Top Google BigQuery Frequently Asked Interview Questions

Abhishek Pratap Singh 22 Apr, 2024
5 min read


Suppose you are appearing in an interview for the Junior or senior role. In that case, it’s important to have a basic understanding of GCP and BigQuery. So, in this article, you will learn interview questions related to GCP.

You can start introducing BigQuery: “It is a powerful cloud-based data warehousing solution that can handle large-scale data processing tasks, including machine learning, predictive analytics, data visualization, and real-time data streaming.”


You might be asked to share a specific example of a business problem you solved using BigQuery, and prepare recent work and projects.


Note: These questions are just a few examples of the types of questions you might encounter during a GCP BigQuery interview Questions, and answers may vary from person to person.

Table of Contents

10 Bigquery interview questions

Q1. How does BigQuery differ from traditional data warehousings solutions like Oracle or SQL Server?

We can differentiate BigQuery from traditional data warehousing solutions in a few ways,

  • You start querying data right away without setting up infrastructure in BigQuery.
  • It handles large datasets and processes queries quickly using a distributed architecture. It’s serverless, so we don’t need to manage servers or infrastructure.

BigQuery is a modern cloud-based solution that allows for more flexibility and scalability than traditional data warehousing solutions and is easier to use and manage.

Q2.  How do you manage data security and privacy, especially when dealing with sensitive data?

To manage data security and privacy in BigQuery, you can explain to the interviewer:

  • Limit access with IAM roles
  • Encrypt data in transit and at rest
  • Enable audit logging, use data masking
  • Check for compliance certifications
  • Establish data retention policies.

We can help ensure our sensitive data’s confidentiality, integrity, and availability in BigQuery.

Q3.  How do you design a schema for a complex data model, such as a hierarchical or graph database?

Designing a BigQuery schema for a complex data model, such as a hierarchical or graph database, requires careful consideration of the data structure and relationships.

To design a BigQuery schema for a complex data model, you can explain to the interviewer:

  • Identify entities and relationships
  • Normalize the data
  • Choose an appropriate schema type
  • Optimize for query performance
  • Test and iterate as needed.

Q4.  How do you handle streaming data, and what are some best practices for real-time data processing?

We can use BigQuery’s streaming inserts, choose the appropriate data ingestion method, optimize data ingestion, optimize query performance, and implement real-time monitoring and alerting. By implementing these best practices, we can ensure that our real-time data is processed efficiently and accurately and that issues are detected and resolved quickly.

Q5.  How do you integrate BigQuery with other data processing tools like Apache Spark or Apache Beam?

Integrating BigQuery with other data processing tools, like Apache Spark or Apache Beam, can help us to perform complex data analysis tasks. We can use BigQuery’s connectors, APIs, third-party tools, or data transfer services to integrate with these tools. By integrating BigQuery with other data processing tools, we can simplify and enhance our data processing and analysis capabilities.

 Image source: GCP

Q6.  How do you use BigQuery ML to perform machine learning tasks like regression or classification?

To perform machine learning tasks using BigQuery ML, we must prepare our data, choose a model type, create and train it using SQL statements, evaluate its performance, and make predictions. By following the below steps, We can perform machine learning tasks within BigQuery and gain insights from our data more efficiently.


Q7.  How do you monitor performance and usage?

We can track query performance with execution time, bytes processed, and slot usage also; we can Monitor CPU, memory, and network throughput for resource usage. We can also track job completion time, error rates, and concurrency for BigQuery operations.

Q8.  How do you handle versioning, and what are some best practices for data version control?

Version control can be managed using the BigQuery Data Catalog, source control tools like Git, and by maintaining clear documentation of our data pipeline and transformation processes. Best practices for data version control involve using tools like the BigQuery Data Catalog and source control tools, along with maintaining clear documentation of the data pipeline and transformation processes.

Q9.  How do you use BigQuery for data visualization and reporting, and what are some common tools for data visualization?

It can be used for data visualization and reporting by connecting it with visualization tools like Google Data StudioLookerTableau, or Power BI. These tools allow us to create custom dashboards and reports by querying data directly from BigQuery. Common visualization techniques include creating charts, graphs, tables, and other interactive visualizations to help communicate insights from our data.


Q10.What is the difference between BigQuery and traditional databases?

Unlike traditional databases, BigQuery is designed to handle petabytes of data and allows for massively parallel processing of queries. It is fully managed and serverless, eliminating the need for infrastructure provisioning and management.


We covered a variety of questions related to GCP BigQuery. Understanding best practices for designing efficient schemas, managing data security and privacy, monitoring performance and usage, troubleshooting common issues, integrating with other data processing tools, and handling data from different sources and regions is important And you can Get these BigQuery Interview Questions.

Key Takeaways:

  • Understanding how to optimize query performance, including techniques such as partitioning, clustering, and using appropriate data types.
  • Following best data security and privacy practices, such as using encryption and access controls to protect sensitive data.
  • Monitoring performance and usage metrics to identify bottlenecks and optimize resources.

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