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

Databricks Certified Data Engineer Professional Sample Questions:

1. Which of the following is true of Delta Lake and the Lakehouse?

A) Views in the Lakehouse maintain a valid cache of the most recent versions of source tables at all times.
B) Z-order can only be applied to numeric values stored in Delta Lake tables
C) Because Parquet compresses data row by row. strings will only be compressed when a character is repeated multiple times.
D) Primary and foreign key constraints can be leveraged to ensure duplicate values are never entered into a dimension table.
E) Delta Lake automatically collects statistics on the first 32 columns of each table which are leveraged in data skipping based on query filters.

2. To reduce storage and compute costs, the data engineering team has been tasked with curating a series of aggregate tables leveraged by business intelligence dashboards, customer-facing applications, production machine learning models, and ad hoc analytical queries.
The data engineering team has been made aware of new requirements from a customer-facing application, which is the only downstream workload they manage entirely. As a result, an aggregate table used by numerous teams across the organization will need to have a number of fields renamed, and additional fields will also be added.
Which of the solutions addresses the situation while minimally interrupting other teams in the organization without increasing the number of tables that need to be managed?

A) Send all users notice that the schema for the table will be changing; include in the communication the logic necessary to revert the new table schema to match historic queries.
B) Configure a new table with all the requisite fields and new names and use this as the source for the customer-facing application; create a view that maintains the original data schema and table name by aliasing select fields from the new table.
C) Replace the current table definition with a logical view defined with the query logic currently writing the aggregate table; create a new table to power the customer-facing application.
D) Add a table comment warning all users that the table schema and field names will be changing on a given date; overwrite the table in place to the specifications of the customer-facing application.
E) Create a new table with the required schema and new fields and use Delta Lake's deep clone functionality to sync up changes committed to one table to the corresponding table.

3. A distributed team of data analysts share computing resources on an interactive cluster with autoscaling configured. In order to better manage costs and query throughput, the workspace administrator is hoping to evaluate whether cluster upscaling is caused by many concurrent users or resource-intensive queries.
In which location can one review the timeline for cluster resizing events?

A) Workspace audit logs
B) Cluster Event Log
C) Driver's log file
D) Ganglia
E) Executor's log file

4. Where in the Spark UI can one diagnose a performance problem induced by not leveraging predicate push-down?

A) In the Query Detail screen, by interpreting the Physical Plan
B) In the Delta Lake transaction log. by noting the column statistics
C) In the Stage's Detail screen, in the Completed Stages table, by noting the size of data read from the Input column
D) In the Storage Detail screen, by noting which RDDs are not stored on disk
E) In the Executor's log file, by gripping for "predicate push-down"

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

Solutions: