You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a Delta table named Orders.
You load the Orders table into an Apache Spark DataFrame named df.
You need to create a DataFrame that excludes rows where the order amount is null.
Solution: You run the following expression.
df.filter(df.order_amount.isNotNull())
Does this meet the goal?
Correct Answer: A
The correct answer is A — Yes.
df.filter(df.order_amount.isNotNull()) is the correct PySpark pattern for excluding null rows. The isNotNull() method is a Column method that returns True for every row where order_amount has a value and False for rows where it is null. Spark’s filter keeps only the rows where the condition evaluates to True, producing a DataFrame with all null order_amount rows removed.
This works correctly because isNotNull() is explicitly null-aware — unlike the != None comparison in Q52, it doesn’t rely on Python equality semantics. Under the hood it maps to the SQL expression order_amount IS NOT NULL, which is unambiguous in both SQL and Spark.
Both df.filter(df.order_amount.isNotNull()) and df.dropna(subset=[‘order_amount’]) produce identical results. The choice between them is stylistic — isNotNull() reads more explicitly as a filter condition, while dropna is more compact when handling multiple columns.
You have an Azure Databricks workspace that uses serverless compute.
You need to ingest data by using Lakeflow Jobs. New records must be processed as soon as they become available.
Which type of job trigger should you use for the ingestion?
Correct Answer: D
According to Microsoft Learn on Lakeflow Jobs triggers, the Continuous trigger keeps the job running perpetually and immediately processes new records as they become available — this directly satisfies ‘New records must be processed as soon as they become available.’ The Continuous trigger is specifically designed for near-real-time ingestion scenarios where latency must be minimized. Option A (Manual) requires a human to start each run, adding latency. Option B (File Arrival) triggers a job when new files land in a storage location, which is appropriate for file-based ingestion but introduces per-file trigger overhead rather than true continuous processing. Option C (Scheduled) runs at fixed intervals (e.g., hourly), introducing batch latency that is inconsistent with processing records ‘as soon as they become available.’
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