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Databricks Databricks-Certified-Data-Engineer-Professional Exam Syllabus Topics:

Databricks Certified Data Engineer Professional Sample Questions:

1. A company wants to implement Lakehouse Federation across multiple data sources but is concerned about data consistency and ensuring that all teams access the same authoritative version of their data. Which statement is applicable for Lakehouse Federations to maintain data consistency?

A) Federation creates local copies that must be manually refreshed.
B) A separate data synchronization service must be deployed.
C) Federation provides read-only access that reflects the current state of source systems.
D) Federation implements change data capture (CDC) from all sources.

2. Two of the most common data locations on Databricks are the DBFS root storage and external object storage mounted with dbutils.fs.mount().
Which of the following statements is correct?

A) The DBFS root is the most secure location to store data, because mounted storage volumes must have full public read and write permissions.
B) The DBFS root stores files in ephemeral block volumes attached to the driver, while mounted directories will always persist saved data to external storage between sessions.
C) Neither the DBFS root nor mounted storage can be accessed when using %sh in a Databricks notebook.
D) DBFS is a file system protocol that allows users to interact with files stored in object storage using syntax and guarantees similar to Unix file systems.
E) By default, both the DBFS root and mounted data sources are only accessible to workspace administrators.

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

4. The business reporting tem requires that data for their dashboards be updated every hour. The total processing time for the pipeline that extracts transforms and load the data for their pipeline runs in 10 minutes.
Assuming normal operating conditions, which configuration will meet their service-level agreement requirements with the lowest cost?

A) Schedule a job to execute the pipeline once an hour on a dedicated interactive cluster.
B) Schedule a Structured Streaming job with a trigger interval of 60 minutes.
C) Configure a job that executes every time new data lands in a given directory.
D) Schedule a job to execute the pipeline once an hour on a new job cluster.

5. A data engineer and a platform engineer are working together to automate their system tasks. A script needs to be executed outside of Databricks only if a particular daily Databricks job finishes successfully for the day. Databricks CLI command was used to check the last execution of the job. What are the required command options for that task?

A) databricks jobs list-runs --job-id JOB_ID --start-time-to TODAY_MIDNIGHT_EPOCH_MS --active- only
B) databricks jobs list-runs --job-id JOB_ID --start-time-to TODAY_MIDNIGHT_EPOCH_MS -- completed-only
C) databricks jobs list-runs --job-id JOB_ID --start-time-from TODAY_MIDNIGHT_EPOCH_MS -- completed-only
D) databricks jobs list-runs --job-id JOB_ID --start-time-from TODAY_MIDNIGHT_EPOCH_MS -- active-only

Solutions: