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

Section	Weight	Objectives
Topic 1: Lakehouse Platform Concepts	10-15%	- Understand the Lakehouse architecture and its benefits - Describe key Databricks Lakehouse platform components - Explain data governance and security concepts
Topic 2: 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
Topic 3: Delta Lake Fundamentals	20-25%	- Explain Delta Lake features and benefits - Write to and read from Delta tables - Create and manage Delta tables - Understand ACID transactions and time travel
Topic 4: Python for Data Engineering	10-15%	- Implement user-defined functions (UDFs) - Work with Spark APIs in Python - Use PySpark for data processing
Topic 5: Spark SQL and DataFrames	15-20%	- Handle null values and data quality - Write and execute Spark SQL queries - Aggregate and group data - Join and union DataFrames
Topic 6: 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 (Q224-Q229):

NEW QUESTION # 224
Which of the following describes a benefit of creating an external table from Parquet rather than CSV when using a CREATE TABLE AS SELECT statement?

A. Parquet files have a well-defined schema
B. CREATE TABLE AS SELECT statements cannot be used on files
C. Parquet files have the ability to be optimized
D. Parquet files will become Delta tables
E. Parquet files can be partitioned

Answer: A

NEW QUESTION # 225
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. spark.table("sales")
B. spark.sql("sales")
C. SELECT * FROM sales
D. There is no way to share data between PySpark and SQL.
E. spark.delta.table("sales")

Answer: A

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 # 226
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 job from scratch and add both tasks to run concurrently.
B. They can create a new task in the existing Job and then add it as a dependency of the original task.
C. They can clone the existing task in the existing Job and update it to run the new notebook.
D. They can create a new task in the existing Job and then add the original task as a dependency of the new task.
E. They can clone the existing task to a new Job and then edit it to run the new notebook.

Answer: B

NEW QUESTION # 227
A data engineer wants to delete records from a Delta table where the status column equals
"inactive". The operation should modify only the affected rows without rewriting the entire dataset.
Which SQL command should be used?

A. DELETE
B. MERGE
C. UPDATE
D. INSERT

Answer: A

NEW QUESTION # 228
A new data engineering team team. has been assigned to an ELT project. The new data engineering team will need full privileges on the database customers to fully manage the project.
Which of the following commands can be used to grant full permissions on the database to the new data engineering team?

A. GRANT USAGE ON DATABASE customers TO team;
B. GRANT ALL PRIVILEGES ON DATABASE team TO customers;
C. GRANT SELECT CREATE MODIFY USAGE PRIVILEGES ON DATABASE customers TO team;
D. GRANT ALL PRIVILEGES ON DATABASE customers TO team;
E. GRANT SELECT PRIVILEGES ON DATABASE customers TO teams;

Answer: D

Explanation:
To grant full permissions on a database to a user, group, or service principal, the GRANT ALL PRIVILEGES ON DATABASE command can be used. This command grants all the applicable privileges on the database, such as CREATE, SELECT, MODIFY, and USAGE. The other options are either incorrect or incomplete, as they do not grant all the privileges or specify the wrong database or principal. References:
* GRANT
* Privileges

NEW QUESTION # 229
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