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Databricks Certified Data Engineer Professional Sample Questions:

1. A healthcare analytics team is implementing a dimensional model in Delta Lake for patient care analysis. They have a date dimension table and are evaluating design options to ensure it supports a wide range of time-based analyses. Which design approach for the date dimension will support efficient time-based querying and aggregation?

A) Store the date as string in ISO format (YYYY-MM-DD) for readability.
B) Create separate dimension tables for different calendar systems (fiscal, academic, etc.)
C) Store only the date value and calculate all time attributes in queries.
D) Pre-calculate attributes like fiscal_period, quarter, month_name, day_of_week, and holiday.

2. Each configuration below is identical to the extent that each cluster has 400 GB total of RAM, 160 total cores and only one Executor per VM.
Given a job with at least one wide transformation, which of the following cluster configurations will result in maximum performance?

A) Total VMs: 4
100 GB per Executor
40 Cores/Executor
B) Total VMs: 8
50 GB per Executor
20 Cores / Executor
C) Total VMs: 2
200 GB per Executor
80 Cores / Executor
D) Total VMs: 1
400 GB per Executor
160 Cores / Executor

3. A data pipeline uses Structured Streaming to ingest data from kafka to Delta Lake. Data is being stored in a bronze table, and includes the Kafka_generated timesamp, key, and value. Three months after the pipeline is deployed the data engineering team has noticed some latency issued during certain times of the day.
A senior data engineer updates the Delta Table's schema and ingestion logic to include the current timestamp (as recoded by Apache Spark) as well the Kafka topic and partition. The team plans to use the additional metadata fields to diagnose the transient processing delays.
Which limitation will the team face while diagnosing this problem?

A) Updating the table schema requires a default value provided for each file added.
B) Spark cannot capture the topic partition fields from the kafka source.
C) New fields cannot be added to a production Delta table.
D) Updating the table schema will invalidate the Delta transaction log metadata.
E) New fields will not be computed for historic records.

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

5. A team of data engineer are adding tables to a DLT pipeline that contain repetitive expectations for many of the same data quality checks.
One member of the team suggests reusing these data quality rules across all tables defined for this pipeline.
What approach would allow them to do this?

A) Use global Python variables to make expectations visible across DLT notebooks included in the same pipeline.
B) Add data quality constraints to tables in this pipeline using an external job with access to pipeline configuration files.
C) Maintain data quality rules in a Delta table outside of this pipeline's target schema, providing the schema name as a pipeline parameter.
D) Maintain data quality rules in a separate Databricks notebook that each DLT notebook of file.

Solutions: