DATABRICKS-SPARK Question 110

Community Discussion

Written by other learners. Comments can disagree with the marked answer — check the official documentation when in doubt.

• Chose A0

  - Correct answer : A — Spark exposes a unified engine where Spark SQL, DataFrames, Structured Streaming, and MLlib share the same execution model, so ETL, reporting, and forecasting run on one cluster. A . This is correct: Spark's unified engine lets the same cluster and APIs handle batch ETL, SQL reporting, and MLlib model training, which is exactly the consolidation the company wants. This eliminates the need to operate three separate specialized systems. B . Spark does not translate jobs into MapReduce plans; it compiles work into its own DAG of stages and tasks executed by its own scheduler. Framing Spark as a MapReduce-compatibility layer misstates how its execution engine works. C . Spark does not enforce schema-on-write, and reading raw files does not remove the need for a query engine to serve reporting workloads. Schema inference and enforcement are configurable, not automatic replacements for a SQL layer. D . Spark itself has no built-in charting dashboard; visualization is handled by separate tools such as notebooks or BI products that connect to Spark. This option describes a capability Spark does not provide. E . Spark does not auto-provision GPU pools when ML code runs; hardware allocation depends on the cluster manager and the cluster configuration chosen by an administrator. This overstates what happens automatically.