A data engineer, while designing a Pandas UDF to process financial time-series data with complex calculations that require maintaining state across rows within each stock symbol group, must ensure the function is efficient and scalable.
Which approach will solve the problem with minimum overhead while preserving data integrity?

• A.Use a SCALAR_ITER Pandas UDF with iterator-based processing, implementing state management through persistent storage (Delta tables) that gets updated after each batch to maintain continuity across iterator chunks.
• B.Use a SCALAR Pandas UDF that processes the entire dataset at once, implementing custom partitioning logic within the UDF to group by stock symbol and maintain state using global variables shared across all executor processes.
• C.Use applyInPandas() on a Spark DataFrame that receives all rows for each stock symbol as a Pandas DataFrame, allowing processing within each group while maintaining state variables local to each group's processing function.
• D.Use a grouped_agg Pandas UDF that processes each stock symbol group independently, maintaining state through intermediate aggregation results that get passed between successive UDF calls via broadcast variables.

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Which statement describes integration testing?

• A.Validates interactions between subsystems of your application
• B.Requires an automated testing framework
• C.Requires manual intervention
• D.Validates an application use case
• E.Validates behavior of individual elements of your application

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A data engineering team needs to query a Delta table to extract rows that all meet the same condi-tion.
However, the team has noticed that the query is running slowly. The team has already tuned the size of the
data files. Upon investigating, the team has concluded that the rows meeting the condition are sparsely located
throughout each of the data files.
Based on the scenario, which of the following optimization techniques could speed up the query?

• A.Bin-packing
• B.Write as a Parquet file
• C.Data skipping
• D.Tuning the file size
• E.Z-Ordering

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A CHECK constraint has been successfully added to the Delta table named activity_details using the following logic:

A batch job is attempting to insert new records to the table, including a record where latitude = 45.50 and longitude = 212.67.
Which statement describes the outcome of this batch insert?

• A.The write will fail when the violating record is reached; any records previously processed will be recorded to the target table.
• B.The write will fail completely because of the constraint violation and no records will be inserted into the target table.
• C.The write will insert all records except those that violate the table constraints; the violating records will be recorded to a quarantine table.
• D.The write will include all records in the target table; any violations will be indicated in the boolean column named valid_coordinates.
• E.The write will insert all records except those that violate the table constraints; the violating records will be reported in a warning log.

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A data engineer has written the following query:
1. SELECT *
2. FROM json.`/path/to/json/file.json`;
The data engineer asks a colleague for help to convert this query for use in a Delta Live Tables (DLT)
pipeline. The query should create the first table in the DLT pipeline.
Which of the following describes the change the colleague needs to make to the query?

• A.They need to add the cloud_files(...) wrapper to the JSON file path
• B.They need to add a CREATE DELTA LIVE TABLE table_name AS line at the beginning of the query
• C.They need to add a COMMENT line at the beginning of the query
• D.They need to add a CREATE LIVE TABLE table_name AS line at the beginning of the query
• E.They need to add a live. prefix prior to json. in the FROM line

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