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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 manually configured a series of jobs using the Databricks Jobs UI.
Upon reviewing their work, the engineer realizes that they are listed as the "Owner" for each job.
They attempt to transfer "Owner" privileges to the "DevOps" group, but cannot successfully accomplish this task.
Which statement explains what is preventing this privilege transfer?

A) Other than the default "admins" group, only individual users can be granted privileges on jobs.
B) Databricks jobs must have exactly one owner; "Owner" privileges cannot be assigned to a group.
C) The creator of a Databricks job will always have "Owner" privileges; this configuration cannot be changed.
D) Only workspace administrators can grant "Owner" privileges to a group.
E) A user can only transfer job ownership to a group if they are also a member of that group.

2. An upstream source writes Parquet data as hourly batches to directories named with the current date. A nightly batch job runs the following code to ingest all data from the previous day as indicated by the date variable:

Assume that the fields customer_id and order_id serve as a composite key to uniquely identify each order.
If the upstream system is known to occasionally produce duplicate entries for a single order hours apart, which statement is correct?

A) Each write to the orders table will only contain unique records; if existing records with the same key are present in the target table, these records will be overwritten.
B) Each write to the orders table will only contain unique records; if existing records with the same key are present in the target table, the operation will tail.
C) Each write to the orders table will run deduplication over the union of new and existing records, ensuring no duplicate records are present.
D) Each write to the orders table will only contain unique records, and only those records without duplicates in the target table will be written.
E) Each write to the orders table will only contain unique records, but newly written records may have duplicates already present in the target table.

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 rows is calculated by scanning all data files
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 records is calculated from the parquet file metadata

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

5. A data engineer is configuring a Databricks Asset Bundle to deploy a job with granular permissions.
The requirements are:
- Grant the data-engineers group CAN_MANAGE access to the job.
- Ensure the auditors' group can view the job but not modify/run it.
- Avoid granting unintended permissions to other users/groups.
How should the data engineer deploy the job while meeting the requirements?

A) resources:
jobs:
my-job:
name: data-pipeline
tasks: [...]
job_clusters: [...]
permissions:
- group_name: data-engineers
level: CAN_MANAGE
- group_name: auditors
level: CAN_VIEW
B) resources:
jobs:
my-job:
name: data-pipeline
tasks: [...]
job: [...]
permissions:
- group_name: data-engineers
level: CAN_MANAGE
permissions:
- group_name: auditors
level: CAN_VIEW
C) permissions:
- group_name: data-engineers
level: CAN_MANAGE
- group_name: auditors
level: CAN_VIEW
resources:
jobs:
my-job:
name: data-pipeline
tasks: [...]
job_clusters: [...]
D) resources:
jobs:
my-job:
name: data-pipeline
tasks: [...]
job_clusters: [...]
permissions:
- group_name: data-engineers
level: CAN_MANAGE
- group_name: auditors
level: CAN_VIEW
- group_name: admin-team
level: IS_OWNER

Solutions: