SQL interview questions: Zoom

To succeed in a SQL interview for a position at Zoom, you need to have a nuanced understanding of how this technology supports the company’s data-driven initiatives. The interview assesses your ability to handle data efficiently, optimize queries for performance, and design robust database systems that align with Zoom’s operational excellence and innovation ethos.

This article aims to explain the SQL interview questions that candidates may encounter when applying for a role at Zoom. It covers basic SQL principles as well as advanced data manipulation and optimization techniques, reflecting the various SQL use cases within Zoom’s extensive data ecosystem. By focusing on SQL’s real-world application at Zoom, this article provides insights into the technical depth and analytical skills that the company seeks in its candidates.

Common SQL Interview Questions for Zoom

1. Dynamic Data Filtering

Question: Imagine you need to report on user engagement by identifying users who have not participated in any meetings over the last 30 days. How would you approach this using SQL?

This question tests the candidate’s ability to filter data dynamically based on dates and user activity for generating timely insights into user engagement.

Solution: Use a LEFT JOIN to connect users to their meeting participation records, filtering for users with no meetings in the specified timeframe.

SELECT u.user_id, u.name

FROM users u

LEFT JOIN meetings m ON u.user_id = m.user_id AND m.meeting_date > CURRENT_DATE - INTERVAL '30 days'

WHERE m.meeting_id IS NULL;

2. Complex Aggregations

Question: How would you calculate the weekly average number of meetings per user for the past 3 months, and what insights could this data provide for Zoom?

This evaluates the ability to perform complex aggregations over a time series, important for understanding user behavior trends.

Solution: Aggregate meeting data by week, then calculate the average per user. This insight helps Zoom identify periods of increased or decreased user engagement.

SELECT DATE_TRUNC('week', meeting_date) AS week, COUNT(meeting_id) / COUNT(DISTINCT user_id) AS avg_meetings_per_user

FROM meetings

WHERE meeting_date >= CURRENT_DATE - INTERVAL '3 months'

GROUP BY DATE_TRUNC('week', meeting_date);

3. Performance Optimization with Indexing

Question: Given a table ‘user_logs’ with millions of rows, logging user actions within the Zoom app, how would you optimize a query to find the most recent action of a specific user?

Challenges the candidate to apply indexing for query optimization, a key consideration for maintaining performance at scale.

Solution: Suggest creating an index on the user_id and action_timestamp columns to expedite the search for the most recent action.

-- Assuming the creation of an index on (user_id, action_timestamp)

SELECT action, action_timestamp

FROM user_logs

WHERE user_id = '123'

ORDER BY action_timestamp DESC

LIMIT 1;

4. Schema Design for Scalability

Question: Design a database schema to store meeting transcripts that allow efficient searches for specific words or phrases said in any meeting. How would you ensure the schema supports fast searches?

This question probes the candidate’s ability to design a scalable, search-optimized schema, reflecting real needs for storing and querying large text datasets like meeting transcripts.

Solution: Propose a schema where transcripts are stored with meeting metadata and full-text search capabilities are enabled, possibly using specialized text search indices.

-- Example schema creation

CREATE TABLE meeting_transcripts (

    transcript_id SERIAL PRIMARY KEY,

    meeting_id INT,

    transcript TEXT,

    -- Additional metadata fields

    FULLTEXT(transcript)

);

-- Example of creating a full-text index, syntax can vary based on the RDBMS

CREATE FULLTEXT INDEX idx_transcript ON meeting_transcripts(transcript);

5. Identifying peak usage times

Question: “Write an SQL query to identify the peak hour of the day for meetings across the platform for the last year. How can Zoom use this information?”

This question tests the candidate’s ability to aggregate and analyze data across time intervals, key for optimizing resource allocation and understanding user behavior.

Solution: Group meeting data by hour and count occurrences to find the peak hour. This insight helps Zoom in capacity planning and targeted feature promotion.

SELECT EXTRACT(HOUR FROM meeting_start_time) AS hour_of_day, COUNT(*) AS total_meetings

FROM meetings

WHERE meeting_start_time >= CURRENT_DATE - INTERVAL '1 year'

GROUP BY hour_of_day

ORDER BY total_meetings DESC

LIMIT 1;

Zoom Advance SQL Question

See below for a SQL interview question from Zoom:

Find the participant_id of the users invited to overlapping meetings and who have confirmed their participation in at least two disjoint meetings.

Are you ready to put your skills to the test and take one step closer to securing your dream role at Zoom? Click here to access the advanced SQL interview question and begin your journey towards becoming a data engineering expert at Zoom.

Scenario-Based SQL Questions

Scenario-based SQL questions not only assess your technical proficiency but also your ability to apply SQL skills to solve real-world problems. These questions simulate challenges you might face while working at Zoom, providing a lens into your problem-solving approach and your capacity to leverage SQL insights. Below are examples of such scenario-based SQL questions:

Scenario 1: Meeting Engagement Analysis

Question: Zoom is interested in enhancing user engagement during meetings. Given a database with tables for users, meetings, and interactions (where interactions include reactions, comments, and question submissions during a meeting), write a SQL query to identify the top 5 most engaged users in the past month based on the number of interactions.

