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Databricks Associate-Developer-Apache-Spark-3.5 Exam Syllabus Topics:

Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions:

1. A data engineer is working on a real-time analytics pipeline using Apache Spark Structured Streaming. The engineer wants to process incoming data and ensure that triggers control when the query is executed. The system needs to process data in micro-batches with a fixed interval of 5 seconds.
Which code snippet the data engineer could use to fulfil this requirement?
A)

B)

C)

D)

Options:

A) Uses trigger() - default micro-batch trigger without interval.
B) Uses trigger(continuous='5 seconds') - continuous processing mode.
C) Uses trigger(processingTime='5 seconds') - correct micro-batch trigger with interval.
D) Uses trigger(processingTime=5000) - invalid, as processingTime expects a string.

2. Given this view definition:
df.createOrReplaceTempView("users_vw")
Which approach can be used to query the users_vw view after the session is terminated?
Options:

A) Query the users_vw using Spark
B) Save the users_vw definition and query using Spark
C) Persist the users_vw data as a table
D) Recreate the users_vw and query the data using Spark

3. A Spark DataFrame df is cached using the MEMORY_AND_DISK storage level, but the DataFrame is too large to fit entirely in memory.
What is the likely behavior when Spark runs out of memory to store the DataFrame?

A) Spark duplicates the DataFrame in both memory and disk. If it doesn't fit in memory, the DataFrame is stored and retrieved from the disk entirely.
B) Spark splits the DataFrame evenly between memory and disk, ensuring balanced storage utilization.
C) Spark stores the frequently accessed rows in memory and less frequently accessed rows on disk, utilizing both resources to offer balanced performance.
D) Spark will store as much data as possible in memory and spill the rest to disk when memory is full, continuing processing with performance overhead.

4. In the code block below, aggDF contains aggregations on a streaming DataFrame:

Which output mode at line 3 ensures that the entire result table is written to the console during each trigger execution?

A) append
B) replace
C) complete
D) aggregate

5. Which UDF implementation calculates the length of strings in a Spark DataFrame?

A) spark.udf.register("stringLength", lambda s: len(s))
B) df.withColumn("length", udf(lambda s: len(s), StringType()))
C) df.select(length(col("stringColumn")).alias("length"))
D) df.withColumn("length", spark.udf("len", StringType()))

Solutions: