Databricks-Certified-Associate-Developer-for-Apache-Spark-3.0 Exam Dumps : Databricks Certified Associate Developer for Apache Spark 3.0 Exam

Explanation

The key to solving this QUESTION NO: is knowing (or reading in the documentation) that, by default, cache() stores values to memory and writes any partitions for which there is insufficient memory

to disk. persist() can achieve the exact same behavior, however not with the StorageLevel.MEMORY_ONLY option listed here. It is also worth noting that cache() does not have any arguments.

If you have troubles finding the storage level information in the documentation, please also see this student Q&A thread that sheds some light here.

Static notebook | Dynamic notebook: See test 2, QUESTION NO: 30 (Databricks import instructions)

Explanation

Correct code block:

transactionsDf.filter(col('predError').isin([3, 6])).count()

The isin method is the correct one to use here – the in method does not exist for the Column object.

More info: pyspark.sql.Column.isin — PySpark 3.1.2 documentation

Explanation

DAG stands for "Directing Acyclic Graph".

No, DAG stands for "Directed Acyclic Graph".

Spark strategically hides DAGs from developers, since the high degree of automation in Spark means that developers never need to consider DAG layouts.

No, quite the opposite. You can access DAGs through the Spark UI and they can be of great help when optimizing queries manually.

In contrast to transformations, DAGs are never lazily executed.

DAGs represent the execution plan in Spark and as such are lazily executed when the driver requests the data processed in the DAG.