BigQuery & AI Quiz – Complete Questions & Answers

MCQ | Gen AI Academy APAC Edition | Hack2skill

Data is moving faster than ever—and tools like BigQuery and AI are evolving to keep up. From real-time processing and event-driven pipelines to vector search and generative modeling, understanding these concepts is becoming essential for modern data workflows.

 
In this quick quiz-style guide, we break down key concepts across BigQuery, AI agents, and machine learning—making it easy to test your knowledge and sharpen your understanding in just a few minutes.

Q1. In BigQuery Continuous Queries, which type of processing is supported?

• Aggregations with GROUP BY
• ✔ Stateless row-level transformations
• Multi-table JOIN operations
• Window functions with partitions

Q2. Which operation is commonly used in Continuous Queries to write processed results?

• SELECT INTO
• ✔ INSERT INTO
• MERGE TABLE
• UPDATE STREAM

Q3. In an event-driven pipeline using BigQuery, what typically triggers processing?

• A scheduled cron job
• A manual query execution
• ✔ Arrival of new data in the source
• Completion of a dashboard refresh

Q4. In the ADK-based Data Analyst Agent, what defines the agent’s behavior?

• The database schema only
• ✔ The agent’s prompt, purpose, and instructions
• The size of the dataset
• The cloud region

Q5. Why does a schema-aware agent produce more reliable SQL queries?

• It memorizes previous queries
• It generates queries without validation
• ✔ It understands the actual structure of tables before generating queries
• It avoids using SQL entirely

Q6. What is the primary advantage of connecting an AI agent to BigQuery instead of using static datasets?

• Faster internet speed
• ✔ Access to dynamically updated, large-scale datasets
• Elimination of query costs
• Removal of schema requirements

Q7. In the multimodal workflow, what is the role of feature extraction from images?

• Compressing image size
• ✔ Converting images into structured attributes for analysis
• Deleting irrelevant images
• Improving network speed

Q8. In BigQuery ML, what is the purpose of using K-means clustering?

• Predicting future values
• ✔ Grouping similar data points without predefined labels
• Cleaning missing data
• Joining multiple tables

Q9. In a vector-based search system, how are similarity results typically determined?

• Exact keyword matching
• Sorting by timestamp
• ✔ Distance between vector embeddings
• File size comparison

Q10. When using generative AI to create a data science model from a prompt, what is the key benefit?

• Eliminates need for any validation
• ✔ Automatically generates model logic based on high-level intent
• Removes need for data
• Guarantees perfect accuracy