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

Databricks Certified Data Engineer Professional Sample Questions:

1. A table is registered with the following code:

Both users and orders are Delta Lake tables. Which statement describes the results of querying recent_orders?

A) All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query began.
B) Results will be computed and cached when the table is defined; these cached results will incrementally update as new records are inserted into source tables.
C) The versions of each source table will be stored in the table transaction log; query results will be saved to DBFS with each query.
D) All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query finishes.
E) All logic will execute when the table is defined and store the result of joining tables to the DBFS; this stored data will be returned when the table is queried.

2. When a new Databricks project starts, the central IP team provisions the required infrastructure using Terraform and a Service Principal. This includes creating a Databricks workspace, a Unity Catalog linked to an External Location, and a Databricks group containing all project team members. Project teams must store all assets - e.g., tables and volumes, as Managed assets in Unity Catalog. This model hides infrastructure complexity while giving teams autonomy within their catalog. They can create and manage schemas, tables, volumes, and related objects but cannot rename, delete, or change catalog permissions, those remain under IT's control. Which rights should the project group be granted to enable this model?

A) The group needs to have USE CATALOG and USE SCHEMA on the catalog.
B) The group should be made OWNER of the catalog.
C) The group needs to have ALL PRIVILEGES on the catalog.
D) The group needs to have ALL PRIVILEGES and the MANAGE on the catalog.

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) 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.
B) 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.
C) Configure a Structured Streaming read against the reviews_raw table using the trigger once execution mode to process new records as a batch job.
D) Reprocess all records in reviews_raw and overwrite the next table in the pipeline.
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. The data engineer is using Spark's MEMORY_ONLY storage level. Which indicators should the data engineer look for in the spark UI's Storage tab to signal that a cached table is not performing optimally?

A) On Heap Memory Usage is within 75% of off Heap Memory usage
B) The number of Cached Partitions> the number of Spark Partitions
C) Size on Disk is> 0
D) The RDD Block Name included the '' annotation signaling failure to cache
E) Size on Disk is < Size in Memory

5. A data engineer is analyzing transactional data in a PySpark DataFrame df containing customer_id, transaction_timestamp (precise to milliseconds), and amount_spent. The objective is to compute a cumulative sum of amount_spent per customer, strictly ordered by transaction_timestamp. The cumulative sum must include all transactions from the earliest timestamp up to and including the current row, respecting temporal ordering within each customer partition. Which PySpark code snippet most accurately constructs the appropriate window specification and applies the aggregation to yield the correct cumulative expenditure per customer?

A)

B)

C)

D)

Solutions: