Databricks Databricks-Certified-Data-Engineer-Associate Questions: Improve Your Exam Preparation [2026]

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

SectionObjectives
Data Governance and Quality- Unity Catalog basics
- Data access control and governance
- Data quality concepts and management
Productionizing Data Pipelines- Databricks Workflows / Jobs orchestration
- Scheduling and monitoring jobs
- Pipeline deployment and operationalization
Data Processing and Transformations- PySpark DataFrame transformations
- Delta Lake fundamentals (tables, transactions, optimization)
- Apache Spark SQL operations (joins, aggregations, filtering)
- User-defined functions (UDFs)
Data Ingestion and ELT Development- ETL patterns and transformations
- Handling structured and semi-structured data
- Data ingestion using Spark SQL and PySpark
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 (Q196-Q201):

NEW QUESTION # 196
A data engineer streams customer orders into a Kafka topic (orders_topic) and is currently writing the ingestion script of a DLT pipeline. The data engineer needs to ingest the data from Kafka brokers to DLT using Databricks What is the correct code for ingesting the data?

Answer: D


NEW QUESTION # 197
A data engineer needs to apply custom logic to string column city in table stores for a specific use case. In order to apply this custom logic at scale, the data engineer wants to create a SQL user-defined function (UDF).
Which of the following code blocks creates this SQL UDF?

Answer: B

Explanation:
https://www.databricks.com/blog/2021/10/20/introducing-sql-user-defined-functions.html


NEW QUESTION # 198
A data engineer is facing performance bottlenecks in an e-commerce transactions Delta table.
The table is a managed Unity Catalog table, and it uses partitioning and Z-ordering in its data layout scheme. The predictive optimization for Unity Catalog tables is also enabled. The table has a frequently changing query filter, and the data engineer does not observe a benefit of the Data Layout or the Predictive Optimization. How should the data engineer fix the data layout bottlenecks?

Answer: C

Explanation:
When queries use frequently changing filter conditions, partitioning and Z-ordering lose effectiveness because they rely on fixed columns. The correct solution is to switch to Automatic Liquid Clustering, which dynamically reorganizes data based on observed query patterns, removing the need for manual Z-ordering or static partitioning and resolving the data layout bottleneck.


NEW QUESTION # 199
A data engineer has joined an existing project and they see the following query in the project repository:
CREATE STREAMING LIVE TABLE loyal_customers AS
SELECT customer_id -
FROM STREAM(LIVE.customers)
WHERE loyalty_level = 'high';
Which of the following describes why the STREAM function is included in the query?

Answer: E

Explanation:
The STREAM function is used to process data from a streaming live table or view, which is a table or view that contains data that has been added only since the last pipeline update. Streaming live tables and views are stateful, meaning that they retain the state of the previous pipeline run and only process new data based on the current query. This is useful for incremental processing of streaming or batch data sources. The customers table in the query is a streaming live table, which means that it contains the latest data from the source. The STREAM function enables the query to read the data from the customers table incrementally and create another streaming live table named loyal_customers, which contains the customer IDs of the customers with high loyalty level. References: Difference between LIVE TABLE and STREAMING LIVE TABLE, CREATE STREAMING TABLE, Load data using streaming tables in Databricks SQL.


NEW QUESTION # 200
A new data engineering team has been assigned to work on a project. The team will need access to database customers in order to see what tables already exist. The team has its own group team.
Which of the following commands can be used to grant the necessary permission on the entire database to the new team?

Answer: B

Explanation:
1: The correct command to grant the necessary permission on the entire database to the new team is to use the GRANT USAGE command. The GRANT USAGE command grants the principal the ability to access the securable object, such as a database, schema, or table. In this case, the securable object is the database customers, and the principal is the group team. By granting usage on the database, the team will be able to see what tables already exist in the database. Option E is the only option that uses the correct syntax and the correct privilege type for this scenario. Option A uses the wrong privilege type (VIEW) and the wrong securable object (CATALOG). Option B uses the wrong privilege type (CREATE), which would allow the team to create new tables in the database, but not necessarily see the existing ones. Option C uses the wrong securable object (CATALOG) and the wrong principal (customers). Option D uses the wrong securable object (team) and the wrong principal (customers). Reference: GRANT, Privilege types, Securable objects, Principals


NEW QUESTION # 201
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