Databricks Certified-Data-Engineer-Professional : Databricks Certified Data Engineer Professional

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

Databricks Certified Data Engineer Professional Sample Questions:

1. A junior data engineer has configured a workload that posts the following JSON to the Databricks REST API endpoint 2.0/jobs/create.

Assuming that all configurations and referenced resources are available, which statement describes the result of executing this workload three times?

A) Three new jobs named "Ingest new data" will be defined in the workspace, but no jobs will be executed.
B) The logic defined in the referenced notebook will be executed three times on new clusters with the configurations of the provided cluster ID.
C) The logic defined in the referenced notebook will be executed three times on the referenced existing all purpose cluster.
D) Three new jobs named "Ingest new data" will be defined in the workspace, and they will each run once daily.
E) One new job named "Ingest new data" will be defined in the workspace, but it will not be executed.

2. A transactions table has been liquid clustered on the columns product_id, user_id, and event_date. Which operation lacks support for cluster on write?

A) spark.write.format('delta').mode('append')
B) spark.writestream.format('delta').mode('append')
C) CTAS and RTAS statements
D) INSERT INTO operations

3. A data engineer manages a Unity Catalog table customer_data in schema finance that includes sensitive fields like ssn and credit_score. Intern Group should only see masked values, while Analyst Group should only access rows for their assigned region. The data engineer needs to restrict access based on user role and region without duplicating data. How should the data engineer enforce this security policy?

A) Create dynamic views for each user role and manage access with ACLs.
B) Use Unity Catalog's row filters based on the user roles and column masks based on the region.
C) Use Unity Catalog's row filters based on the region and column masks based on user roles.
D) Create views using current_user() and is_account_group_member() functions, and apply masking logic inside the SQL SELECT clause for each sensitive column.

4. A data engineer needs to productionize a new Spark application written by teammate. This application has numerous external dependencies, including libraries, and requires custom environment variables and Spark configuration parameters to be set. Which two methods will help the data engineer accomplish the task? (Choose two.)

A) Install libraries on DBFS
B) Add libraries to compute policies
C) Use secrets in init scripts to store configuration data
D) Create init scripts on DBFS.
E) Use compute policies to set system properties, environment variables, and Spark configuration parameters.

5. A junior data engineer is working to implement logic for a Lakehouse table named silver_device_recordings. The source data contains 100 unique fields in a highly nested JSON structure.
The silver_device_recordings table will be used downstream for highly selective joins on a number of fields, and will also be leveraged by the machine learning team to filter on a handful of relevant fields, in total, 15 fields have been identified that will often be used for filter and join logic.
The data engineer is trying to determine the best approach for dealing with these nested fields before declaring the table schema.
Which of the following accurately presents information about Delta Lake and Databricks that may Impact their decision-making process?

A) By default Delta Lake collects statistics on the first 32 columns in a table; these statistics are leveraged for data skipping when executing selective queries.
B) Schema inference and evolution on Databricks ensure that inferred types will always accurately match the data types used by downstream systems.
C) Because Delta Lake uses Parquet for data storage, Dremel encoding information for nesting can be directly referenced by the Delta transaction log.
D) Tungsten encoding used by Databricks is optimized for storing string data: newly-added native support for querying JSON strings means that string types are always most efficient.

Solutions: