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

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

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q263-Q268):

NEW QUESTION # 263
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: B


NEW QUESTION # 264
A BI team runs short, highly concurrent SQL queries against gold tables and needs sub-second startup with no cluster management or idle cost.
Which compute option best fits this requirement?

Answer: A

Explanation:
Serverless SQL warehouses start in seconds from a Databricks-managed compute pool, scale automatically with query concurrency, and stop billing when idle. All-purpose and classic warehouses require cluster provisioning time and can incur idle cost while waiting for auto- termination.


NEW QUESTION # 265
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?

Answer: E

Explanation:
In the given command, a data engineer is trying to create a table in Databricks using data from an SQLite database. The correct option to fill in the blank is "sqlite" because it specifies the type of database being connected to in a JDBC connection string. The USING clause should be followed by the format of the data, and since we are connecting to an SQLite database, "sqlite" would be appropriate here. References:
* Create a table using JDBC
* JDBC connection string
* SQLite JDBC driver


NEW QUESTION # 266
A data engineer is migrating pipeline tasks to reduce operational toil. The workspace used Unity Catalog and is in a region that supports serverless. The engineer wants Databricks to auto-select instance types, manage scaling, apply Photon, and handle runtime upgrades automatically for job runs.
How should the data engineer meet this requirement while adhering to Databricks constraints?

Answer: C

Explanation:
Serverless compute for workflows allows Databricks to automatically manage instance selection, scaling, Photon enablement, and runtime upgrades for job runs, provided Unity Catalog is enabled and the workspace region supports serverless.


NEW QUESTION # 267
A data engineer needs to ingest from both streaming and batch sources for a firm that relies on highly accurate data. Occasionally, some of the data picked up by the sensors that provide a streaming input are outside the expected parameters. If this occurs, the data must be dropped, but the stream should not fail.
Which feature of Delta Live Tables meets this requirement?

Answer: B


NEW QUESTION # 268
......

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