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

Section	Weight	Objectives
Development and Ingestion	30%	- Notebook development fundamentals 1. Data exploration and validation 2. Using PySpark and Spark SQL - Data ingestion patterns and methods 1. Connecting to external data sources 2. Batch and streaming ingestion 3. Delta Lake basics and usage
Data Governance & Quality	11%	- Unity Catalog implementation 1. Permissions and access control 2. Data governance model - Data quality and reliability 1. Data validation and quality checks 2. Schema enforcement and evolution
Databricks Intelligence Platform	10%	- Platform architecture and core concepts 1. Data layout and optimization: partitioning, file sizing, caching 2. Compute options: clusters, SQL warehouses, serverless 3. Workspace navigation and management
Data Processing & Transformations	31%	- Query optimization and performance 1. Understanding query plans 2. Optimization strategies - Data transformation techniques 1. Aggregations, joins, and window functions 2. Complex data processing logic 3. DataFrame operations and transformations
Productionizing Data Pipelines	18%	- Monitoring and troubleshooting 1. Pipeline reliability and recovery 2. Logging and error handling - Deployment and CI/CD 1. Databricks Asset Bundles 2. Version control integration - Workflow orchestration 1. Lakeflow Jobs creation and management 2. Scheduling, triggers, and dependencies

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q253-Q258):

NEW QUESTION # 253
A data engineer is working on a personal laptop and needs to perform complex transformations on data stored in a Delta Lake on cloud storage. The engineer decides to use Databricks Connect to interact with Databricks clusters and work in their local IDE.
How does Databricks Connect enable the engineer to develop, test, and debug code seamlessly on their local machine while interacting with Databricks clusters?

A. By providing a local environment that mimics the Databricks runtime, enabling the engineer to develop, test, and debug code using a specific IDE that is required by Databricks
B. By providing a local environment that mimics the Databricks runtime, enabling the engineer to develop, test, and debug code using their preferred ide
C. By allowing direct execution of Spark jobs from the local machine without needing a network connection
D. By providing a local environment that mimics the Databricks runtime, enabling the engineer to develop, test, and debug code only through Databricks' own web interface

Answer: B

NEW QUESTION # 254
A data engineer is using the following code block as part of a batch ingestion pipeline to read from a composable table:

Which of the following changes needs to be made so this code block will work when the transactions table is a stream source?

A. Replace spark.read with spark.readStream
B. Replace predict with a stream-friendly prediction function
C. Replace "transactions" with the path to the location of the Delta table
D. Replace format("delta") with format("stream")
E. Replace schema(schema) with option ("maxFilesPerTrigger", 1)

Answer: A

Explanation:
To read from a stream source, the data engineer needs to use the spark.readStream method instead of the spark.
read method. The spark.readStream method returns a DataStreamReader object that can be used to specify the details of the input source, such as the format, the schema, the path, and the options. The spark.read method is only suitable for batch processing, not streaming processing. The other changes are not necessary or correct for reading from a stream source. References: Structured Streaming Programming Guide, Read a stream, Databricks Data Sources

NEW QUESTION # 255
A company sells products across multiple categories (e.g., Electronics, Clothing) and regions.
The sales team has provided you with a PySpark dataframe named sales_df as below, and the team wants the data engineer to analyze the sales data to help make strategic decisions.

Calculate the total sales amount for each region and store the results in a new dataframe called region_sales.
Given the expected result:

Which code will generate the expected result?

A. region_sales = sales_df.groupBy("region").agg(sum("sales_amount").alias("total_sales_amount"))
B. region_sales = sales_df.sum("sales_amount").groupBy("region").alias("total_sales_amount")
C. region_sales = sales_df.groupBy("category").sum("sales_amount").alias("total_sales_amount")
D. region_sales = sales_df.agg(sum("sales_amount").groupBy("region").alias("total_sales_amount"))

Answer: A

Explanation:
To calculate total sales per region, you group by "region" and apply an aggregation using sum("sales_amount"), aliasing it as total_sales_amount. This produces the expected result of 250 (North), 500 (South), 250 (East), and 400 (West).

NEW QUESTION # 256
A global retail company sells products across multiple categories (e.g.. Electronics, Clothing) and regions (e.
g.. North. South, East. West). The sales team has provided the data engineer with a PySpark dataframe named sales_df as below and the team wants the data engineer to analyze the sales data to help them make strategic decisions.

A. Category_sale: .es df -agg (sum ("sales amount") .-;r*i:rRy ("category") .alias ("total sa.en amount"))
B. Category_sales = sales df.groupBy("category").agg(sum("sales amount") .alias ("total sales amount"))
C. Category_sales = sales_df.groupBy("reqion"). agq(sum("sales_amountn).alias(ntotal_sales_amount''))
D. Category_sales = sales_df.sum("3ales_amount"). g-1- upBy("categcryn).alias("toLal_sales_amount))

Answer: B

NEW QUESTION # 257
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.jdbc
B. autoloader
C. org.apache.spark.sql.sqlite
D. DELTA
E. sqlite

Answer: E

Explanation:
1: 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. Reference:
Create a table using JDBC
JDBC connection string
SQLite JDBC driver

NEW QUESTION # 258
......

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