This question requires you to join the users, meetings, and interactions tables, filtering records for the past month and aggregating interaction counts per user. The query should then order the results by the count of interactions descending and limit the output to the top 5 users.

SELECT u.user_id, u.name, COUNT(i.interaction_id) AS interaction_count

FROM users u

JOIN meetings m ON u.user_id = m.user_id

JOIN interactions i ON m.meeting_id = i.meeting_id

WHERE m.meeting_date BETWEEN CURRENT_DATE - INTERVAL '1 month' AND CURRENT_DATE

GROUP BY u.user_id

ORDER BY interaction_count DESC

LIMIT 5;

Scenario 2: Optimizing Meeting Times

Question: Zoom wants to optimize scheduled meeting times for its new product team spread across different time zones. Using a table named team_members (with columns for member_id, name, time_zone), and another table named meeting_preferences (with member_id, preferred_start_time, preferred_end_time), write a SQL query to find the optimal meeting start time that fits within the preferred time frames of all team members.

This complex query involves calculating overlaps in preferred meeting times across different time zones for all team members. It might require subqueries or window functions to compare each member’s preferences and identify a common time frame that accommodates everyone.

Scenario 3: Feature Rollout Analysis

Question: Zoom plans to roll out a new feature and needs to analyze its impact. Given a table feature_usage (with user_id, feature_name, usage_date, duration), write a SQL query to calculate the average usage duration of the new feature for users who tried it within the first week of its launch, compared to the week after.

The candidate needs to filter usage records by the feature name and calculate the average duration for the specified time frames. This involves conditional aggregation and possibly date functions to align usage records with the feature rollout phases.

SELECT

  CASE

    WHEN usage_date BETWEEN launch_date AND launch_date + INTERVAL '7 days' THEN 'first_week'

    WHEN usage_date BETWEEN launch_date + INTERVAL '8 days' AND launch_date + INTERVAL '14 days' THEN 'second_week'

  END AS rollout_phase,

  AVG(duration) AS avg_duration

FROM feature_usage

WHERE feature_name = 'NewFeature'

GROUP BY rollout_phase;

FAQ: Preparing for Your Zoom SQL Interview

Q: What types of SQL questions are most common in Zoom interviews for data roles?

A: Zoom interviews typically cover a wide range of SQL topics, from writing basic queries to complex data manipulation and optimization scenarios. Expect questions on JOIN operations, aggregate functions, subqueries, window functions, and performance optimization techniques like indexing.

Q: How should I prepare for scenario-based SQL questions at Zoom?

A: To prepare for scenario-based questions, practice with real-world datasets and scenarios. Focus on understanding how to translate business requirements into efficient SQL queries. Reviewing Zoom’s public datasets or similar industry data can provide relevant practice.

Q: Are there any practical SQL coding exercises during the Zoom interview?

A: Yes, practical SQL coding exercises are a key component of the Zoom interview process for data roles. Candidates may be asked to write queries or solve problems in a live coding environment, demonstrating their ability to craft solutions on the spot.

Q: What resources does Zoom recommend for brushing up on SQL skills before the interview?

A: While Zoom doesn’t officially recommend specific resources, candidates find success by practicing on platforms like LeetCode, HackerRank, and Data Engineer Academy. Books such as “Ace the Data Engineer Interview” and online courses SQL Data Engineer Interview tailored to database management and SQL are also highly beneficial.

Q: Will I need to explain the reasoning behind my SQL queries in the interview?

A: Absolutely. Articulating the rationale behind your query choices, structures, and optimization decisions is crucial. It demonstrates not only your technical acumen but also your analytical thinking and problem-solving approach.

Q: Can personal SQL projects or experiences enhance my interview?

A: Yes, discussing personal SQL projects or professional experiences where you’ve applied SQL to solve complex problems can significantly strengthen your interview. It provides concrete evidence of your skills and shows your passion for data work.

Conclusion

Zoom’s SQL interview questions are designed not only to test your technical skills but also to probe your analytical thinking, problem-solving capabilities, and your ability to leverage SQL in deriving meaningful insights from complex datasets. The breadth of questions — from basic query operations to advanced data manipulation and optimization techniques — reflects the comprehensive SQL expertise Zoom seeks in candidates ready to contribute to its data-driven decision-making processes.

Ready to ace your SQL interview at Zoom and advance your data engineering career? Explore the SQL courses and coaching available at Data Engineer Academy today and take the next step toward achieving your professional goals.

Chris Garzon

Christopher Garzon has worked as a data engineer for Amazon, Lyft, and an asset management start up where he was responsible for building the entire Data Infrastructure from scratch. He is the author “Ace the Data Engineer Interview” and has helped 100’s of students break into the data engineer industry. He is also an angel investor, an advisor to multiple to multiple start ups, and the founder and CEO of Data Engineer Academy.