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

Section	Weight	Objectives
Data Processing & Transformations	31%	- Query optimization and performance 1. Optimization strategies 2. Understanding query plans - Data transformation techniques 1. DataFrame operations and transformations 2. Complex data processing logic 3. Aggregations, joins, and window functions
Productionizing Data Pipelines	18%	- Monitoring and troubleshooting 1. Logging and error handling 2. Pipeline reliability and recovery - Deployment and CI/CD 1. Version control integration 2. Databricks Asset Bundles - Workflow orchestration 1. Lakeflow Jobs creation and management 2. Scheduling, triggers, and dependencies
Databricks Intelligence Platform	10%	- Platform architecture and core concepts 1. Workspace navigation and management 2. Compute options: clusters, SQL warehouses, serverless 3. Data layout and optimization: partitioning, file sizing, caching
Development and Ingestion	30%	- Notebook development fundamentals 1. Data exploration and validation 2. Using PySpark and Spark SQL - Data ingestion patterns and methods 1. Delta Lake basics and usage 2. Connecting to external data sources 3. Batch and streaming ingestion
Data Governance & Quality	11%	- Data quality and reliability 1. Data validation and quality checks 2. Schema enforcement and evolution - Unity Catalog implementation 1. Data governance model 2. Permissions and access control

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q251-Q256):

NEW QUESTION # 251
A data engineer has been provided a PySpark DataFrame named df with columns product and revenue. The data engineer needs to compute complex aggregations to determine each product's total revenue, average revenue, and transaction count.
Which code snippet should the data engineer use?

Answer: A

NEW QUESTION # 252
A data engineer is designing a streaming pipeline and wants to limit how long Spark maintains state information for aggregation queries. Which Structured Streaming feature defines how long late data can be processed?

A. Watermark
B. Partition
C. Cache
D. Checkpoint

Answer: A

NEW QUESTION # 253
Which of the following code blocks will remove the rows where the value in column age is greater than 25 from the existing Delta table my_table and save the updated table?

A. SELECT * FROM my_table WHERE age > 25;
B. DELETE FROM my_table WHERE age > 25;
C. DELETE FROM my_table WHERE age <= 25;
D. UPDATE my_table WHERE age <= 25;
E. UPDATE my_table WHERE age > 25;

Answer: B

Explanation:
The DELETE command in Delta Lake allows you to remove data that matches a predicate from a Delta table.
This command will delete all the rows where the value in the column age is greater than 25 from the existing Delta table my_table and save the updated table. The other options are either incorrect or do not achieve the desired result. Option A will only select the rows that match the predicate, but not delete them. Option B will update the rows that match the predicate, but not delete them. Option D will update the rows that do not match the predicate, but not delete them. Option E will delete the rows that do not match the predicate, which is the opposite of what we want. References: Table deletes, updates, and merges - Delta Lake Documentation

NEW QUESTION # 254
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. DELTA
B. autoloader
C. org.apache.spark.sql.jdbc
D. sqlite
E. org.apache.spark.sql.sqlite

Answer: D

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 # 255
A data engineer wants to create a relational object by pulling data from two tables. The relational object does not need to be used by other data engineers in other sessions. In order to save on storage costs, the data engineer wants to avoid copying and storing physical data.
Which of the following relational objects should the data engineer create?

A. Temporary view
B. View
C. Delta Table
D. Database
E. Spark SQL Table

Answer: A

Explanation:
A temporary view is a relational object that is defined in the metastore and points to an existing DataFrame. It does not copy or store any physical data, but only saves the query that defines the view. The lifetime of a temporary view is tied to the SparkSession that was used to create it, so it does not persist across different sessions or applications. A temporary view is useful for accessing the same data multiple times within the same notebook or session, without incurring additional storage costs. The other options are either materialized (A, E), persistent (B, C), or not relational objects . Reference: Databricks Documentation - Temporary View, Databricks Community - How do temp views actually work?, Databricks Community - What's the difference between a Global view and a Temp view?, Big Data Programmers - Temporary View in Databricks.

NEW QUESTION # 256
......

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