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

Databricks Certified Data Engineer Professional Sample Questions:

1. Two data engineers are working on the same Databricks notebook in separate branches. Both have edited the same section of code. When one tries to merge the other's branch into their own using the Databricks Git folders UI, a merge conflict occurs on that notebook file. The UI highlights the conflict and presents options for resolution. How should the data engineers resolve this merge conflict using Databricks Git folders?

A) Abort the merge, discard all local changes, and try the merge operation again without reviewing the conflicting code.
B) Use the Git CLI in the cluster's web terminal to force-push the conflicted merge (git push -force), overriding the remote branch with the local version and discarding changes.
C) Use the Git folders UI to manually edit the notebook file, selecting the desired lines from both versions and removing the conflict markers, then mark the conflict as resolved.
D) Delete the conflicted notebook file via the Databricks workspace UI, commit the deletion, and recreate the notebook from scratch in a new commit to bypass the conflict entirely.

2. The view updates represents an incremental batch of all newly ingested data to be inserted or updated in the customers table.
The following logic is used to process these records.

Which statement describes this implementation?

A) The customers table is implemented as a Type 2 table; old values are overwritten and new customers are appended.
B) The customers table is implemented as a Type 0 table; all writes are append only with no changes to existing values.
C) The customers table is implemented as a Type 2 table; old values are maintained but marked as no longer current and new values are inserted.
D) The customers table is implemented as a Type 3 table; old values are maintained as a new column alongside the current value.
E) The customers table is implemented as a Type 1 table; old values are overwritten by new values and no history is maintained.

3. A data engineer is using Auto Loader to read incoming JSON data as it arrives. They have configured Auto Loader to quarantine invalid JSON records but notice that over time, some records are being quarantined even though they are well-formed JSON.
The code snippet is:
df = (spark.readStream
.format("cloudFiles")
.option("cloudFiles.format", "json")
.option("badRecordsPath", "/tmp/somewhere/badRecordsPath")
.schema("a int, b int")
.load("/Volumes/catalog/schema/raw_data/"))
What is the cause of the missing data?

A) At some point, the upstream data provider switched everything to multi-line JSON.
B) The source data is valid JSON but does not conform to the defined schema in some way.
C) The engineer forgot to set the option "cloudFiles.quarantineMode" = "rescue".
D) The badRecordsPath location is accumulating many small files.

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

5. The DevOps team has configured a production workload as a collection of notebooks scheduled to run daily using the Jobs UI. A new data engineering hire is onboarding to the team and has requested access to one of these notebooks to review the production logic.
What are the maximum notebook permissions that can be granted to the user without allowing accidental changes to production code or data?

A) Can Edit
B) Can Run
C) Can Read
D) Can Manage
E) No permissions

Solutions: