BigQuery Interview Questions & Answers! Ready for a BigQuery interview but nervous? Do not worry! BigQuery can be intimidating to data analysts at all levels.

BigQuery can be managed with proper preparation and a growth mindset, allowing you to showcase your skills and land your ideal job.

BigQuery Interview Questions & Answers: 

1. What is BigQuery?

BigQuery is a serverless data warehouse component that enables users to analyze, transform, create complicated queries, generate reports, and manage infrastructure scaling. It is available in several locations and can handle both batch and streaming data intake.

2. How does BigQuery ensure performance?

BigQuery’s performance is great since the engine manages its infrastructure. BigQuery’s scale out functionality enables it to automatically handle load balancing when a large volume of data arrives.

3. What are the benefits of using BigQuery?

BigQuery provides various advantages, including support for batch and streaming data ingestion, AI and ML libraries, and fully managed services. It is a robust and adaptable data warehouse system with promising future potential for big data developers.

4. Is BigQuery a true serverless architecture?

BigQuery is a serverless design, however Google maintains a real server to manage infrastructure issues. When an application experience increased demand or requests, Google automatically boosts RAM, hard drive space, and network bandwidth.

5. Do BigQuery available in all regions?

Data centers are deployed in different areas for different services, so BigQuery is available everywhere. Users can take advantage of faster reaction times and improved performance by deploying their systems and applications to the location with the best performance.

6. What is PaaS?

Pay as you go (PaaS) is a concept in which businesses pay for cloud services as they are used, with the remaining days available. This enables businesses to reduce cloud computing costs.

7. What is the concept of federated queries in BigQuery?

Federated queries allow BigQuery to query Google Cloud Storage, Cloud SQL, and RDBMS without transferring data from Google Drive. BigQuery can execute queries on Cloud SQL, Bigtable, Span, and Google Drive data. And here you can learn Google Cloud Platform (GCP) big query interview questions as well.

8. What is BigQuery’s Architecture?

BigQuery’s design is separated, separating storage and compute, making it cost effective and letting customers to pay just for compute when conducting queries. This division enables more effective infrastructure management.

9. What is the first layer of BigQuery’s architecture?

The first layer of BigQuery’s architecture is Colesus, a storage layer similar to Google’s file system. It is a cluster-level file system that replicates stored data.

10. What are the four data-important data layers that BigQuery supports?

BigQuery supports four data-important data layers: ingestion, processing, storage, and visualization.

11. How does BigQuery ensure fault-tolerant data storage?

BigQuery enables fault-tolerant data storage, which means that even if one node fails, the data is still available on another node. Any modifications to the data are retained for seven days, offering a seven-day snapshot of the data.

12. What programming languages does BigQuery support?

BigQuery supports Java, Python, Node, JS, CSOR, Go, Ruby, and PHP. These languages enable BigQuery data processing, analytics, and visualization. Users can write SQL queries, establish data pipelines, and automate BigQuery data transfer using these languages.

13. What are some use cases for BigQuery?

BigQuery is an advanced data engineering platform for replication, programmatic integration, visualization, and ingestion. It can be used for data warehousing, machine learning, and analytics.

14. What is column-oriented storage in BigQuery?

Column-oriented storage is a general storage type that can be used in various databases like AWS, Cassandra, HBase, and SQL databases. This allows for better performance when running aggregation queries on specific columns.

15. What are partitions in BigQuery?

Partitions in BigQuery are significant elements that influence query performance. They specify how data is stored and accessible in a table and can be used to improve speed in a variety of databases and query engines.

16. What is the purpose of partitions in a database?

Partitions are used to improve performance and reduce the amount of data read. They are critical for decreasing query scanning and improving performance.

17. How do partitions work in a database?

Partitions are constructed based on a certain column, such as a city or date, and store data that lies under the defined column. When a query is executed, just the relevant data is returned depending on the partition column, which reduces scanning time.

18. What are the different types of partitions in BigQuery?

Integer range, column, and ingestion time-based partitions are some of BigQuery’s partition kinds. Each kind has its own set of restrictions regarding which columns it can be used with.

19. What is the process for viewing partition details in BigQuery?

To view partition information, select the table name, partition ID, and total number of rows from the project or transaction database. A BigQuery-defined information schema within this schema provides access to all partition information.

20. What are some other features of BigQuery?

BigQuery provides a variety of tools and features to help users explore and analyze data sets, including the ability to save queries, share them with others, and access the saved queries section.

21. What is a scheduled query in BigQuery?

A scheduled query in BigQuery is one that executes on a regular basis and returns results to a table. This is a common practice in data analytics, analytics, engineering, and other disciplines.

22. How do you create a scheduled query in BigQuery?

To create a scheduled query in BigQuery, you can set up a query that will run and usually overwrite another table.

23. How do you format your query for BigQuery?

BigQuery query formatting is done by clicking “more” and going to SQL settings. Standard practice is to save query results in a temporary table. However, you can create a data set called Birds and set a destination table for query results.

24. How do BigQuery users browse data?

Put your BigQuery data in a Google sheet or looker studio to explore it with Python. This displays work data and results in JSON format.

25. What are the two options for creating scheduled queries in BigQuery?

The two options for creating scheduled queries in BigQuery are to create a new scheduled query or update an existing scheduled query.

26. What happens if the query is a live table?

For live tables, the query will overwrite the table with new data. Both tables may have the same name, which is incorrect.

27. What is the purpose of creating a scheduled query in BigQuery?

Scheduled BigQuery queries automate data refresh and update. This can help monitor and analyze data in real time or generate regular reports.

28. What is BigQuery ML?

BigQuery ML is a feature of BigQuery that provides a flexible foundation for machine learning and artificial intelligence by bringing Machine Learning (ML) to their data with BigQuery ML. Integrations with cloud ML engine and TensorFlow enable training powerful models on structured data.

29. What technologies does BigQuery use to enable data integration, transformation, analysis, visualization, and reporting?

BigQuery’s Google infrastructure technologies Dremel, Colossus, Jupiter, and Borg enable data integration, transformation, analysis, visualization, and reporting. After breaking queries, Dremel Query reassembles results. It Mixers gather data, and the team leverages Google’s Jupiter network to send it quickly.

30. What is the separation of storage and compute in BigQuery?

BigQuery separates storage and computation using low-level infrastructure as well as high-level technologies, APIs, and services like as Bigtable, Spanner, and Stubby. Data is often stored in Google Cloud Storage or Amazon S3 and loaded to computers on demand. Jupiter avoids this step, resulting in faster SQL query data reading.

31. What is the ultimate value of BigQuery?

The most significant advantage of BigQuery is its ability to scale for ordinary SQL queries without requiring software, virtual machines, networks, or storage. BigQuery, a serverless database, provides customers with dozens of petabytes with an experience similar to that of casual users.

32. What are tablets and views in BigQuery?

Tablets are collections of columns and rows stored in managed storage and defined by a schema with strongly typed columns of values. Views are virtual tables defined by a skill-carry and allow access control at the view level.

33. What is columnar storage in BigQuery?

Columnar storage in BigQuery allows you to stream data easily to BigQuery tables and update or delete existing values. It supports mutations without limits and uses variations and advancements on columnar storage, such as capacitor, which has several benefits for data warehouse workloads.

34. What is the persistence layer in BigQuery?

The persistence layer in BigQuery is provided by Google’s distributed file system Colossus (Distributed Storage), which ensures durability using erasure encoding to store and distribute redundant chunks of data on multiple physical tests without impacting computing power available for queries.

35. How does BigQuery manage the storage that holds your data?

BigQuery uses columnar storage, so it’s possible to use simply stream data to tables and change or delete information. It enables unlimited mutations and leverages columnar storage innovations like capacitor, which help data warehousing applications.

36. What is the recommended choice for batch use cases in BigQuery?

The recommended choice for batch use cases in BigQuery is Cloud Storage, as it is a durable, highly available, and cost-effective object storage service.

37. What is streaming ingestion in BigQuery?

Streaming ingestion in BigQuery allows for real-time analysis of high volumes of continuously arriving data. It is a way to process data in real-time as it arrives.

38. What is the Cloud Dataflow pipeline in BigQuery?

The Cloud Dataflow pipeline is a common pattern for ingesting real-time data on Google Cloud Platform. It processes data in real-time and then writes the results to BigQuery tables.

39. What is the data transfer service in BigQuery?

The data transfer service is a fully managed service that ingests data from Google software as a service apps, external cloud storage providers, and data warehouse technologies. It automates data movement into BigQuery on a scheduled and managed basis.

40. What is the query execution details in BigQuery?

The BigQuery dashboard shows the execution details of the queries, demonstrating how several queries are executed to obtain the required results.

41. What is the first step in getting data into BigQuery?

Once the list of source files is validated, the files are transformed, copied to BigQuery cloud storage, loaded into BigQuery, and finally used once BigQuery is no longer needed.

42. How does BigQuery prove to be a good platform for dashboard data?

BigQuery’s ability to provide a BigQuery browser-based user interface for A doc analysis makes it an excellent platform for dashboard data. This allows users to dive deeper into the dashboard data.

43. What is the recommendation for dividing data into ears?

To split data into years, Google suggests using BigQuery’s shortcut for union tables. They construct a new table at the end of each year and archive the active ones, so users have the ease of one large table with the performance of multiple smaller ones as needed.

Now, let’s smarter your knowledge in BigQuery in the form of MCQ’S

1) What is BigQuery?

1. Google Cloud Data Service in the Google Compute Cloud

2. The serverless, highly scalable data warehouse component lets users process, transform, write complex queries, generate reports, and perform ETL transmissions.

3. Developers and customers don’t have to worry about infrastructure with serverless architecture.

4. Security is for customers, but Google considers it server-related.

2) What is serverless architecture?

1. In a clustered system, each node handles a specific task,

2. Google handles infrastructure issues, allowing developers to focus on their code.

3. High is a data warehouse component like BigQuery, which is serverless.

4. None of the above

3) What is Google’s serverless architecture?

1. Google handles all infrastructure issues, letting developers focus on code.

2. Google has a physical server for infrastructure difficulties.

3. Developers and customers should not worry about infrastructure.

4. Google is not serverless.

4) What is the purpose of BigQuery’s internal query engine?

1. Easy platform entry for database or data developers. 

2. High performance owing to engine-managed infrastructure.

3. AI and MLP libraries can be processed by BigQuery.

4. Serverless and scalable, it supports AML and fully managed services.

5) Which Google application uses Dremel?

1. Google Maps

2. Google Chrome

3. Google Drive

4. Google BigQuery

6) What is the main advantage of using BigQuery’s column input or storage format and compression algorithm?

1. For every SQL query, it speeds data reading.

2. It scales smoothly without expensive compute resources.

3. It dynamically assigns slots to searches, giving each user millions of disks.

4. It creates DCV-specific U-Ritz call datasets from data tables.

7) Which of the following is not a low-level infrastructure component used by BigQuery for separation of storage and compute?

1. Google Cloud Storage

2. AWS S3

3. Jupiter network

4. Spanner

8) What is the ultimate value of BigQuery?

1. This serverless database gives customers with dozens of petabytes a nearly same experience as their free-time customers.

2. The ideal number of physical charts and data encoding are determined by query access patterns.

3. It creates DCV-specific U-Ritz call datasets from data tables.

4. It allocates server resources to jobs using Google’s Work cluster management system.

9) When it comes to data warehouse workloads, which of these advantages does columnar storage not offer?

1. It allows for faster data reading for every SQL query.

2. It enables seamless scaling to petabytes in storage.

3. It uses variations and advancements on columnar storage, such as capacitor.

4. It supports mutations without limits.

10) Is there any way to make e-commerce recommendation systems that use BigQuery machine learning less beneficial?

1. It enables users to bring any data into BigQuery for seamless analysis.

2. It allows for faster time to insights.

3. It predicts customers in lifetime value.

4. It designs propensity to purchase solutions.

11) Which BigQuery function does Safari not use for their operations team dashboard?

1. It gives Safari’s sales team intelligence.

2. Data can be imported into BigQuery for easy analysis.

3. It lets users delve down into dashboard data.

4. BigQuery is great for analytical heavy lifting and storing results in an inexpensive lamp stack for mass consumption.

12) Which is not a BigQuery data entry step?

1. Validate the list of source files previously loaded into BigQuery.

2. Transform the files.

3. Copy the files to BigQuery cloud storage.

4. Load the files into BigQuery.

13) Which one of the following does not belong to the category of data integration partnerships that BigQuery boasts?

1. Google Cloud Storage.

2. Google Drive.

3. Snowflake.

4. Oracle Redshift data.

14) Which of the following is not a use case for BigQuery’s Google queries smart analytics reference patterns?

1. Sales intelligence.

2. Dashboard data.

3. Ad hoc queries.

4. Predictive analytics.

And i hope you have an perfect overview on BigQuery Interview Questions & Answers.

All the Best!!!