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

Section	Weight	Objectives
Topic 1: Delta Lake Fundamentals	20-25%	- Understand ACID transactions and time travel - Explain Delta Lake features and benefits - Create and manage Delta tables - Write to and read from Delta tables
Topic 2: Python for Data Engineering	10-15%	- Implement user-defined functions (UDFs) - Use PySpark for data processing - Work with Spark APIs in Python
Topic 3: Spark SQL and DataFrames	15-20%	- Handle null values and data quality - Aggregate and group data - Join and union DataFrames - Write and execute Spark SQL queries
Topic 4: Data Pipeline Architecture	15-20%	- Design data pipelines for batch and streaming - Monitor and optimize pipeline performance - Understand ELT vs ETL patterns - Implement incremental data processing
Topic 5: Apache Spark Data Processing Fundamentals	20-25%	- Create and use Spark DataFrames - Work with structured data types (arrays, maps, structs) - Use Spark SQL for data processing - Apply transformations and actions on DataFrames
Topic 6: Lakehouse Platform Concepts	10-15%	- Describe key Databricks Lakehouse platform components - Understand the Lakehouse architecture and its benefits - Explain data governance and security concepts

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q278-Q283):

NEW QUESTION # 278
A data engineer must ensure that analysts in different regions querying the same view see only the rows for their own region, without maintaining a separate view per region.
Which approach satisfies this requirement in Unity Catalog?

A. Grant SELECT on the underlying table and revoke it on the view.
B. Use DESCRIBE HISTORY to filter rows by the user who inserted them.
C. Create a dynamic view using is_account_group_member() in the WHERE clause, or apply a row filter function to the table.
D. Create one external table per region under separate external locations.

Answer: C

Explanation:
Dynamic views can call functions such as current_user() and is_account_group_member() to filter rows based on the querying principal's group membership. Unity Catalog also supports row filters and column masks applied directly to a table via user-defined functions, achieving the same result without duplicating views.

NEW QUESTION # 279
A Delta Live Table pipeline includes two datasets defined using streaming live table. Three datasets are defined against Delta Lake table sources using live table.
The table is configured to run in Production mode using the Continuous Pipeline Mode.
What is the expected outcome after clicking Start to update the pipeline assuming previously unprocessed data exists and all definitions are valid?

A. All datasets will be updated once and the pipeline will shut down. The compute resources will be terminated.
B. All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will be deployed for the update and terminated when the pipeline is stopped.
C. All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will persist to allow for additional testing.
D. All datasets will be updated once and the pipeline will shut down. The compute resources will persist to allow for additional testing.

Answer: B

Explanation:
In Delta Live Tables (DLT), when configured to run in Continuous Pipeline Mode, particularly in a production environment, the system is designed to continuously process and update data as it becomes available. This mode keeps the compute resources active to handle ongoing data processing and automatically updates all datasets defined in the pipeline at predefined intervals. Once the pipeline is manually stopped, the compute resources are terminated to conserve resources and reduce costs. This mode is suitable for production environments where datasets need to be kept up-to-date with the latest data.
Reference:
Databricks documentation on Delta Live Tables: Delta Live Tables Guide

NEW QUESTION # 280
Which of the following commands will return the number of null values in the member_id column?

A. SELECT null(member_id) FROM my_table;
B. SELECT count(member_id) - count_null(member_id) FROM my_table;
C. SELECT count_if(member_id IS NULL) FROM my_table;
D. SELECT count(member_id) FROM my_table;
E. SELECT count_null(member_id) FROM my_table;

Answer: C

Explanation:
Explanation
https://docs.databricks.com/en/sql/language-manual/functions/count.html Returns A BIGINT.
If * is specified also counts row containing NULL values.
If expr are specified counts only rows for which all expr are not NULL.
If DISTINCT duplicate rows are not counted.

NEW QUESTION # 281
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. autoloader
B. org.apache.spark.sql.jdbc
C. sqlite
D. DELTA
E. org.apache.spark.sql.sqlite

Answer: C

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 # 282
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?

A. Use an all-purpose cluster with cluster policies to enforce standard sizes and enable autoscaling.
B. Run the job on a serverless compute for workflows configuration, ensuring Unity Catalog is enabled and regional support is available.
C. Create a job with a single-task job cluster and manually set instance families and min/max workers.
D. Use a Pro SQL warehouse and schedule Python notebook tasks to execute as pipeline steps.

Answer: B

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 # 283
......

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