Databricks DATABRICKS-CERTIFIED-ASSOCIATE-DEVELOPER-FOR-APACHE-SPARK Exam Dumps

Correct Answer: A

In Spark's execution hierarchy, a job may reach over multiple stage boundaries. Correct. A job is a sequence of stages, and thus may reach over multiple stage boundaries. In Spark's execution hierarchy, tasks are one layer above slots. Incorrect. Slots are not a part of the execution hierarchy. Tasks are the lowest layer. In Spark's execution hierarchy, a stage comprises multiple jobs. No. It is the other way around ?a job consists of one or multiple stages. In Spark's execution hierarchy, executors are the smallest unit. False. Executors are not a part of the execution hierarchy. Tasks are the smallest unit! In Spark's execution hierarchy, manifests are one layer above jobs. Wrong. Manifests are not a part of the Spark ecosystem.

Correct Answer: D

Correct code block: from pyspark.sql.functions import udf from pyspark.sql import types as T transactionsDf.createOrReplaceTempView('transactions') def pow_5(x): if x: return x**5 return x spark.udf.register('power_5_udf', pow_5, T.LongType()) spark.sql('SELECT power_5_udf(value) AS result FROM transactions') Here it is important to understand how the pow_5 method handles empty values. In the wrong code block above, the pow_5 method is unable to handle empty values and will throw an error, since Python's ** operator cannot deal with any null value Spark passes into method pow_5. The order of arguments for registering the UDF function with Spark via spark.udf.register matters. In the code snippet in the question, the arguments for the SQL method name and the actual Python function are switched. You can read more about the arguments of spark.udf.register and see some examples of its usage in the documentation (link below). Finally, you should recognize that in the original code block, an expression to rename column created through the UDF function is missing. The renaming is done by SQL's AS result argument. Omitting that argument, you end up with the column name power_5_udf(value) and not result. More info: pyspark.sql.functions.udf -- PySpark 3.1.1 documentation

Correct Answer: C

The driver receives data upon request by actions.

Correct! Actions trigger the distributed execution of tasks on executors which, upon task completion,

transfer result data back to the driver. Actions are Spark's way of exchanging data between executors. No.

In Spark, data is exchanged between executors via shuffles. Writing data to disk is the primary purpose of

actions. No. The primary purpose of actions is to access data that is stored in Spark's RDDs and return the

data, often in aggregated form, back to the driver.

Actions are Spark's way of modifying RDDs.

Incorrect. Firstly, RDDs are immutable ?they cannot be modified. Secondly, Spark generates new RDDs

via transformations and not actions. Stage boundaries are commonly established by actions. Wrong. A

stage boundary is commonly established by a shuffle, for example caused by a wide transformation.