databricks take home assignment

General Topics

Databricks interview

I had a talk with a recruiter today and he sent me a mail about the steps. Man there are so many steps. Most concerning are a take home assignment after the onsite and reference check. Who the F does all this. Why can’t they train their interviews better to judge better!!! TC: 160k #databricks #interview

Take home after the on-site ?? 😂

Yeah that’s so horrible

8 rounds in total

I thought the interview process was really easy

How is Splunk?

Lol what’s the point of the on-site if you have more to do after

Lol yeah I said no ty after the screen, which they gave me an assignment 🤣 I got 6 offers after that, never looked back.

Can you share what was in the assignment?

8 rounds? That’s ridiculous. Seems like they are still trying to refine their interview process and have not trained their interviewers to follow a playbook. Granted, they are a hot start-up at the moment and can put their candidates through the ringer if they chose to.

I don’t think they are trying anything like that. They just want have a superiority complex

So i went through this process. I can confirm its real. The process took months. The take home assignment generally is something only done if the candidate did not perform 100%. If the candidate was on the edge, they will do a takehome. 1. Sys design phone screen 2. onsite (1 sys, 2 coding, 1 behavoral) 3. HC: leaning yes, not sure 4. References (actually called) and take home 5. Request for me to fix part of my take home 6. Offer It was an insanely long process and im hoping its something we can slim down a bit. But honestly thats just part of it. Databricks is trying to set a really high bar and that means more steps :(

Did they pay well? Just curious

Yes but very equity heavy

I don't know where all the talk of 8 rounds comes from. I recently went through the process and had only 6 rounds, 2 phone and 4 onsite. Not much more than usual. Time from second phone interview to offer was only two weeks, I had other offers so they expedited everything and I was also able to skip the take-home. Reference checks are brutal yes but honestly I found it kinda fun and I think my references also enjoyed it. Would suck if every company did them but I can imagine that this approach does select for better colleagues. Plus in the end the TC is worth it. L4 TC here is basically L5 TC elsewhere, you're not getting any higher offers outside of trading companies and a few select much more risky startups.

Could you share your interview questions experience please?

I interviewed for a full-stack role, for which they tend to ask more practical frontend coding questions. I'm not going to say more than that because I signed an NDA and don't want to undermine their process.

Tech Industry

Google google yes

No man is 100% straight

2024 Presidential Election

There’s going to be an emergency shut down

Personal Finance

Hit new milestone: 1.4M at 29F

I hope none of you ever gets a GC

• Microsoft Azure
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• DatabricksIQ-powered features

Use Databricks Assistant

This feature is currently in Public Preview . Usage of the feature during the preview is free. Final pricing will be established and communicated prior to general availability (GA).

Databricks Assistant is a context-aware AI assistant that is available in Databricks notebooks, SQL editor, and file editor. Databricks Assistant lets you query data using a conversational interface, making you more productive inside Databricks. You can describe your task in English and let the assistant generate Python code or SQL queries, explain complex code, and automatically fix errors. The assistant uses Unity Catalog metadata to understand your tables, columns, descriptions, and popular data assets across your company to provide personalized responses.

Databricks Assistant can perform the following tasks:

Generate code.

Debug code, including identifying and suggesting fixes for errors.

Transform and optimize code.

Explain code.

Help you find relevant information in the Databricks documentation.

Databricks Assistant is enabled by default. An admin can disable or enable Databricks Assistant for all workspaces in an account. If an admin has permitted workspace setting overrides, workspace admins can enable or disable Databricks Assistant for specific workspaces. For more details, see Enable or disable Databricks Assistant .

Enablement of the Databricks Assistant for your account is captured as an account event in your audit logs, see Account-level account events .

Additional information

The following articles contain additional information about using Databricks Assistant:

What is Databricks Assistant?

DatabricksIQ trust and safety .

How to use Databricks Assistant

Databricks Assistant is available in notebooks, SQL editor, file editor, and dashboards. This document focuses on notebooks. Usage in the other products is similar. For information about using Databricks Assistant to create visualizations for dashboards, see Create visualizations with Databricks Assistant .

In a notebook, Databricks Assistant is available in the Assistant pane or inline in a code cell.

Use Databricks Assistant in a notebook cell

To use Databricks Assistant directly in a code cell, press Cmd + i on MacOS or Ctrl + i on Windows. A text box appears within the cell. You can type a question or comment in English and then press Enter (not Shift+Enter , as you would to run a cell) to have the assistant generate a response.

You can also type / to display a list of common commands, such as /explain to explain the code in a cell, /fix to propose a fix if the code has an error, and /doc to suggest documentation comments for your code.

When you use /fix or /doc , the assistant displays proposed changes in a diff window. Select Accept to accept the proposed changes or Reject to keep the original code. If you accept the proposed code, the code does not automatically run. You can review the code before running it. If the generated code is not what you wanted, try again by adding more details or information to your comment. See Tips for using Databricks Assistant .

For code autocomplete, performance may be better using the Assistant pane than in a notebook cell.

Use the Assistant pane

AI-based autocomplete

This feature is in Public Preview .

AI-based autocomplete provides inline code suggestions as you type in Databricks notebooks, the SQL editor, and the file editor. Inline code suggestions are available for Python and SQL.

Enable and disable inline code suggestions

In most workspaces, inline code suggestions are enabled by default. An admin can disable the feature as follows:

Click your username in the upper-right corner of the Databricks workspace, and select Previews from the drop-down menu.

Toggle Databricks Assistant Autocomplete .

The feature must be enabled for each user as follows:

Under Experimental features , toggle Databricks Assistant Autocomplete .

Inline code suggestions are not available in AWS GovCloud regions or workspaces with FedRAMP compliance.

Use inline code suggestions

As you type, suggestions automatically appear. Press Tab to accept a suggestion. To manually trigger a suggestion, press Option + Shift + Space (on macOS) or Control + Shift + Space (on Windows).

AI-based autocomplete can also generate code from comments:

To use Databricks Assistant to fix code, ask a question in the Assistant pane or click the Diagnose Error button that appears in the cell results when an error occurs. The tabs below show examples for Python and SQL code.

Explain code

Databricks Assistant can provide detailed explanations of code snippets. In the prompt you provide, you can include terms like “be concise” or “explain code line-by-line” to request the level of detail for the explanation it provides. You can also ask Databricks Assistant to add comments to code.

Find relevant information in the Databricks documentation

Databricks Assistant can help answer questions based on Databricks documentation directly from the notebook editor.

Tips for using Databricks Assistant

This section includes some general tips and best practices when using Databricks Assistant.

Databricks Assistant uses context to provide better answers

Databricks Assistant has accesss to table and column schema and metadata. This allows you to use natural language and generate more accurate queries. For example, if a table has a column State, you can ask Databricks Assistant to generate a list of users who live in Michigan.

Databricks Assistant uses the following context:

Code or queries in the current notebook cell or Databricks SQL editor tab.

Table and Column names and descriptions.

Previous prompt questions.

Favorite and active tables.

For the diagnose error feature, the stack trace from the error output.

When selecting columns from a DataFrame, you can get more accurate results by providing a starting query. For example, provide a statement like SELECT * FROM <table_name> . This allows Databricks Assistant to get the column names and not have to guess.

Because Databricks Assistant uses your conversation history to provide better and more accurate answers, you can ask Databricks Assistant to alter the output of a previous response without having to rewrite the entire prompt. You can use the Assistant’s chat history to iteratively clean, explore, filter, and slice DataFrames in the Assistant pane.

Be specific

The structure and detail that Databricks Assistant provides varies from time to time, even for the same prompt. Try to provide the assistant as much guidance as you can to help it return the information you want in the desired format, level of detail, and so on. For example:

“Explain this code in a couple sentences” or “Explain this code line-by-line”.

“Create a visualization using MatPlotLib” or “Create a visualization using Seaborn”.

Give examples of row-level data values

Because Databricks Assistant does not use row-level data, you might need to provide more detail to prompts to get the most accurate answer. Use table or column comments in Catalog Explorer to add a line of sample data. For example, suppose your height column is in the format feet - inches . To help the assistant interpret the data, add a comment such as “The height column is in string format and is separated by a hyphen. Example: ‘6-2’.” For information about table and column comments, see Document data in Catalog Explorer using markdown comments .

You might also need to provide details if column data type conversions are needed to perform an operation. For example: “convert this code from pandas to PySpark, including the code needed to convert the pandas DataFrame to a PySpark DataFrame and changing the data type of column churn from boolean to integer”.

Use Shift+Enter to add a new line in the chat text box

You can use Shift+Enter to add a new line in the Assistant chat text box. This makes it easy to format and organize your messages to Databricks Assistant.

Edit and run code in Databricks Assistant chat pane

When you run code in the Assistant pane, output is displayed and the variables become usable in the notebook.

You can also edit the code that Databricks Assistant generates directly in the Assistant chat box before moving the code to the notebook.

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take-home-assignment

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• Jupyter Notebook

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