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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 - Write to and read from Delta tables - Explain Delta Lake features and benefits - Create and manage Delta tables
Topic 2: Data Pipeline Architecture	15-20%	- Implement incremental data processing - Understand ELT vs ETL patterns - Design data pipelines for batch and streaming - Monitor and optimize pipeline performance
Topic 3: Spark SQL and DataFrames	15-20%	- Handle null values and data quality - Join and union DataFrames - Aggregate and group data - Write and execute Spark SQL queries
Topic 4: 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 5: Python for Data Engineering	10-15%	- Implement user-defined functions (UDFs) - Use PySpark for data processing - Work with Spark APIs in Python
Topic 6: 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

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q174-Q179):

NEW QUESTION # 174
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. spark.table("sales")
C. SELECT * FROM sales
D. spark.sql("sales")
E. spark.delta.table("sales")

Answer: B

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 # 175
In a Lakeflow Declarative Pipeline (Delta Live Tables), a data engineer must ensure that records with a null order_id are discarded from the silver table while the pipeline continues to run.
Which expectation clause should be used?

A. CONSTRAINT valid_id EXPECT (order_id IS NOT NULL) ON VIOLATION DROP ROW
B. ALTER TABLE silver_orders ADD CONSTRAINT valid_id CHECK (order_id IS NOT NULL)
C. CONSTRAINT valid_id EXPECT (order_id IS NOT NULL) ON VIOLATION FAIL UPDATE
D. CONSTRAINT valid_id EXPECT (order_id IS NOT NULL)

Answer: A

NEW QUESTION # 176
A new data engineering team team has been assigned to an ELT project. The new data engineering team will need full privileges on the table sales to fully manage the project.
Which command can be used to grant full permissions on the database to the new data engineering team?

A. GRANT ALL PRIVILEGES ON TABLE team TO sales;
B. GRANT SELECT CREATE MODIFY ON TABLE sales TO team;
C. GRANT SELECT ON TABLE sales TO team;
D. grant all privileges on table sales TO team;

Answer: D

Explanation:
To grant full privileges on a table such as 'sales' to a group like 'team', the correct SQL command in Databricks is:
GRANT ALL PRIVILEGES ON TABLE sales TO team;
This command assigns all available privileges, including SELECT, INSERT, UPDATE, DELETE, and any other data manipulation or definition actions, to the specified team. This is typically necessary when a team needs full control over a table to manage and manipulate it as part of a project or ongoing maintenance.

NEW QUESTION # 177
A data engineering team has two tables. The first table march_transactions is a collection of all retail transactions in the month of March. The second table april_transactions is a collection of all retail transactions in the month of April. There are no duplicate records between the tables.
Which of the following commands should be run to create a new table all_transactions that contains all records from march_transactions and april_transactions without duplicate records?

A. CREATE TABLE all_transactions AS
SELECT * FROM march_transactions
OUTER JOIN SELECT * FROM april_transactions;
B. CREATE TABLE all_transactions AS
SELECT * FROM march_transactions
UNION SELECT * FROM april_transactions;
C. CREATE TABLE all_transactions AS
SELECT * FROM march_transactions
INNER JOIN SELECT * FROM april_transactions;
D. CREATE TABLE all_transactions AS
SELECT * FROM march_transactions
INTERSECT SELECT * from april_transactions;
E. CREATE TABLE all_transactions AS
SELECT * FROM march_transactions
MERGE SELECT * FROM april_transactions;

Answer: B

Explanation:
Explanation
To create a new table all_transactions that contains all records from march_transactions and april_transactions without duplicate records, you should use the UNION operator, as shown in option B. This operator combines the result sets of the two tables while automatically removing duplicate records.

NEW QUESTION # 178
A data engineer has three tables in a Delta Live Tables (DLT) pipeline. They have configured the pipeline to drop invalid records at each table. They notice that some data is being dropped due to quality concerns at some point in the DLT pipeline. They would like to determine at which table in their pipeline the data is being dropped.
Which of the following approaches can the data engineer take to identify the table that is dropping the records?

A. They can set up DLT to notify them via email when records are dropped.
B. They cannot determine which table is dropping the records.
C. They can set up separate expectations for each table when developing their DLT pipeline.
D. They can navigate to the DLT pipeline page, click on the "Error" button, and review the present errors.
E. They can navigate to the DLT pipeline page, click on each table, and view the data quality statistics.

Answer: E

Explanation:
Explanation
To identify the table in a Delta Live Tables (DLT) pipeline where data is being dropped due to quality concerns, the data engineer can navigate to the DLT pipeline page, click on each table in the pipeline, and view the data quality statistics. These statistics often include information about records dropped, violations of expectations, and other data quality metrics. By examining the data quality statistics for each table in the pipeline, the data engineer can determine at which table the data is being dropped.

NEW QUESTION # 179
......

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