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

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

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q303-Q308):

NEW QUESTION # 303
A data engineer is designing a data pipeline. The source system generates files in a shared directory that is also used by other processes. As a result, the files should be kept as is and will accumulate in the directory. The data engineer needs to identify which files are new since the previous run in the pipeline, and set up the pipeline to only ingest those new files with each run.
Which of the following tools can the data engineer use to solve this problem?

A. Unity Catalog
B. Data Explorer
C. Delta Lake
D. Databricks SQL
E. Auto Loader

Answer: E

Explanation:
Auto Loader is a tool that can incrementally and efficiently process new data files as they arrive in cloud storage without any additional setup. Auto Loader provides a Structured Streaming source called cloudFiles, which automatically detects and processes new files in a given input directory path on the cloud file storage.
Auto Loader also tracks the ingestion progress and ensures exactly-once semantics when writing data into Delta Lake. Auto Loader can ingest various file formats, such as JSON, CSV, XML, PARQUET, AVRO, ORC, TEXT, and BINARYFILE. Auto Loader has support for both Python and SQL in Delta Live Tables, which are a declarative way to build production-quality data pipelines with Databricks. References: What is Auto Loader?, Get started with Databricks Auto Loader, Auto Loader in Delta Live Tables

NEW QUESTION # 304
Which Databricks Asset Bundle format is valid?

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

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 # 305
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. org.apache.spark.sql.sqlite
B. org.apache.spark.sql.jdbc
C. sqlite
D. DELTA
E. autoloader

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 # 306
A data engineer is working in a Python notebook on Databricks to process data, but notices that the output is not as expected. The data engineer wants to investigate the issue by stepping through the code and checking the values of certain variables during execution. Which tool should the data engineer use to inspect the code execution and variables in real-time?

A. SQL Analytics
B. Job Execution Dashboard
C. Python Notebook Interactive Debugger
D. Cluster Logs

Answer: C

Explanation:
The Python Notebook Interactive Debugger in Databricks allows stepping through code execution, setting breakpoints, and inspecting variable values in real-time. This makes it the correct tool for debugging Python code inside a Databricks notebook.

NEW QUESTION # 307
A data engineer has configured a Structured Streaming job to read from a table, manipulate the data, and then perform a streaming write into a new table.
The cade block used by the data engineer is below:

If the data engineer only wants the query to execute a micro-batch to process data every 5 seconds, which of the following lines of code should the data engineer use to fill in the blank?

A. trigger(processingTime="5 seconds")
B. trigger(continuous="5 seconds")
C. trigger()
D. trigger("5 seconds")
E. trigger(once="5 seconds")

Answer: A

Explanation:
The processingTime option specifies a time-based trigger interval for fixed interval micro-batches. This means that the query will execute a micro-batch to process data every 5 seconds, regardless of how much data is available. This option is suitable for near-real time processing workloads that require low latency and consistent processing frequency. The other options are either invalid syntax (A, C), default behavior (B), or experimental feature (E). Reference: Databricks Documentation - Configure Structured Streaming trigger intervals, Databricks Documentation - Trigger.

NEW QUESTION # 308
......

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