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Databricks Certified Data Engineer Professional Sample Questions:

1. Each configuration below is identical to the extent that each cluster has 400 GB total of RAM 160 total cores and only one Executor per VM.
Given an extremely long-running job for which completion must be guaranteed, which cluster configuration will be able to guarantee completion of the job in light of one or more VM failures?

A) - Total VMs: 1
- 400 GB per Executor
- 160 Cores/Executor
B) - Total VMs: 16
- 25 GB per Executor
- 10 Cores / Executor
C) - Total VMs: 8
- 50 GB per Executor
- 20 Cores / Executor
D) - Total VMs: 4
- 100 GB per Executor
- 40 Cores / Executor
E) - Total VMs: 2
- 200 GB per Executor
- 80 Cores / Executor

2. In order to prevent accidental commits to production data, a senior data engineer has instituted a policy that all development work will reference clones of Delta Lake tables. After testing both deep and shallow clone, development tables are created using shallow clone. A few weeks after initial table creation, the cloned versions of several tables implemented as Type 1 Slowly Changing Dimension (SCD) stop working. The transaction logs for the source tables show that vacuum was run the day before.
Why are the cloned tables no longer working?

A) The data files compacted by vacuum are not tracked by the cloned metadata; running refresh on the cloned table will pull in recent changes.
B) The metadata created by the clone operation is referencing data files that were purged as invalid by the vacuum command
C) Because Type 1 changes overwrite existing records, Delta Lake cannot guarantee data consistency for cloned tables.
D) Tables created with SHALLOW CLONE are automatically deleted after their default retention threshold of 7 days.
E) Running vacuum automatically invalidates any shallow clones of a table; deep clone should always be used when a cloned table will be repeatedly queried.

3. A Databricks SQL dashboard has been configured to monitor the total number of records present in a collection of Delta Lake tables using the following query pattern:
SELECT COUNT (*) FROM table
Which of the following describes how results are generated each time the dashboard is updated?

A) The total count of records is calculated from the Delta transaction logs
B) The total count of records is calculated from the parquet file metadata
C) The total count of rows will be returned from cached results unless REFRESH is run
D) The total count of records is calculated from the Hive metastore
E) The total count of rows is calculated by scanning all data files

4. A data engineer needs to create an application that will collect information about the latest job run including the repair history. How should the data engineer format the request?

A) Call/api/2.1/jobs/runs/list with the job_id and include_history parameters
B) Call/api/2.1/jobs/runs/get with the run_id and include_history parameters
C) Call/api/2.1/jobs/runs/get with the job_id and include_history parameters
D) Call/api/2.1/jobs/runs/list with the run_id and include_history parameters

5. A company has a task management system that tracks the most recent status of tasks. The system takes task events as input and processes events in near real-time using Lakeflow Declarative Pipelines. A new task event is ingested into the system when a task is created or the task status is changed. Lakeflow Declarative Pipelines provides a streaming table (tasks_status) for BI users to query.
The table represents the latest status of all tasks and includes 5 columns:
task_id (unique for each task)
task_name
task_owner
task_status
task_event_time
The table enables three properties: deletion vectors, row tracking, and change data feed (CDF).
A data engineer is asked to create a new Lakeflow Declarative Pipeline to enrich the tasks_status table in near real-time by adding one additional column representing task_owner's department, which can be looked up from a static dimension table (employee).
How should this enrichment be implemented?

A) Create a new Lakeflow Declarative Pipeline: use the readStream() function to read tasks_status table; enrich with the employee table; store the result in a new streaming table.
B) Create a new Lakeflow Declarative Pipeline: use the read() function to read tasks_status table; enrich with employee table; store the result in a materialized view.
C) Create a new Lakeflow Declarative Pipeline: use readStream() function with option readChangeFeed to read tasks_status table CDF; enrich with the employee table; create a new streaming table as the result table and use apply_changes() function to process the changes from the enriched CDF.
D) Create a new Lakeflow Declarative Pipeline: use the readStream() function with the option skipChangeCommits to read the tasks_status table; enrich with the employee table; store the result in a new streaming table.

Solutions: