Try Databricks : Certified-Data-Engineer-Professional valid & accurate questions and answers

The security team is exploring whether or not the Databricks secrets module can be leveraged for connecting to an external database.
After testing the code with all Python variables being defined with strings, they upload the password to the secrets module and configure the correct permissions for the currently active user. They then modify their code to the following (leaving all other variables unchanged).

Which statement describes what will happen when the above code is executed?

• A. An interactive input box will appear in the notebook; if the right password is provided, the connection will succeed and the encoded password will be saved to DBFS.
• B. The connection to the external table will succeed; the string value of password will be printed in plain text.
• C. The connection to the external table will succeed; the string "redacted" will be printed.
• D. An interactive input box will appear in the notebook; if the right password is provided, the connection will succeed and the password will be printed in plain text.
• E. The connection to the external table will fail; the string "redacted" will be printed.

A junior developer complains that the code in their notebook isn't producing the correct results in the development environment. A shared screenshot reveals that while they're using a notebook versioned with Databricks Repos, they're using a personal branch that contains old logic. The desired branch named dev-2.3.9 is not available from the branch selection dropdown.
Which approach will allow this developer to review the current logic for this notebook?

• A. Use Repos to pull changes from the remote Git repository and select the dev-2.3.9 branch.
• B. Use Repos to make a pull request use the Databricks REST API to update the current branch to dev-2.3.9
• C. Use Repos to checkout the dev-2.3.9 branch and auto-resolve conflicts with the current branch
• D. Merge all changes back to the main branch in the remote Git repository and clone the repo again
• E. Use Repos to merge the current branch and the dev-2.3.9 branch, then make a pull request to sync with the remote repository

A data engineer is developing a Lakeflow Declarative Pipeline (LDP) using a Databricks notebook directly connected to their pipeline. After adding new table definitions and transformation logic in their notebook, they want to check for any syntax errors in the pipeline code without actually processing data or running the pipeline. How should the data engineer perform this syntax check?

• A. Disconnect the notebook from the pipeline and reconnect it to a compute cluster to access code validation features.
• B. Use the "Validate" option in the notebook to check for syntax errors.
• C. Open the web terminal from the notebook and run a shell command to validate the pipeline code.
• D. Switch to a workspace file instead of a notebook to access validation and diagnostics tools.

A data engineer is analyzing a large, partitioned retail dataset in Databricks, where each row represents a sale made by a salesperson. The dataset contains millions of records with the following schema:
sales_df: [salesperson_id: string, region: string, sale_amount: double, sale_date: date] The data engineer needs to generate a DataFrame that ranks salespeople within each region based on their total cumulative sales, with the highest seller ranked as 1. If multiple salespeople have the same total sales, they should share the same rank.
The data engineer wants to implement this logic using a PySpark window function and the dense_rank () function.
Which code snippet will perform this ranking?

• A.
  
• B.
  
• C.
  
• D.
  

A faulty IoT sensor in a factory reports a temperature of -500, causing the LDP pipeline to fail the expectation, which only allows values between -100 and 200 degrees Celsius. The data engineer would like to further analyze the faulty data to better understand the reason behind this. How should the data engineer resolve the faulty data while ensuring data quality standards are maintained?

• A. Ignore the error and simply re-run the pipeline, as Databricks will automatically skip the problematic record on the next run.
• B. Remove all expectations form the pipeline to prevent any future failures, regardless of data quality.
• C. Fix the pipeline code and implement a quarantine logic to isolate the faulty data before re-running the pipeline.
• D. Change the expectation action from fail to warn so that invalid records are included in the output and the pipeline does not fail.