Databricks Databricks-Certified-Data-Engineer-Professional

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Databricks Certified Data Engineer Professional Sample Questions:

1. A data engineer wants to ingest a large collection of image files (JPEG and PNG) from cloud object storage into a Unity Catalog-managed table for analysis and visualization. Which two configurations and practices are recommended to incrementally ingest these images into the table? (Choose two.)

A) Move files to a volume and read with SQL editor.
B) Use Auto Loader and set cloudFiles.format to "IMAGE".
C) Use the pathGlobFilter option to select only image files (e.g., "*.jpg,*.png").
D) Use Auto Loader and set cloudFiles.format to "TEXT".
E) Use Auto Loader and set cloudFiles.format to "BINARYFILE".

2. An external object storage container has been mounted to the location /mnt/finance_eda_bucket.
The following logic was executed to create a database for the finance team:

After the database was successfully created and permissions configured, a member of the finance team runs the following code:

If all users on the finance team are members of the finance group, which statement describes how the tx_sales table will be created?

A) A managed table will be created in the DBFS root storage container.
B) An managed table will be created in the storage container mounted to /mnt/finance_eda_bucket.
C) An external table will be created in the storage container mounted to /mnt/finance eda bucket.
D) A logical table will persist the physical plan to the Hive Metastore in the Databricks control plane.
E) A logical table will persist the query plan to the Hive Metastore in the Databricks control plane.

3. 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) By default, both the DBFS root and mounted data sources are only accessible to workspace administrators.
B) The DBFS root is the most secure location to store data, because mounted storage volumes must have full public read and write permissions.
C) 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.
D) Neither the DBFS root nor mounted storage can be accessed when using %sh in a Databricks notebook.
E) 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.

4. A data architect has heard about lake's built-in versioning and time travel capabilities. For auditing purposes they have a requirement to maintain a full of all valid street addresses as they appear in the customers table.
The architect is interested in implementing a Type 1 table, overwriting existing records with new values and relying on Delta Lake time travel to support long-term auditing. A data engineer on the project feels that a Type 2 table will provide better performance and scalability. Which piece of information is critical to this decision?

A) Delta Lake time travel does not scale well in cost or latency to provide a long-term versioning solution.
B) Data corruption can occur if a query fails in a partially completed state because Type 2 tables requires setting multiple fields in a single update.
C) Delta Lake time travel cannot be used to query previous versions of these tables because Type 1 changes modify data files in place.
D) Shallow clones can be combined with Type 1 tables to accelerate historic queries for long-term versioning.
E) Delta Lake only supports Type 0 tables; once records are inserted to a Delta Lake table, they cannot be modified.

5. A data engineer is implementing a job to download multiple PDF files from a third-party provided REST API endpoint by specifying different report types. The REST API is time-consuming and encounters intermittent errors, so the engineer wants to track each download activity to know when it fails and to retry partially, while providing scalable throughput. The engineer needs to download ten report types, and the list can be changed over time. How should the data engineer achieve this?

A) Use a foreach task with a list of report types as its inputs.
B) Define a list variable within a Notebook to loop through the report types to download them, and print the download results. Execute it as a Notebook tasks.
C) Define ten Notebook tasks to clearly track which report download failed.
D) Use a Delta Lake table to track each report download status as 10 rows, and use it as a source table to execute the download function as a Pandas UDF.

Solutions: