

Refer to the exhibit.

```
ERROR: (gcloud.beta.run.deploy) The user-provided container failed to start and listen on the port defined provided by the PORT=8080 environment variable.
Logs:
2023-05-01 12:00:00.000 INFO server started on port 3000
```

Trap 1: The container health check is misconfigured.

The error is about port availability, not health checks.

Trap 2: The container startup script fails.

The logs indicate successful startup.

Trap 3: The container does not have a web server.

The logs show it started on port 3000, so a server is running.

Study all Analyze and optimize technical and business processes common traps →

A
The container is listening on port 3000 instead of 8080.
Cloud Run requires the container to listen on the port defined by the PORT environment variable, which defaults to 8080.
B
The container health check is misconfigured.
Why wrong: The error is about port availability, not health checks.
C
The container startup script fails.
Why wrong: The logs indicate successful startup.
D
The container does not have a web server.
Why wrong: The logs show it started on port 3000, so a server is running.

Full breakdown with real-world context →

Question 11hardmultiple choice

Read the full Analyze and optimize technical and business processes explanation →

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?

Trap 1: Increase the number of nodes in the cluster to reduce network…

Network is not the bottleneck; CPU throttling is.

Trap 2: Increase the number of backend replicas to 20.

More replicas with same CPU request could worsen throttling on the same nodes.

Trap 3: Increase the memory request for the backend pods to 4 Gi.

Memory is not the issue.

Study all Analyze and optimize technical and business processes common traps →

A
Increase the number of nodes in the cluster to reduce network contention.
Why wrong: Network is not the bottleneck; CPU throttling is.
B
Increase the number of backend replicas to 20.
Why wrong: More replicas with same CPU request could worsen throttling on the same nodes.
C
Increase the CPU request for the backend pods to 2 CPUs.
More CPU will reduce throttling and latency.
D
Increase the memory request for the backend pods to 4 Gi.
Why wrong: Memory is not the issue.

Full breakdown with real-world context →

Question 12mediummultiple choice

Read the full Analyze and optimize technical and business processes explanation →

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?

Trap 1: Keep the monolithic application on Compute Engine and use Cloud…

Does not achieve modernization benefits.

Trap 2: Migrate all application components to Cloud Run and use Cloud Tasks…

Cloud Run has limitations for stateful workloads, increasing complexity.

Trap 3: Rewrite the entire application as microservices and deploy on GKE…

This increases operational complexity without a phased approach.

Study all Analyze and optimize technical and business processes common traps →

A
Keep the monolithic application on Compute Engine and use Cloud Monitoring to optimize resource utilization.
Why wrong: Does not achieve modernization benefits.
B
Migrate all application components to Cloud Run and use Cloud Tasks for asynchronous communication.
Why wrong: Cloud Run has limitations for stateful workloads, increasing complexity.
C
Rewrite the entire application as microservices and deploy on GKE with Istio for service mesh.
Why wrong: This increases operational complexity without a phased approach.
D
Identify stateless components to migrate to Cloud Run, and keep stateful components on GKE with managed services like Cloud Spanner.
Balances modernization with reduced complexity by using serverless where appropriate.

Full breakdown with real-world context →

Question 13hardmulti select

Read the full Analyze and optimize technical and business processes explanation →

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.)

Trap 1: Use sustained use discounts for short-lived instances.

Sustained use discounts apply automatically, but short-lived instances may not benefit as much.

Trap 2: Right-size all Compute Engine instances by analyzing utilization…

Requires changes to instance types and may involve downtime.

Trap 3: Migrate all web serving workloads to Cloud Functions to benefit…

Significant architectural change and may not be suitable for all web serving.

Study all Analyze and optimize technical and business processes common traps →

A
Use sustained use discounts for short-lived instances.
Why wrong: Sustained use discounts apply automatically, but short-lived instances may not benefit as much.
B
Use preemptible VMs for batch processing jobs that are fault-tolerant.
Preemptible VMs are cost-effective for fault-tolerant workloads.
C
Purchase committed use discounts for 1-year or 3-year terms for stable workloads.
Provides significant discounts without changing architecture.
D
Right-size all Compute Engine instances by analyzing utilization metrics.
Why wrong: Requires changes to instance types and may involve downtime.
E
Migrate all web serving workloads to Cloud Functions to benefit from pay-per-use pricing.
Why wrong: Significant architectural change and may not be suitable for all web serving.

Full breakdown with real-world context →

Question 14easymultiple choice

Read the full Analyze and optimize technical and business processes explanation →

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?

Exhibit

Refer to the exhibit.

```
resource "type" "name" {
  project = "my-project"
  name    = "example-instance"
  machine_type = "e2-medium"
  zone         = "us-central1-a"
  boot_disk {
    initialize_params {
      image = "debian-cloud/debian-11"
      size  = 10
      type  = "pd-standard"
    }
  }
  network_interface {
    network = "default"
    access_config {
      // Ephemeral public IP
    }
  }
}
```

Trap 1: The machine type e2-medium does not support public IP addresses.

Machine type does not determine public IP support.

Trap 2: The instance is not attached to a VPC network.

The configuration specifies the 'default' network.

Trap 3: The boot disk size is too small to run the operating system.

10 GB is sufficient for Debian.

Study all Analyze and optimize technical and business processes common traps →

A
The machine type e2-medium does not support public IP addresses.
Why wrong: Machine type does not determine public IP support.
B
The instance is not attached to a VPC network.
Why wrong: The configuration specifies the 'default' network.
C
No firewall rule allows ingress traffic to the instance.
Firewall rules are needed to allow inbound traffic; the default network may not have appropriate rules.
D
The boot disk size is too small to run the operating system.
Why wrong: 10 GB is sufficient for Debian.

Full breakdown with real-world context →

Question 15mediummultiple choice

Review the full routing breakdown →

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?

Trap 1: Change the HPA to use memory utilization instead of CPU.

The issue is CPU-related; memory may not be the bottleneck.

Trap 2: Increase the backend service's max connections per pod in the…

This addresses connection limits but does not solve the scaling issue.

Trap 3: Increase the maximum number of nodes in the cluster autoscaler to…

The issue is pod scaling, not node availability. More nodes won't help if HPA doesn't scale pods.

Study all Analyze and optimize technical and business processes common traps →

A
Change the HPA to use memory utilization instead of CPU.
Why wrong: The issue is CPU-related; memory may not be the bottleneck.
B
Lower the HPA CPU target to 60% and increase the number of replicas min to 5.
Lowering the target triggers scaling earlier, and more min replicas provide baseline capacity.
C
Increase the backend service's max connections per pod in the backendConfig.
Why wrong: This addresses connection limits but does not solve the scaling issue.
D
Increase the maximum number of nodes in the cluster autoscaler to 20.
Why wrong: The issue is pod scaling, not node availability. More nodes won't help if HPA doesn't scale pods.

Full breakdown with real-world context →

Question 16mediummulti select

Read the full NAT/PAT explanation →

A company is using Cloud NAT to allow instances in a private subnet to access the internet for updates. The security team wants to audit outbound connections. Which TWO steps should be taken to enable flow logs for Cloud NAT?

Trap 1: Enable private Google access on the subnet

Private Google Access does not provide outbound internet flow logs.

Trap 2: Enable VPC Flow Logs on the subnet

VPC flow logs capture network traffic but not specifically NAT gateway flows; also not the recommended way for Cloud NAT auditing.

Trap 3: Create a new Cloud NAT gateway with logging enabled

Creates a redundant gateway without addressing the existing one.

Study all Analyze and optimize technical and business processes common traps →

A
Enable private Google access on the subnet
Why wrong: Private Google Access does not provide outbound internet flow logs.
B
Configure a log sink to export Cloud NAT logs to BigQuery
Exporting to BigQuery enables analysis and auditing of the flow logs.
C
Enable VPC Flow Logs on the subnet
Why wrong: VPC flow logs capture network traffic but not specifically NAT gateway flows; also not the recommended way for Cloud NAT auditing.
D
Create a new Cloud NAT gateway with logging enabled
Why wrong: Creates a redundant gateway without addressing the existing one.
E
Update the existing Cloud NAT gateway to enable flow logs
Flow logs are enabled directly on the Cloud NAT gateway resource.

Full breakdown with real-world context →

Question 17hardmultiple choice

Read the full NAT/PAT explanation →

A team deployed the Terraform configuration shown in the exhibit. They observe that Cloud NAT is not translating traffic from the private subnet as expected. What is the most likely cause?

Exhibit

Refer to the exhibit.

```
resource "google_compute_router_nat" "nat" {
  name   = "my-nat"
  router = google_compute_router.router.name
  region = "us-central1"
  nat_ip_allocate_option = "AUTO_ONLY"
  source_subnetwork_ip_ranges_to_nat = "LIST_OF_SUBNETWORKS"
  subnetworks {
    name                    = google_compute_subnetwork.private.self_link
    source_ip_ranges_to_nat = ["PRIMARY_IP_RANGE"]
  }
  log_config {
    enable = true
    filter = "ERRORS_ONLY"
  }
}
```

Trap 1: The log filter is set to ERRORS_ONLY, which suppresses all logs

Logging configuration does not affect NAT functionality.

Trap 2: The NAT IP allocation is manual and no IPs were specified

The configuration uses AUTO_ONLY, so IPs are automatically allocated.

Trap 3: The subnet is not included in the…

The subnet is specified in the subnetworks block, which overrides the list. So it is included.

Study all Analyze and optimize technical and business processes common traps →

A
The Cloud Router is not in the same VPC network as the private subnet
Cloud NAT requires the router to be in the same VPC network as the subnet. If the router is in a different VPC, NAT will not work.
B
The log filter is set to ERRORS_ONLY, which suppresses all logs
Why wrong: Logging configuration does not affect NAT functionality.
C
The NAT IP allocation is manual and no IPs were specified
Why wrong: The configuration uses AUTO_ONLY, so IPs are automatically allocated.
D
The subnet is not included in the source_subnetwork_ip_ranges_to_nat list
Why wrong: The subnet is specified in the subnetworks block, which overrides the list. So it is included.

Full breakdown with real-world context →

Question 18easymultiple choice

Read the full Analyze and optimize technical and business processes explanation →

A startup runs a web application on Google Kubernetes Engine (GKE) with 3 replicas serving user traffic. They use Cloud SQL for the database. Recently, the application experienced intermittent timeouts during peak hours. Monitoring shows high CPU usage on the GKE nodes and increased database connection pool exhaustion. The team is looking for a cost-effective solution that minimizes architectural changes. The application is stateless. What should they do?

Trap 1: Add more nodes to the GKE cluster and enable cluster autoscaling

Adding nodes without increasing pods doesn't reduce CPU usage per node; connection pool issue remains.

Trap 2: Vertically scale the GKE node pool to larger machine types and…

Vertical scaling is costly and may not solve connection pool exhaustion; also architectural change is minimal but not optimal.

Trap 3: Set up a Cloud SQL read replica and route read queries to it

Read replica does not reduce write connection pool exhaustion; also adds complexity and cost.

Study all Analyze and optimize technical and business processes common traps →

A
Add more nodes to the GKE cluster and enable cluster autoscaling
Why wrong: Adding nodes without increasing pods doesn't reduce CPU usage per node; connection pool issue remains.
B
Increase the number of pod replicas and configure a connection pooler like PgBouncer for Cloud SQL
More pods distribute CPU load, and a connection pooler reduces database connections, addressing both issues cost-effectively.
C
Vertically scale the GKE node pool to larger machine types and increase Cloud SQL tier
Why wrong: Vertical scaling is costly and may not solve connection pool exhaustion; also architectural change is minimal but not optimal.
D
Set up a Cloud SQL read replica and route read queries to it
Why wrong: Read replica does not reduce write connection pool exhaustion; also adds complexity and cost.

Full breakdown with real-world context →

Question 19mediumdrag order

Read the full Analyze and optimize technical and business processes explanation →

Drag and drop the steps to set up a Cloud SQL for PostgreSQL instance with high availability into the correct order.

Drag steps to the numbered slots on the right, or tap a step then tap a slot.

Steps

Order

1Step 1

2Step 2

3Step 3

4Step 4

5Step 5

Question 20mediumdrag order

Read the full Analyze and optimize technical and business processes explanation →

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

Drag steps to the numbered slots on the right, or tap a step then tap a slot.

Steps

Order

1Step 1

2Step 2

3Step 3

4Step 4

5Step 5

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Exam traps to avoid

▸Answering from memory before reading the full scenario.
▸Missing a constraint such as cost, availability, security, scope or command context.
▸Choosing a broad answer when the question asks for the most specific fix.
▸Ignoring why the wrong options are tempting.

Analyze and optimize technical and business processes practice questions

What to know about Analyze and optimize technical and business processes

Common Analyze and optimize technical and business processes exam traps

Analyze and optimize technical and business processes questions

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?

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?

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?

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?

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?

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?

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.)

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.)

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?

Exhibit

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

Exhibit

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.)

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?

Exhibit

A company is using Cloud NAT to allow instances in a private subnet to access the internet for updates. The security team wants to audit outbound connections. Which TWO steps should be taken to enable flow logs for Cloud NAT?

A team deployed the Terraform configuration shown in the exhibit. They observe that Cloud NAT is not translating traffic from the private subnet as expected. What is the most likely cause?

Exhibit

Drag and drop the steps to set up a Cloud SQL for PostgreSQL instance with high availability into the correct order.

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

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