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Databricks Certified Data Engineer Professional Sample Questions:

1. A data engineer wants to join a stream of advertisement impressions (when an ad was shown) with another stream of user clicks on advertisements to correlate when impressions led to monetizable clicks.
In the code below, Impressions is a streaming DataFrame with a watermark ("event_time", "10 minutes")

The data engineer notices the query slowing down significantly.
Which solution would improve the performance?

A) Joining on event time constraint: clickTime == impressionTime using a leftOuter join
B) Joining on event time constraint: clickTime + 3 hours < impressionTime - 2 hours
C) Joining on event time constraint: clickTime >= impressionTime - interval 3 hours and removing watermarks
D) Joining on event time constraint: clickTime >= impressionTime AND clickTime <= impressionTime interval 1 hour

2. A data team is automating a daily multi-task ETL pipeline in Databricks. The pipeline includes a notebook for ingesting raw data, a Python wheel task for data transformation, and a SQL query to update aggregates. They want to trigger the pipeline programmatically and see previous runs in the GUI. They need to ensure tasks are retried on failure and stakeholders are notified by email if any task fails. Which two approaches will meet these requirements? (Choose two.)

A) Use Databricks Asset Bundles (DABs) to deploy the workflow, then trigger individual tasks directly by referencing each task's notebook or script path in the workspace.
B) Use the REST API endpoint /jobs/runs/submit to trigger each task individually as separate job runs and implement retries using custom logic in the orchestrator.
C) Create a single orchestrator notebook that calls each step with dbutils.notebook.run(), defining a job for that notebook and configuring retries and notifications at the notebook level.
D) Trigger the job programmatically using the Databricks Jobs REST API (/jobs/run-now), the CLI (databricks jobs run-now), or one of the Databricks SDKs.
E) Create a multi-task job using the UI, Databricks Asset Bundles (DABs), or the Jobs REST API (/jobs/create) with notebook, Python wheel, and SQL tasks. Configure task-level retries and email notifications in the job definition.

3. A data organization has adopted Delta Sharing to securely distribute curated datasets from a Unity Catalog-enabled workspace. The data engineering team shares large Delta tables internally via Databricks-to-Databricks and externally via Open Sharing for aggregated reports. While testing, they encounter challenges related to access control, data update visibility, and shareable object types. What is a limitation of the Delta Sharing protocol or implementation when used with Databricks-to-Databricks or Open Sharing?

A) With Databricks-to-Databricks sharing, Unity Catalog recipients must re-ingest data manually using COPY INTO or REST APIs.
B) Delta Sharing (both Databricks-to-Databricks and Open Sharing) allows recipients to modify the source data if they have select privileges.
C) Delta Sharing does not support Unity Catalog-enabled tables; only legacy Hive Metastore tables are shareable.
D) With Open Sharing, recipients cannot access Volumes, Models, or notebooks -- only static Delta tables are supported.

4. 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) Configure a Structured Streaming read against the reviews_raw table using the trigger once execution mode to process new records as a batch job.
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) 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.

5. What describes a primary technical challenge in ensuring consistent PII masking across all nodes in large-scale, distributed Databricks batch and streaming pipelines?

A) Native masking in Databricks automatically synchronizes with all downstream external Databricks systems.
B) Masking functions must be standardized and managed through Unity Catalog, with enforcement applied across all relevant datasets to avoid any data inconsistency.
C) PII masking is only required for direct identifiers.
D) Dynamic data masking is applied only at rest, so it does not affect query performance.

Solutions: