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

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

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q154-Q159):

NEW QUESTION # 154
A data engineer needs to parse only png files in a directory that contains files with different suffixes. Which code should the data engineer use to achieve this task?

Answer: D

NEW QUESTION # 155
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 SELECT PRIVILEGES ON DATABASE customers TO teams;
B. GRANT USAGE ON DATABASE customers TO team;
C. GRANT SELECT CREATE MODIFY USAGE PRIVILEGES ON DATABASE customers TO team;
D. GRANT ALL PRIVILEGES ON DATABASE customers TO team;
E. GRANT ALL PRIVILEGES ON DATABASE team TO customers;

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. Reference:
GRANT
Privileges

NEW QUESTION # 156
A data provider must share a live Delta table with an external partner who uses an open-source Spark environment outside Databricks, without copying the data.
Which feature should be used?

A. A materialized view exported as Parquet and delivered by SFTP
B. A deep clone written to a partner-owned S3 bucket nightly
C. Delta Sharing with a recipient and share
D. A Unity Catalog external location with a storage credential granted to the partner

Answer: C

Explanation:
Delta Sharing is an open protocol that lets a provider share live data from Unity Catalog with recipients on any platform that supports the protocol, with no data replication. Access is governed by shares and recipients, and the recipient always reads the current version of the data.

NEW QUESTION # 157
A data analyst has developed a query that runs against Delta table. They want help from the data engineering team to implement a series of tests to ensure the data returned by the query is clean. However, the data engineering team uses Python for its tests rather than SQL.
Which of the following operations could the data engineering team use to run the query and operate with the results in PySpark?

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

Answer: E

Explanation:
Explanation
from pyspark.sql import SparkSession
spark = SparkSession.builder.getOrCreate()
df = spark.sql("SELECT * FROM sales")
print(df.count())

NEW QUESTION # 158
A data engineer is running a Spark SQL job on Databricks and notices that joins between a small lookup table and a large fact table are slow. The engineer wants Spark to automatically send the small lookup table to all executors to accelerate the join.
Which configuration parameter should the data engineer adjust?

A. spark.executor.memory
B. spark.sql.autoBroadcastJoinThreshold
C. spark.default.parallelism
D. spark.sql.shuffle.partitions

Answer: B

NEW QUESTION # 159
......

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