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

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

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q71-Q76):

NEW QUESTION # 71
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: B

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 # 72
Which of the following describes a scenario in which a data team will want to utilize cluster pools?

Answer: C

Explanation:
Databricks cluster pools are a set of idle, ready-to-use instances that can reduce cluster start and auto-scaling times. This is useful for scenarios where a data team needs to run an automated report as quickly as possible, without waiting for the cluster to launch or scale up. Cluster pools can also help save costs by reusing idle instances across different clusters and avoiding DBU charges for idle instances in the pool. References: Best practices: pools | Databricks on AWS, Best practices: pools - Azure Databricks | Microsoft Learn, Best practices: pools | Databricks on Google Cloud


NEW QUESTION # 73
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?

Answer: C

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


NEW QUESTION # 74
The Delta transaction log for the 'students' tables is shown using the 'DESCRIBE HISTORY students' command. A Data Engineer needs to query the table as it existed before the UPDATE operation listed in the log.

Which command should the Data Engineer use to achieve this? (Choose two.)

Answer: A,D


NEW QUESTION # 75
A data engineering team runs multiple daily batch jobs. The team needs to run the jobs on a fixed overnight schedule with consistent performance and controlled resource usage. The team wants to minimize operational overhead and reduce idle cluster costs.
Which compute option should the team use?

Answer: B


NEW QUESTION # 76
......

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