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

Section	Objectives
Topic 1: Data Ingestion and ELT Development	- Handling structured and semi-structured data - Data ingestion using Spark SQL and PySpark - ETL patterns and transformations
Topic 2: Productionizing Data Pipelines	- Pipeline deployment and operationalization - Scheduling and monitoring jobs - Databricks Workflows / Jobs orchestration
Topic 3: Data Governance and Quality	- Data access control and governance - Data quality concepts and management - Unity Catalog basics
Topic 4: Data Processing and Transformations	- Delta Lake fundamentals (tables, transactions, optimization) - Apache Spark SQL operations (joins, aggregations, filtering) - PySpark DataFrame transformations - User-defined functions (UDFs)
Topic 5: Databricks Lakehouse Platform Fundamentals	- Clusters, notebooks, and basic Databricks environment usage - Workspace, architecture, and core platform concepts

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q242-Q247):

NEW QUESTION # 242
Which of the following commands can be used to write data into a Delta table while avoiding the writing of duplicate records?

A. DROP
B. MERGE
C. APPEND
D. IGNORE
E. INSERT

Answer: B

Explanation:
The MERGE command can be used to upsert data from a source table, view, or DataFrame into a target Delta table. It allows you to specify conditions for matching and updating existing records, and inserting new records when no match is found. This way, you can avoid writing duplicate records into a Delta table1. The other commands (DROP, IGNORE, APPEND, INSERT) do not have this functionality and may result in duplicate records or data loss234. Reference: 1: Upsert into a Delta Lake table using merge | Databricks on AWS 2: SQL DELETE | Databricks on AWS 3: SQL INSERT INTO | Databricks on AWS 4: SQL UPDATE | Databricks on AWS

NEW QUESTION # 243
A data engineer needs to create a table in Databricks using data from their organization's existing SQLite database.
They run the following command:

Which of the following lines of code fills in the above blank to successfully complete the task?

A. org.apache.spark.sql.jdbc
B. sqlite
C. org.apache.spark.sql.sqlite
D. autoloader
E. DELTA

Answer: C

NEW QUESTION # 244
A data engineer is designing a Bronze-to-Silver pipeline on the Databricks Data Intelligence Platform. The source system sends daily CSV files, and new optional columns are added over time.
The engineer needs a storage format and table capabilities that provide all of the following:
Writes that do not conform to the defined schema are rejected.
The schema can evolve to include new optional columns without manually recreating the table.
Previous table versions can be queried for debugging and auditing.
Which solution fulfills these requirements?

A. Use a Delta table with its native schema enforcement, schema evolution, and table-history capabilities.
B. Use an external table with Auto Loader schema inference for the CSV files.
C. Use a Delta table with schema enforcement and recreate the table whenever new columns are added.
D. Use a Parquet table with Spark's default schema inference and rerun the job whenever the schema changes.

Answer: A

NEW QUESTION # 245
In a Lakeflow Declarative Pipeline (Delta Live Tables), a data engineer must ensure that records with a null order_id are discarded from the silver table while the pipeline continues to run.
Which expectation clause should be used?

A. CONSTRAINT valid_id EXPECT (order_id IS NOT NULL) ON VIOLATION FAIL UPDATE
B. CONSTRAINT valid_id EXPECT (order_id IS NOT NULL) ON VIOLATION DROP ROW
C. ALTER TABLE silver_orders ADD CONSTRAINT valid_id CHECK (order_id IS NOT NULL)
D. CONSTRAINT valid_id EXPECT (order_id IS NOT NULL)

Answer: B

Explanation:
ON VIOLATION DROP ROW removes failing records from the target dataset and records the violation in the event log while the pipeline continues. An expectation with no ON VIOLATION clause retains bad records and only reports metrics, and FAIL UPDATE stops the pipeline.

NEW QUESTION # 246
A data engineer is decommissioning a sandbox schema in Unity Catalog. Some tables are ephemeral staging outputs that can be safety removed entirely, but a few tables point at shared cloud storage used by downstream jobs outside Databricks. The engineer must avoid deleting any shared files when cleaning up catalog objects.
How does Unity Catalog behave when dropping Managed vs External tables?

A. DROP managed tables that are ephemeral and DROP external tables; files for both remain for 7 days.
B. DROP external tables first to delete their files, then DROP managed tables to keep their files for recovery.
C. DROP all tables; Databricks will only remove metadata for both managed and external tables.
D. DROP managed staging tables to remove data and metadata, and DROP external tables to remove only metadata

Answer: D

Explanation:
In Unity Catalog, dropping a managed table deletes both the table metadata and the underlying data files, while dropping an external table removes only the catalog metadata and leaves the referenced cloud storage data intact.

NEW QUESTION # 247
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