Google Cloud Platform: GCP Interview Questions

GCP Interview Questions help candidates prepare for roles related to cloud computing. GCP Interview Questions gives you ideas and makes you confident, which is very important if you are planning for a career in cloud technology. Professionals with good knowledge of cloud services, architecture, and deployment models are in demand. Google Cloud Platform is fast gaining popularity, and there are numerous openings available for qualified candidates.

So, today on this blog, I will discuss some of the most-asked GCP interview questions & answers. So if you are preparing for a GCP interview and want to know some more GCP interview questions or are curious about Google Cloud, you are at the right blog, as today we are going to discuss not only the GCP interview questions but also Learn about Google Cloud. What is GCP? How does it work?

What is Google Cloud Platform?

GCP is a collection of tools that offers cloud services organised by Google’s global network of data centres, which also run core Google products like Search, Gmail, and YouTube. You can equip virtual machines, databases, storage buckets, and AI tools through GCP instead of buying physical services yourself. 

Everything you run on GCP—like storage, apps, or databases—gets organised into a special folder called “projects.” These projects act like buckets that keep everything together, set rules on who can access what, and track your bills for what you use. All the services of this project are controlled mainly by a system called IAM (Google Cloud Identity and Access Management). IAM lets you pick exactly which people or apps get permission for who can edit your work or who can only view your work, so that nothing gets messed up by the wrong hands.  Moreover, You can Learn about the Google Cloud Platform by enrolling in a Google Cloud Course.

Best Google Cloud Platform, Data Engineer or GCP Interview Questions

Beginner-level GCP Interview Questions & Answers

1. What is a compute engine? 

Compute Engine provides scalable virtual machines (VMs) to Google data centres for running databases, apps, or compute tasks such as ML workloads. 

2. What are the cloud storage classes?

Cloud storage classes include Standard (frequent access), Nearline (Monthly), Coldline (Yearly), and Archive (long-term); they all differ in cost and access frequency. 

3. What is BigQuery? 

BigQuery is an undoubtedly Google fully managed, serverless data warehouse designed for analysing massive databases using SQL queries. It works automatically to handle petabytes of data in seconds or minutes with no physical or infrastructure management. In addition, it’s only a major warehouse, where direct queries can be asked on AWS or Azure data without ELT or duplication via its Omni features. Pricing is pay-per-use based on data scanned or flat-rate slots. 

4. What is a project in GCP?

Projects in GCP are top-level containers that organise and isolate all your cloud resources, such as managing bills, enforcing IAM policies for access control, compute instances, storage buckets, and BigQuery datasets. Each project maintains its own setting for billing, authentication, monitoring, APIs, and permissions, ensuring resources just belong to one project at a time. 

5. What happens to persistent disk data if an instance stops?

Persistent disk data is preserved even if the VM stops, shuts down, or restarts. These disks are durable block storage separate from the instance, retaining all data, internal IPs, and MAC addresses even if the guest OS shuts down. 

Contrast with other storage—

Storage Type	Data Fate on Stop
Persistent Disk	Preserved
Local SSD	Lost (unless via CLI flags preserved)
Ephemeral IP	Released

Intermediate-level Data Engineer Interview Questions & GCP Interview Questions—

6. How do you configure autoscaling in Compute Engine? 

To configure autoscaling in GCP, attach an autoscaler to a managed instance group (MIG) using metrics like CPU utilisation, load balancer capacity, or a custom signal. This automatically adds or removes VM instances based on load, optimising costs and performance. 

7. How does IAM grant temporary access?

IAM in Google Cloud Platform grants temporary access primarily through IAM Conditions (time-bound policies) and short-lived credentials generated via service account impersonation. IAM conditions have policy restrictions added to role bindings that automatically expire access after a set time or event. While Short Lived Credentials generates short, temporary OAuth tokens (valid for 1 hour or less) for accounts to impersonate them. 

8. Difference between Project ID and Project Number?

Project Number and Project ID are both unique GCP identifiers, but serve completely different purposes. 

ID is a human-readable string that humans choose or edit during creation, is permanent, globally unique, and used in APIs or URLs and resource names. 

Whereas Project Number is an auto-generated numeric ID, commonly generated and assigned by a computer or Google, it is also permanent and unique, mainly for internal systems and references. 

Both identified projects are equivalent in most APIs, but ID is preferred for human interaction. 

9. What NoSQL services does GCP offer? 

GCP offers several fully managed NoSQL database services optimised for scalability, high throughput, and flexible schemes. 

In Additional, main services include Cloud Firestone, Cloud Datastore, and Cloud Bigtabel. 

Comparison—

Services	Best For	Consistency Model
Cloud Firestone	Clients’ side apps, real-time	Strong/ACID.
Cloud Datastore	Server apps queries	Strong.
Cloud Bigtabel	High-throughput analytics	Eventual.

Advanced Level Google Cloud Platform Interview Questions & GCP Interview Questions —

10. How to monitor/troubleshoot a complex GCP environment?

GCP provides Cloud Monitoring and Cloud Logging as primary tools for monitoring and troubleshooting complex environments spanning Compute Engine, GKE, BigQuery, and more. It tracks numbers like CPU use, memory, and network traffic from all services. You see charts and graphs to spot if anything slows down or breaks. Set alerts to ping you by sending emails, messages, or Slack when the problem starts. 

• Cloud logging grabs all error messages and activity logs. 
• Cloud Trace shows slow parts in your apps. 
• Dashboards group everything in one view.
• Alerts stop outside surprises before they hurt you.
• Uptime checks ping your services from outside.
• SLOs measure whether your service meets the promises.
• Logs Router sends old logs to cheap storage. 

11. How to handle disaster recovery on GCP?

Disaster recovery on GCP means backing up data and apps so you can restart them fast if something goes wrong or it breaks, like a region outage or crash. Backup your data frequently, use Cloud Storage snapshots or backup and DR Service for VMs, databases, and files. Set it to copy to another place automatically. 

Pick a recovery pattern based on speed needs— Warm, Cold & Hot.  

Use a multi-region setup, running apps in 2+ regions with global load balancing to auto-switch traffic if one fails. Test your plan monthly; run fake disasters to check if recovery works well under 1 hour. 

12. What is the difference between Dataflow and Dataproc?

DATAFLOW & DATAPROC

Dataflow	Dataproc
Dataflow is fully managed and serverless; it automatically handles all your computers and scaling for you. Accordingly, it can run big data or live data at once without setup. Perfect for ETL, where you can transform data exactly once, no duplicates.	Dataproc is managed by Hadoop/Spark clusters. Additionally, you can create a cluster of machines at first and then run Spark or Hadoop jobs on it. More control over settings, but you have to manage cluster size, lifecycle, and costs.
Use Dataflow for new pipelines or streaming. Zero ops will have overhead, auto-scale perfectly, portable to other clouds. Will handles real-time data like Pub/Sub to BigQuery seamlessly. Also, it is cheaper for variable loads since it shuts down when idle.	Use Dataproc for existing Spark code or Hadoop lift-and-shift. Faster startup within 90 seconds, blends with legacy jobs. It can autoscale, too, but you pick machine types and pay for idle clusters. It is better for heavy batch jobs like terabyte logs to go to BigQuery.
Dataflow- no infrastructure thinking.	Dataproc- Spark flexibility with some management.

Both connect to Storage, BigQuery, Pub/Sub easily.

13. How does Cloud Composer fit into data workflows?

Cloud Composer is like a smart scheduler for your data jobs on GCP. It acts as the boss that plans when and how different tasks run together in a smooth sequence. In data workflows, Composer handles the big pictures: chaining ELT pipelines, sending notifications, and checking results. It keeps everything organised, so one job waits for another to finish perfectly. 

14. When would you use BigQuery vs. Bigtabel for analytics?

You should use BigQuery for analytics when you need to ask complex questions across huge datasets. It lets you run SQL queries like ‘average sales by region last year’ on petabytes of data fast. Perfect for reports, dashboards, and data science exploration, where you scan everything. 

Use Bigtable for operational analytics needing instant reads and writes. Think of time series like a website click or a lot of sensor data, where apps fetch single records in milliseconds. No SQL—just key lookups for serving live user requests. 

BigQuery = batch analysis (slow, writes, fast complex queries).

Bigtable = Real-time serving (fast reads/writes, simple lookups).

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Conclusion

As we conclude, there are a few things you should remember. If you are planning to give a GCP Interview or a Data Engineering Interview, you should first know about GCP and GCP Interview Questions. To know about it, you must learn carefully, attend masterclasses, join courses, and you can also go through Henry Harwin’s GCP course. Make sure to be consistent and diligent in your journey. Must not forget to go through all the GCP Interview Questions. 

Recommended Reads

1. What is Cloud Computing? Types, Benefits, and Examples
2. What is a Cloud Database?
3. Google Cloud Platform: Overview
4. Top Cloud Computing Skills for Success in 2026
5. Advantages and Disadvantages of Cloud Computing 

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