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

Section	Objectives
Topic 1: Data Ingestion and ELT Development	- Data ingestion using Spark SQL and PySpark - ETL patterns and transformations - Handling structured and semi-structured data
Topic 2: Productionizing Data Pipelines	- Databricks Workflows / Jobs orchestration - Scheduling and monitoring jobs - Pipeline deployment and operationalization
Topic 3: Data Processing and Transformations	- PySpark DataFrame transformations - Delta Lake fundamentals (tables, transactions, optimization) - Apache Spark SQL operations (joins, aggregations, filtering) - User-defined functions (UDFs)
Topic 4: Data Governance and Quality	- Unity Catalog basics - Data quality concepts and management - Data access control and governance
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 (Q279-Q284):

NEW QUESTION # 279
A data engineer has a Python variable table_name that they would like to use in a SQL query. They want to construct a Python code block that will run the query using table_name.
They have the following incomplete code block:
____(f"SELECT customer_id, spend FROM {table_name}")
Which of the following can be used to fill in the blank to successfully complete the task?

A. dbutils.sql
B. spark.sql
C. spark.delta.sql
D. spark.table
E. spark.delta.table

Answer: B

Explanation:
The spark.sql method can be used to execute SQL queries programmatically and return the result as a DataFrame. The spark.sql method accepts a string argument that contains a valid SQL statement. The data engineer can use a formatted string literal (f-string) to insert the Python variable table_name into the SQL query. The other methods are either invalid or not suitable for running SQL queries. Reference: Running SQL Queries Programmatically, Formatted string literals, spark.sql

NEW QUESTION # 280
Which method should a Data Engineer apply to ensure Workflows are being triggered on schedule?

A. Scheduled Workflows require an always-running cluster, which is more expensive but reduces processing latency.
B. Scheduled Workflows can reduce resource consumption and expense since the cluster runs only long enough to execute the pipeline.
C. Scheduled Workflows process data as it arrives at configured sources.
D. Scheduled Workflows run continuously until manually stopped.

Answer: B

Explanation:
Scheduled Workflows in Databricks are designed to trigger jobs at specified intervals. These workflows can be configured to start a cluster when the job is scheduled, and the cluster will only run for the duration of the task. Once the workflow completes, the cluster can be terminated, which helps to reduce resource consumption and costs since you are only paying for the cluster while the workflow is running.

NEW QUESTION # 281
A data engineer is designing an ETL pipeline to process both streaming and batch data from multiple sources The pipeline must ensure data quality, handle schema evolution, and provide easy maintenance. The team is considering using Delta Live Tables (DLT) in Databricks to achieve these goals. They want to understand the key features and benefits of DLT that make it suitable for this use case.
Why is Delta Live Tables (DLT) an appropriate choice?

A. Requires custom code for data quality checks, no support for streaming data, and complex pipeline maintenance
B. Supports only batch processing, no data versioning, and high infrastructure costs
C. Manual schema enforcement, high operational overhead, and limited scalability
D. Automatic data quality checks, built-in support for schema evolution, and declarative pipeline development

Answer: D

NEW QUESTION # 282
A data engineer has configured a Lakeflow Job that runs daily to ingest customer transaction data from a legacy relational database. The extracted data must be written directly to a Unity Catalog table and be immediately queryable via SQL. The team needs to ensure full data lineage is preserved. Which action enables this data ingestion with direct landing in Unity Catalog, preserved lineage, and immediate SQL query ability?

A. Use spark.read.format("jdbc") and write with saveAsTable("catalog.schema.table")
B. Stage data in cloud storage via REST API, then load with a separate job
C. Use ODBC pooling to extract all records in one batch and write to /dbfs/
D. Extract to Pandas DataFrame, convert to Spark, then save() to external path

Answer: A

Explanation:
spark.read.format("jdbc") loads the relational data into a Spark DataFrame, and saveAsTable("catalog.schema.table") writes it directly as a Unity Catalog table that is immediately queryable with SQL. Unity Catalog captures lineage for the Databricks read/write operation.

NEW QUESTION # 283
A data architect has determined that a table of the following format is necessary:

Which of the following code blocks uses SQL DDL commands to create an empty Delta table in the above format regardless of whether a table already exists with this name?

A. Option A
B. Option C
C. Option B
D. Option D
E. Option E

Answer: E

Explanation:
References: Create a table using SQL | Databricks on AWS, Create a table using SQL - Azure Databricks, Delta Lake Quickstart - Azure Databricks

NEW QUESTION # 284
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