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Databricks Certified Data Engineer Professional Sample Questions:

1. A data engineer is implementing liquid clustering on a Delta Lale table and needs to understand how it affects data management operations. The table will be updated frequently with new data.
The table is an external table and not managed by Unity Catalog. How does liquid clustering in Delta Lake handle new data that is inserted after the initial table creation?

A) New data remains unclustered until the next OPTIMIZE operation.
B) New data is written to a staging area and clustered during scheduled maintenance.
C) New data is rejected if it doesn't match the clustering pattern.
D) New data is automatically clustered during write operations.

2. What statement is true regarding the retention of job run history?

A) It is retained for 60 days, after which logs are archived
B) It is retained for 90 days or until the run-id is re-used through custom run configuration
C) It is retained for 60 days, during which you can export notebook run results to HTML
D) It is retained until you export or delete job run logs
E) It is retained for 30 days, during which time you can deliver job run logs to DBFS or S3

3. A nightly batch job is configured to ingest all data files from a cloud object storage container where records are stored in a nested directory structure YYYY/MM/DD. The data for each date represents all records that were processed by the source system on that date, noting that some records may be delayed as they await moderator approval. Each entry represents a user review of a product and has the following schema:
user_id STRING, review_id BIGINT, product_id BIGINT, review_timestamp TIMESTAMP, review_text STRING The ingestion job is configured to append all data for the previous date to a target table reviews_raw with an identical schema to the source system. The next step in the pipeline is a batch write to propagate all new records inserted into reviews_raw to a table where data is fully deduplicated, validated, and enriched.
Which solution minimizes the compute costs to propagate this batch of data?

A) Reprocess all records in reviews_raw and overwrite the next table in the pipeline.
B) Perform a batch read on the reviews_raw table and perform an insert-only merge using the natural composite key user_id, review_id, product_id, review_timestamp.
C) Use Delta Lake version history to get the difference between the latest version of reviews_raw and one version prior, then write these records to the next table.
D) Configure a Structured Streaming read against the reviews_raw table using the trigger once execution mode to process new records as a batch job.
E) Filter all records in the reviews_raw table based on the review_timestamp; batch append those records produced in the last 48 hours.

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

5. A junior data engineer on your team has implemented the following code block.

The view new_events contains a batch of records with the same schema as the events Delta table. The event_id field serves as a unique key for this table.
When this query is executed, what will happen with new records that have the same event_id as an existing record?

A) They are deleted.
B) They are updated.
C) They are ignored.
D) They are merged.
E) They are inserted.

Solutions: