Cost optimization strategies in GCP - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Understanding Committed Use Discounts

Which statement best describes how Committed Use Discounts (CUDs) help reduce costs in Google Cloud Platform?

A CUDs apply only to on-demand resources and increase costs if usage exceeds the commitment.

B CUDs provide a discount when you commit to using a specific amount of resources for a 1 or 3 year term, regardless of actual usage.

C CUDs automatically reduce costs based on the number of users accessing the service each month.

D CUDs are discounts given only when you use preemptible virtual machines.

💡 Hint

Think about committing to a fixed amount of resource usage over time to get a lower price.

Committed Use Discounts require you to commit to a certain amount of resource usage for 1 or 3 years. In exchange, Google Cloud offers a lower price regardless of whether you use all the committed resources or not.

❓ Architecture

intermediate

Choosing Storage Classes for Cost Savings

You have data that is accessed rarely but must be retained for years. Which Google Cloud Storage class should you choose to minimize costs while meeting this requirement?

A Coldline Storage

B Nearline Storage

C Archive Storage

D Standard Storage

💡 Hint

Consider the storage class designed for long-term data retention with the lowest cost.

Archive Storage is the lowest-cost storage class designed for data accessed less than once a year and retained for long periods. It is ideal for archival data.

❓ security

advanced

Impact of Overprovisioned Resources on Cost and Security

What is a key risk of overprovisioning virtual machines in Google Cloud beyond just increased cost?

A Overprovisioning can lead to increased attack surface and potential security vulnerabilities.

B Overprovisioning automatically disables firewall rules, reducing security.

C Overprovisioning causes Google Cloud to throttle network traffic, impacting performance.

D Overprovisioning results in automatic deletion of unused resources after 30 days.

💡 Hint

Think about how having more resources than needed might affect security exposure.

Having more virtual machines than necessary increases the number of systems exposed to potential attacks, increasing the attack surface and security risks.

❓ service_behavior

advanced

Effect of Autoscaling on Cost Optimization

How does enabling autoscaling on a Google Kubernetes Engine (GKE) cluster help optimize costs?

A Autoscaling adjusts the number of nodes based on workload demand, reducing costs during low usage periods.

B Autoscaling permanently increases the number of nodes to handle peak traffic at all times.

C Autoscaling disables billing alerts to prevent cost notifications during scaling events.

D Autoscaling converts all nodes to preemptible instances automatically.

💡 Hint

Consider how scaling resources up and down affects billing.

Autoscaling dynamically adjusts the number of nodes in the cluster based on workload demand, so you only pay for what you need, saving costs during low traffic.

✅ Best Practice

expert

Designing a Cost-Effective Multi-Region Architecture

You need a multi-region architecture for high availability but want to minimize costs. Which design choice best balances availability and cost?

A Deploy full active-active clusters in all regions with synchronous replication.

B Deploy active resources in all regions but disable load balancing to reduce costs.

C Deploy all resources only in one region and rely on backups for disaster recovery.

D Use a primary region with active resources and a secondary region with cold standby resources.

💡 Hint

Think about how to keep costs low while still having a backup ready in another region.

Using a primary region with active resources and a secondary region with cold standby reduces costs by not running full active resources everywhere, yet still provides failover capability.

Practice

1. Which of the following is a basic way to reduce costs in Google Cloud Platform (GCP)?

easy

A. Use the most powerful machines regardless of need

B. Increase the number of virtual machines

C. Use smaller or cheaper machine types

D. Keep all resources running 24/7

Solution

1. Step 1: Understand cost drivers in GCP

   Machine types with more CPU and memory cost more money.

2. Step 2: Choose cost-saving options

   Using smaller or cheaper machine types reduces the hourly cost.

3. Final Answer:

   Use smaller or cheaper machine types -> Option C

4. Quick Check:

   Smaller machines cost less [OK]

Hint: Pick smaller machines to save money quickly [OK]

Common Mistakes:

• Thinking more machines always save money
• Believing running all resources nonstop is cheaper
• Choosing the most powerful machines by default

2. Which of the following is the correct way to specify a preemptible VM in a GCP instance creation command?

easy

A. gcloud compute instances create my-vm --preemptible

B. gcloud compute instances create my-vm --no-preemptible

C. gcloud compute instances create my-vm --machine-type=preemptible

D. gcloud compute instances create my-vm --enable-preemptible=false

Solution

1. Step 1: Review gcloud syntax for preemptible VMs

   The flag to create a preemptible VM is --preemptible.

2. Step 2: Identify the correct command

   gcloud compute instances create my-vm --preemptible uses --preemptible correctly to enable preemptible VM.

3. Final Answer:

   gcloud compute instances create my-vm --preemptible -> Option A

4. Quick Check:

   Use --preemptible flag to create preemptible VM [OK]

Hint: Look for --preemptible flag to enable preemptible VMs [OK]

Common Mistakes:

• Using --no-preemptible which disables preemptible
• Confusing machine-type with preemptible flag
• Using incorrect flag names

3. Consider this Python snippet using GCP SDK to list VM instances and filter by machine type for cost optimization:

instances = [
  {"name": "vm1", "machine_type": "n1-standard-1"},
  {"name": "vm2", "machine_type": "n1-highmem-4"},
  {"name": "vm3", "machine_type": "n1-standard-1"}
]
cheap_vms = [vm["name"] for vm in instances if vm["machine_type"] == "n1-standard-1"]
print(cheap_vms)

What is the output?

medium

A. ['vm2']

B. ['vm1', 'vm3']

C. ['vm1', 'vm2', 'vm3']

D. []

Solution

1. Step 1: Understand the list comprehension filter

   The code selects VMs where machine_type equals 'n1-standard-1'.

2. Step 2: Identify matching VMs

   vm1 and vm3 have 'n1-standard-1', vm2 does not.

3. Final Answer:

   ['vm1', 'vm3'] -> Option B

4. Quick Check:

   Filter by machine_type 'n1-standard-1' gives vm1 and vm3 [OK]

Hint: Filter list by machine_type to find cheaper VMs [OK]

Common Mistakes:

• Including vm2 which has a different machine type
• Returning empty list by mistake
• Confusing keys in dictionary

4. You wrote this command to create a preemptible VM but it keeps creating normal VMs:

gcloud compute instances create cheap-vm --machine-type=n1-standard-1 --enable-preemptible=false

What is the error and how to fix it?

medium

A. The VM name is invalid; rename it

B. The machine type is wrong; use a cheaper type

C. The command is missing zone specification

D. The flag disables preemptible; use --preemptible instead

Solution

1. Step 1: Analyze the preemptible flag usage

   The flag --enable-preemptible=false disables preemptible VMs.

2. Step 2: Correct the flag to enable preemptible

   Use --preemptible without false to create preemptible VM.

3. Final Answer:

   The flag disables preemptible; use --preemptible instead -> Option D

4. Quick Check:

   Use --preemptible to enable preemptible VMs [OK]

Hint: Use --preemptible flag alone to enable preemptible VMs [OK]

Common Mistakes:

• Using --enable-preemptible=false disables preemptible
• Changing machine type instead of flag
• Ignoring flag syntax errors

5. A company wants to optimize costs by automatically stopping VMs when not in use during nights and weekends. Which GCP service or strategy best supports this goal?

hard

A. Use Cloud Scheduler to trigger Cloud Functions that stop/start VMs on schedule

B. Manually stop VMs every night using the console

C. Use preemptible VMs only without scheduling

D. Increase VM size to reduce runtime

Solution

1. Step 1: Identify automation options for VM management

   Cloud Scheduler can run tasks on a schedule, and Cloud Functions can execute commands like stopping VMs.

2. Step 2: Match automation to cost optimization goal

   Automating stop/start reduces costs by not running VMs when unused.

3. Final Answer:

   Use Cloud Scheduler to trigger Cloud Functions that stop/start VMs on schedule -> Option A

4. Quick Check:

   Automate VM stop/start with Cloud Scheduler and Functions [OK]

Hint: Automate VM stop/start with Cloud Scheduler and Functions [OK]

Common Mistakes:

• Relying on manual stopping which is error-prone
• Using preemptible VMs without scheduling
• Increasing VM size which raises costs