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

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

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q218-Q223):

NEW QUESTION # 218
Which of the following benefits of using the Databricks Lakehouse Platform is provided by Delta Lake?

A. The ability to collaborate in real time on a single notebook
B. The ability to support batch and streaming workloads
C. The ability to set up alerts for query failures
D. The ability to distribute complex data operations
E. The ability to manipulate the same data using a variety of languages

Answer: B

Explanation:
Delta Lake is the optimized storage layer that provides the foundation for storing data and tables in the Databricks lakehouse. Delta Lake is fully compatible with Apache Spark APIs, and was developed for tight integration with Structured Streaming, allowing you to easily use a single copy of data for both batch and streaming operations and providing incremental processing at scale1. Delta Lake supports upserts using the merge operation, which enables you to efficiently update existing data or insert new data into your Delta tables2. Delta Lake also provides time travel capabilities, which allow you to query previous versions of your data or roll back to a specific point in time3. Reference: 1: What is Delta Lake? | Databricks on AWS 2: Upsert into a table using merge | Databricks on AWS 3: [Query an older snapshot of a table (time travel) | Databricks on AWS] Learn more

NEW QUESTION # 219
Which of the following queries is performing a streaming hop from raw data to a Bronze table?

Answer: C

NEW QUESTION # 220
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 OPTIMIZE command was nun on the table
B. The VACUUM command was run on the table
C. The TIME TRAVEL command was run on the table
D. The DELETE HISTORY command was run on the table
E. The HISTORY command was run on the table

Answer: D

NEW QUESTION # 221
A data engineer needs to create a table in Databricks using data from a CSV file at location /path/to/csv.
They run the following command:

Which of the following lines of code fills in the above blank to successfully complete the task?

A. FROM "path/to/csv"
B. None of these lines of code are needed to successfully complete the task
C. USING CSV
D. FROM CSV
E. USING DELTA

Answer: C

NEW QUESTION # 222
A data engineer has a single-task Job that runs each morning before they begin working. After identifying an upstream data issue, they need to set up another task to run a new notebook prior to the original task.
Which of the following approaches can the data engineer use to set up the new task?

A. They can create a new task in the existing Job and then add it as a dependency of the original task.
B. They can create a new task in the existing Job and then add the original task as a dependency of the new task.
C. They can create a new job from scratch and add both tasks to run concurrently.
D. They can clone the existing task in the existing Job and update it to run the new notebook.
E. They can clone the existing task to a new Job and then edit it to run the new notebook.

Answer: E

NEW QUESTION # 223
......

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