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Databricks Certified Data Engineer Professional Sample Questions:

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

2. A user new to Databricks is trying to troubleshoot long execution times for some pipeline logic they are working on. Presently, the user is executing code cell-by-cell, using display() calls to confirm code is producing the logically correct results as new transformations are added to an operation. To get a measure of average time to execute, the user is running each cell multiple times interactively.
Which of the following adjustments will get a more accurate measure of how code is likely to perform in production?

A) Scala is the only language that can be accurately tested using interactive notebooks; because the best performance is achieved by using Scala code compiled to JARs. all PySpark and Spark SQL logic should be refactored.
B) The Jobs Ul should be leveraged to occasionally run the notebook as a job and track execution time during incremental code development because Photon can only be enabled on clusters launched for scheduled jobs.
C) Production code development should only be done using an IDE; executing code against a local build of open source Spark and Delta Lake will provide the most accurate benchmarks for how code will perform in production.
D) The only way to meaningfully troubleshoot code execution times in development notebooks Is to use production-sized data and production-sized clusters with Run All execution.
E) Calling display () forces a job to trigger, while many transformations will only add to the logical query plan; because of caching, repeated execution of the same logic does not provide meaningful results.

3. The data science team has created and logged a production model using MLflow. The model accepts a list of column names and returns a new column of type DOUBLE.
The following code correctly imports the production model, loads the customers table containing the customer_id key column into a DataFrame, and defines the feature columns needed for the model.

Which code block will output a DataFrame with the schema "customer_id LONG, predictions DOUBLE"?

A) df.map(lambda x:model(x[columns])).select("customer_id, predictions")
B) df.apply(model, columns).select("customer_id, predictions")
C) model.predict(df, columns)
D) df.select("customer_id", pandas_udf(model, columns).alias("predictions"))
E) df.select("customer_id", model(*columns).alias("predictions"))

4. The data science team has created and logged a production model using MLflow. The following code correctly imports and applies the production model to output the predictions as a new DataFrame named preds with the schema "customer_id LONG, predictions DOUBLE, date DATE".
Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from

The data science team would like predictions saved to a Delta Lake table with the ability to compare all predictions across time. Churn predictions will be made at most once per day.
Which code block accomplishes this task while minimizing potential compute costs?

A)

B)

C)

D) preds.write.mode("append").saveAsTable("churn_preds")
E) preds.write.format("delta").save("/preds/churn_preds")

5. The Databricks CLI is use to trigger a run of an existing job by passing the job_id parameter. The response that the job run request has been submitted successfully includes a filed run_id.
Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from Which statement describes what the number alongside this field represents?

A) The job_id is returned in this field.
B) The globally unique ID of the newly triggered run.
C) The job_id and number of times the job has been are concatenated and returned.
D) The total number of jobs that have been run in the workspace.
E) The number of times the job definition has been run in the workspace.

Solutions: