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

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

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q63-Q68):

NEW QUESTION # 63
A data engineer has realized that they made a mistake when making a daily update to a table. They need to use Delta time travel to restore the table to a version that is 3 days old. However, when the data engineer attempts to time travel to the older version, they are unable to restore the data because the data files have been deleted.
Which of the following explains why the data files are no longer present?

A. The DELETE HISTORY command was run on the table
B. The OPTIMIZE command was nun on the table
C. The HISTORY command was run on the table
D. The VACUUM command was run on the table
E. The TIME TRAVEL command was run on the table

Answer: A

NEW QUESTION # 64
A data analyst has created a Delta table sales that is used by the entire data analysis team. They want help from the data engineering team to implement a series of tests to ensure the data is clean. However, the data engineering team uses Python for its tests rather than SQL.
Which of the following commands could the data engineering team use to access sales in PySpark?

A. There is no way to share data between PySpark and SQL.
B. SELECT * FROM sales
C. spark.delta.table("sales")
D. spark.table("sales")
E. spark.sql("sales")

Answer: D

Explanation:
The data engineering team can use the spark.table method to access the Delta table sales in PySpark. This method returns a DataFrame representation of the Delta table, which can be used for further processing or testing. The spark.table method works for any table that is registered in the Hive metastore or the Spark catalog, regardless of the file format1. Alternatively, the data engineering team can also use the DeltaTable.
forPath method to load the Delta table from its path2. References: 1: SparkSession | PySpark 3.2.0 documentation 2: Welcome to Delta Lake's Python documentation page - delta-spark 2.4.0 documentation

NEW QUESTION # 65
Which query is performing a streaming hop from raw data to a Bronze table?

Answer: D

Explanation:
The query performing a streaming hop from raw data to a Bronze table is identified by using the Spark streaming read capability and then writing to a Bronze table. Let ' s analyze the options:
Option A: Utilizes .writeStream but performs a complete aggregation which is more characteristic of a roll-up into a summarized table rather than a hop into a Bronze table.
Option B: Also uses .writeStream but calculates an average, which again does not typically represent the raw to Bronze transformation, which usually involves minimal transformations.
Option C: This uses a basic .write with .mode( " append " ) which is not a streaming operation, and hence not suitable for real-time streaming data transformation to a Bronze table.
Option D: It employs spark.readStream.load() to ingest raw data as a stream and then writes it out with .
writeStream, which is a typical pattern for streaming data into a Bronze table where raw data is captured in real-time and minimal transformation is applied. This approach aligns with the concept of a Bronze table in a modern data architecture, where raw data is ingested continuously and stored in a more accessible format.
References:Databricks documentation on Structured Streaming: Structured Streaming in Databricks

NEW QUESTION # 66
Which Databricks Asset Bundle format is valid?

A. resources {
jobs {
name = " hello-job "
tasks {
task_key = " hello-task "
existing_cluster_id = " 1234-567890-abcde123 "
notebook_task {
notebook_path = " ./hello.py "
}
}
}
}
B. configuration = {
" resources " : {
" jobs " : {
" name " : " hello-job " ,
" tasks " : {
" task_key " : " hello-task " ,
" existing_cluster_id " : " 1234-567890-abcde123 " ,
" notebook_task " : {
" notebook_path " : " ./hello.py "
}
}
}
}
}
C. resources:
jobs:
hello-job:
name: hello-job
tasks:
- task_key: hello-task
existing_cluster_id: 1234-567890-abcde123
notebook_task:
notebook_path: ./hello.py
D. {
" resources " : {
" jobs " : {
" name " : " hello-job " ,
" tasks " : {
" task_key " : " hello-task " ,
" existing_cluster_id " : " 1234-567890-abcde123 " ,
" notebook_task " : {
" notebook_path " : " ./hello.py "
}
}
}
}
}

Answer: C

Explanation:
Databricks Asset Bundles use YAML configuration, and Databricks documentation specifies that bundle configuration is expressed in a root databricks.yml file. The resources mapping is used to define supported objects such as jobs , and job definitions include keys like name and tasks. Tasks in a job are defined under the tasks key, and a notebook task is represented with notebook_task and notebook_path. Option A is the only answer written in the correct general YAML bundle structure and matches the documented style of defining bundle resources. Options B and C use JSON- and Python-style syntax rather than bundle YAML. Option D uses Terraform/HCL-style syntax, which is not the Databricks Asset Bundle format. While a full production bundle often includes more top-level settings such as bundle, targets, or workspace, the question asks which format is valid, and only option A matches the documented Databricks Asset Bundles configuration model.
=========

NEW QUESTION # 67
A data engineer needs to create a table in Databricks using data from their organization's existing SQLite database. They run the following command:
CREATE TABLE jdbc_customer360
USING
OPTIONS (
url "jdbc:sqlite:/customers.db", dbtable "customer360"
)
Which line of code fills in the above blank to successfully complete the task?

A. autoloader
B. org.apache.spark.sql.sqlite
C. sqlite
D. org.apache.spark.sql.jdbc

Answer: D

Explanation:
To create a table in Databricks using data from an SQLite database, the correct syntax involves specifying the format of the data source. The format in the case of using JDBC (Java Database Connectivity) with SQLite is specified by the org.apache.spark.sql.jdbc format. This format allows Spark to interface with various relational databases through JDBC. Here is how the command should be structured:
CREATE TABLE jdbc_customer360
USING org.apache.spark.sql.jdbc
OPTIONS (
url "jdbc:sqlite:/customers.db",
dbtable "customer360"
)
The USING org.apache.spark.sql.jdbc line specifies that the JDBC data source is being used, enabling Spark to interact with the SQLite database via JDBC.

NEW QUESTION # 68
......

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