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

Section	Objectives
Data Processing and Transformations	- User-defined functions (UDFs) - Delta Lake fundamentals (tables, transactions, optimization) - PySpark DataFrame transformations - Apache Spark SQL operations (joins, aggregations, filtering)
Data Ingestion and ELT Development	- Data ingestion using Spark SQL and PySpark - ETL patterns and transformations - Handling structured and semi-structured data
Data Governance and Quality	- Unity Catalog basics - Data access control and governance - Data quality concepts and management
Productionizing Data Pipelines	- Pipeline deployment and operationalization - Databricks Workflows / Jobs orchestration - Scheduling and monitoring jobs
Databricks Lakehouse Platform Fundamentals	- Clusters, notebooks, and basic Databricks environment usage - Workspace, architecture, and core platform concepts

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q244-Q249):

NEW QUESTION # 244
A data engineer is writing a script that is meant to ingest new data from cloud storage. In the event of the Schema change, the ingestion should fail. It should fail until the changes downstream source can be found and verified as intended changes.
Which command will meet the requirements?

A. none
B. rescue
C. addNewColumns
D. failOnNewColumns

Answer: D

NEW QUESTION # 245
A Delta Live Table pipeline includes two datasets defined using STREAMING LIVE TABLE.
Three datasets are defined against Delta Lake table sources using LIVE TABLE.
The table is configured to run in Development mode using the Continuous Pipeline Mode.
Assuming previously unprocessed data exists and all definitions are valid, what is the expected outcome after clicking Start to update the pipeline?

A. All datasets will be updated once and the pipeline will shut down. The compute resources will be terminated.
B. All datasets will be updated once and the pipeline will shut down. The compute resources will persist to allow for additional testing.
C. All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will persist until the pipeline is shut down.
D. All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will persist to allow for additional testing.
E. All datasets will be updated once and the pipeline will persist without any processing. The compute resources will persist but go unused.

Answer: D

NEW QUESTION # 246
A data engineer has been provided a PySpark DataFrame named df with columns product and revenue. The data engineer needs to compute complex aggregations to determine each product's total revenue, average revenue, and transaction count.
Which code snippet should the data engineer use?

Answer: C

NEW QUESTION # 247
A data engineer has configured a Structured Streaming job to read from a table, manipulate the data, and then perform a streaming write into a new table.

The code block used by the data engineer is below:
Which line of code should the data engineer use to fill in the blank if the data engineer only wants the query to execute a micro-batch to process data every 5 seconds?

A. trigger(once="5 seconds")
B. trigger(continuous="5 seconds")
C. trigger(processingTime="5 seconds")
D. trigger("5 seconds")

Answer: C

NEW QUESTION # 248
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_sales = sales df.groupBy("category").agg(sum("sales amount") .alias ("total sales amount"))
B. Category_sales = sales_df.groupBy("reqion"). agq(sum("sales_amountn).alias(ntotal_sales_amount''))
C. Category_sale: .es df -agg (sum ("sales amount") .-;r*i:rRy ("category") .alias ("total sa.en amount"))
D. Category_sales = sales_df.sum("3ales_amount"). g-1- upBy("categcryn).alias("toLal_sales_amount))

Answer: A

NEW QUESTION # 249
......

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