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Databricks Certified Data Engineer Associate - 340 Questions

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Practice exam for the Databricks Certified Data Engineer Associate certification. Covers the Databricks Lakehouse Platform, ELT with Spark SQL and Python, incremental data processing with Delta Live Tables, production pipelines with Workflows, and data governance with Unity Catalog.

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Sample Questions — Databricks Certified Data Engineer Associate - 340 Questions

5 free sample questions from this practice exam. Correct answers are highlighted.

1. A data engineer is explaining the Lakehouse architecture to a new team member. Which statement best describes the core value proposition of a Lakehouse compared to using a separate data lake and data warehouse?

A It replaces the need for structured data by storing only unstructured files on cloud object storage.
B It combines the scalability and low cost of a data lake with the reliability and performance features of a data warehouse on a single platform. ✓ Correct
C It uses proprietary binary formats to achieve better compression than a standard data lake or data warehouse.
D It improves BI query performance by removing the data warehouse layer and querying raw lake files directly.

2. A data engineering team stores customer transaction data in a Delta table registered in Unity Catalog. When a team member runs DROP TABLE on this managed table, what happens to the underlying data files?

A Both the metadata registration and the underlying data files are permanently deleted. ✓ Correct
B Only the metadata is removed; the underlying data files are retained in cloud storage.
C The data files are moved to a system-managed trash folder and can be recovered within 30 days.
D The DROP command fails unless the PURGE option is specified along with it.

3. A company stores its data in Amazon S3 using plain Parquet files. A data engineer proposes migrating to Delta Lake. Which feature of Delta Lake would most directly address the concern about partial write failures corrupting the dataset?

A ACID transactions, which ensure that write operations are atomic and prevent partial failures from corrupting the dataset. ✓ Correct
B Schema enforcement, which rejects writes that do not match the table schema.
C Time travel, which allows the data engineer to restore a previous version of the table after corruption.
D Auto-optimization, which compacts small files to ensure data consistency.

4. A company uses external Delta tables that point to data stored in their own Azure Data Lake Storage Gen2 account. A data engineer runs DROP TABLE on one of these external tables. What is the expected outcome?

A Both the table definition and the data files in ADLS Gen2 are permanently deleted.
B Only the table definition is removed from the metastore; the data files in ADLS Gen2 are preserved. ✓ Correct
C The DROP command fails because external tables require CASCADE to drop.
D The external table is converted to a managed table before being dropped.

5. A data engineering team wants to use the same data for both machine learning experiments and SQL-based business intelligence dashboards without duplicating data. Which characteristic of the Databricks Lakehouse Platform makes this possible?

A Each workload type requires data to be loaded into a dedicated in-memory store optimized for that use case.
B Delta Lake uses a proprietary format that is natively understood by both ML frameworks and SQL engines within Databricks.
C The Lakehouse stores data in open formats on cloud object storage, enabling multiple compute engines to access the same data without duplication. ✓ Correct
D Databricks automatically replicates data into separate optimized stores for SQL and ML workloads.

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