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

Databricks Certified Data Engineer Professional Sample Questions:

1. Why are Pandas UDFs often preferred over traditional PySpark UDFs in performance-critical applications involving large datasets?

A) They leverage Apache Arrow to enable vectorized operations between the JVM and Python runtimes, reducing serialization costs and improving computational efficiency.
B) They minimize memory usage by streaming each row individually through a lightweight Python wrapper, avoiding batch processing overhead.
C) They eliminate the JVM-Python boundary by bypassing serialization entirely, thereby avoiding data conversion overhead.
D) They allow row-level execution of functions in Python with native Spark optimization, removing the need for columnar execution.

2. The view updates represents an incremental batch of all newly ingested data to be inserted or updated in the customers table.
The following logic is used to process these records.

Which statement describes this implementation?

A) The customers table is implemented as a Type 2 table; old values are overwritten and new customers are appended.
B) The customers table is implemented as a Type 2 table; old values are maintained but marked as no longer current and new values are inserted.
C) The customers table is implemented as a Type 0 table; all writes are append only with no changes to existing values.
D) The customers table is implemented as a Type 3 table; old values are maintained as a new column alongside the current value.
E) The customers table is implemented as a Type 1 table; old values are overwritten by new values and no history is maintained.

3. A senior data engineer is planning large-scale data workflows. The current task is to identify the considerations that form a foundation for creating scalable data models that are essential for effective management of large datasets. The data engineering team has identified the core capabilities as part of a scalable data model to build a modern data platform and provided their reasoning for considering Delta Lake for review. The senior data engineer is responsible for identifying the recommendations that are not valid. Which key features can be ignored while evaluating Delta Lake?

A) Delta Lake works with various data formats (e.g., Parquet, JSON, CSV) and integrates well with Spark and Databricks tools.
B) Delta Lake's capability to process data in both batch and streaming modes seamlessly, providing flexibility in data ingestion and processing.
C) Delta Lake optimizes metadata handling, efficiently managing billions of files and facilitating scalability to petabyte-scale datasets.
D) Delta Lake provides limited support for monitoring and troubleshooting data pipelines, so relevant partner tools have to be identified and set up for enhanced operational efficiency.

4. A data architect is designing a Databricks solution to efficiently process data for different business requirements. In which scenario should a data engineer use a materialized view compared to a streaming table?

A) Processing high-volume, continuous clickstream data from a website to monitor user behavior in real-time.
B) Implementing a CDC (Change Data Capture) pipeline that needs to detect and respond to database changes within seconds.
C) Ingesting data from Apache Kafka topics with sub-second processing requirements for immediate alerting.
D) Precomputing complex aggregations and joins from multiple large tables to accelerate BI dashboard performance.

5. A data engineer is building a Lakeflow Declarative Pipelines pipeline to process healthcare claims data. A metadata JSON file defines data quality rules for multiple tables, including:
{
"claims": [
{"name": "valid_patient_id", "constraint": "patient_id IS NOT NULL"},
{"name": "non_negative_amount", "constraint": "claim_amount >= 0"}
]
}
The pipeline must dynamically apply these rules to the claims table without hardcoding the rules.
How should the data engineer achieve this?

A) Use a SQL CONSTRAINT block referencing the JSON file path.
B) Invoke an external API to validate records against the metadata rules.
C) Reference each expectation with @dlt.expect decorators in the table declaration.
D) Load the JSON metadata, loop through its entries, and apply expectations using dlt.expect_all.

Solutions: