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Databricks Certified Data Engineer Professional Sample Questions:

1. A new data engineer notices that a critical field was omitted from an application that writes its Kafka source to Delta Lake. This happened even though the critical field was in the Kafka source.
That field was further missing from data written to dependent, long-term storage. The retention threshold on the Kafka service is seven days. The pipeline has been in production for three months.
Which describes how Delta Lake can help to avoid data loss of this nature in the future?

A) Data can never be permanently dropped or deleted from Delta Lake, so data loss is not possible under any circumstance.
B) The Delta log and Structured Streaming checkpoints record the full history of the Kafka producer.
C) Delta Lake automatically checks that all fields present in the source data are included in the ingestion layer.
D) Ingestine all raw data and metadata from Kafka to a bronze Delta table creates a permanent, replayable history of the data state.
E) Delta Lake schema evolution can retroactively calculate the correct value for newly added fields, as long as the data was in the original source.

2. How are the operational aspects of Lakeflow Declarative Pipelines different from Spark Structured Streaming?

A) Structured Streaming can process continuous data streams, while Lakeflow Declarative Pipelines cannot.
B) Lakeflow Declarative Pipelines automatically handle schema evolution, while Structured Streaming always requires manual schema management.
C) Lakeflow Declarative Pipelines can write to Delta Lake format, while Structured Streaming cannot.
D) Lakeflow Declarative Pipelines manage the orchestration of multi-stage pipelines automatically, while Structured Streaming requires external orchestration for complex dependencies.

3. A data engineer is developing a Lakeflow Declarative Pipeline (LDP) using a Databricks notebook directly connected to their pipeline. After adding new table definitions and transformation logic in their notebook, they want to check for any syntax errors in the pipeline code without actually processing data or running the pipeline. How should the data engineer perform this syntax check?

A) Switch to a workspace file instead of a notebook to access validation and diagnostics tools.
B) Open the web terminal from the notebook and run a shell command to validate the pipeline code.
C) Disconnect the notebook from the pipeline and reconnect it to a compute cluster to access code validation features.
D) Use the "Validate" option in the notebook to check for syntax errors.

4. The business intelligence team has a dashboard configured to track various summary metrics for retail stories. This includes total sales for the previous day alongside totals and averages for a variety of time periods. The fields required to populate this dashboard have the following schema:

For Demand forecasting, the Lakehouse contains a validated table of all itemized sales updated incrementally in near real-time. This table named products_per_order, includes the following fields:

Because reporting on long-term sales trends is less volatile, analysts using the new dashboard only require data to be refreshed once daily. Because the dashboard will be queried interactively by many users throughout a normal business day, it should return results quickly and reduce total compute associated with each materialization.
Which solution meets the expectations of the end users while controlling and limiting possible costs?

A) Configure a webhook to execute an incremental read against products_per_order each time the dashboard is refreshed.
B) Populate the dashboard by configuring a nightly batch job to save the required values as a table overwritten with each update.
C) Define a view against the products_per_order table and define the dashboard against this view.
D) Use the Delta Cache to persists the products_per_order table in memory to quickly the dashboard with each query.
E) Use Structure Streaming to configure a live dashboard against the products_per_order table within a Databricks notebook.

5. The data engineering team has configured a Databricks SQL query and alert to monitor the values in a Delta Lake table. The recent_sensor_recordings table contains an identifying sensor_id alongside the timestamp and temperature for the most recent 5 minutes of recordings.
The below query is used to create the alert:

The query is set to refresh each minute and always completes in less than 10 seconds. The alert is set to trigger when mean (temperature) > 120. Notifications are triggered to be sent at most every 1 minute.
If this alert raises notifications for 3 consecutive minutes and then stops, which statement must be true?

A) The maximum temperature recording for at least one sensor exceeded 120 on three consecutive executions of the query
B) The total average temperature across all sensors exceeded 120 on three consecutive executions of the query
C) The source query failed to update properly for three consecutive minutes and then restarted
D) The average temperature recordings for at least one sensor exceeded 120 on three consecutive executions of the query
E) The recent_sensor_recordingstable was unresponsive for three consecutive runs of the query

Solutions: