Master Bokeh with real-world interview questions and production-ready scenarios for data professionals.

Python Bokeh Interactive Data Visualization preparation is the definitive bridge between basic charting and building high-performance, enterprise-grade analytical applications. This comprehensive question bank is designed to simulate the rigorous technical assessments used by top-tier tech firms, ensuring you master everything from the foundational ColumnDataSource and low-level bokeh.models to complex client-side CustomJS callbacks that eliminate the need for a running Python backend. We dive deep into the mechanics of the Bokeh Server, exploring session state management and real-time data streaming, while also tackling the architectural challenges of deploying scalable apps via Gunicorn, Nginx, and Datashader for "Big Data" rendering. By engaging with these detailed scenarios, you won't just learn the syntax; you'll gain the design-thinking skills required to architect responsive, secure, and visually stunning dashboards that meet modern production standards.

Exam Domains & Sample Topics

Foundations & Core Glyphs: figure() objects, bokeh.plotting vs models, and JSON serialization.
Interactivity & Callbacks: Widgets (Sliders/Buttons), HoverTools, and standalone CustomJS.
Bokeh Server Applications: Python-to-JS synchronization, curdoc(), and periodic callbacks.
Advanced Layouts & Styling: gridplot, tabs, Theming, and wrapping third-party JS libraries.
Production & Security: Nginx/Gunicorn deployment, Tornado settings, and Datashader integration.

Sample Practice Questions

1. When using a ColumnDataSource (CDS) in Bokeh, which of the following practices is most efficient for updating a specific subset of data without re-sending the entire dataset to the browser?

A. Replacing the entire .data dictionary with a new Python dictionary. B. Using the patch() method to send only the specific changes to the client. C. Calling source. stream() with an empty list to trigger a partial refresh. D. Converting the CDS to a Pandas DataFrame, updating it, and re-converting it back. E. Using a CustomJS callback to manually loop through the data property. F. Re-initializing the figure() object to clear the old data buffers.

Correct Answer: B

Overall Explanation: Bokeh is optimized for performance by minimizing the amount of data sent over the wire. While replacing the dictionary works, it forces a full re-serialization of the data. The patch() method is specifically designed for surgical updates.
Option A Incorrect: This works but is inefficient for large datasets as it sends all data, not just the changes.
Option B Correct: patch() allows you to update specific indices or slices, sending only the "delta" to the browser.
Option C Incorrect: stream() is used for appending new rows of data, not modifying existing values.
Option D Incorrect: Re-converting between formats adds significant overhead and does not solve the serialization bottleneck.
Option E Incorrect: While CustomJS can modify data, it doesn't solve the problem of efficient server-to-client communication for existing models.
Option F Incorrect: This is a "destructive" approach that ruins user experience and causes unnecessary re-rendering of the entire plot UI.

2. In a Bokeh Server application, why is it recommended to use curdoc().add_periodic_callback() rather than a standard Python while True loop with time.sleep()?

A. Bokeh Server does not support standard Python time modules. B. Standard loops block the Tornado event loop, preventing the app from responding to other users. C. add_periodic_callback automatically encrypts the data stream between Python and JS. D. Python loops are executed on the client side, whereas Bokeh callbacks stay on the server. E. time.sleep() causes the browser tab to crash due to memory leaks. F. Standard loops cannot access the ColumnDataSource properties.

Correct Answer: B

Overall Explanation: Bokeh Server is built on the Tornado web framework, which is asynchronous. A blocking loop (while True) prevents the server from processing other events (like mouse clicks or other users).
Option A Incorrect: Python's time module works fine, but its blocking nature is the problem.
Option B Correct: Using the periodic callback allows Tornado to yield control between executions, keeping the app responsive.
Option C Incorrect: Callbacks have nothing to do with data encryption (which is handled by SSL/TLS).
Option D Incorrect: Bokeh Server callbacks always run on the server; standard loops would also run on the server.
Option E Incorrect: It won't crash the browser directly, but it will make the application appear "frozen" or unresponsive.
Option F Incorrect: Standard loops can access properties, but the changes won't be synced to the UI if the event loop is blocked.

3. You are deploying a Bokeh application behind an Nginx reverse proxy and notice that the WebSocket connection fails to establish. Which setting is most likely missing or misconfigured?

A. The --allow-websocket-origin flag on the Bokeh Server command. B. The theme parameter in the curdoc() definition. C. The output_file() path in the Python script. D. The CSS display property in the main HTML template. E. The CDN resource setting in bokeh.resources. F. The version of Python used to run the Gunicorn worker.

Correct Answer: A

Overall Explanation: For security, Bokeh Server restricts WebSocket connections to the host it is running on. When behind a proxy (like Nginx), the "origin" changes, requiring explicit permission.
Option A Correct: You must specify the external domain using --allow-websocket-origin to permit the handshake.
Option B Incorrect: Themes only affect visual styling and have no impact on network protocols.
Option C Incorrect: output_file is for standalone HTML, not for server-based deployments.
Option D Incorrect: CSS properties affect layout and visibility, not network connectivity.
Option E Incorrect: CDN settings dictate where JS/CSS files are loaded from, not how the WebSocket connects.
Option F Incorrect: While versions matter for code compatibility, they don't typically cause specific WebSocket "Origin" failures.

Welcome to the best practice exams to help you prepare for your Python Bokeh Interactive Data Visualization.
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