LFC Photon Engine Optimization 2

Question 1

Which data format characteristic most benefits Photon's vectorized execution?

Accepted Answer

Columnar storage with contiguous memory layout

Answer

Photon's vectorized execution processes batches of column values stored contiguously in memory, maximizing CPU cache efficiency.

Question 2

What happens to Photon execution when a query encounters a UDF (User Defined Function)?

Accepted Answer

Photon falls back to the JVM-based Spark engine for UDF evaluation

Answer

Photon cannot execute JVM-based UDFs natively and falls back to the standard Spark engine for those operations.

Question 3

In Photon, what is the primary purpose of predicate pushdown when reading Delta tables?

Accepted Answer

To skip reading data files that don't match filter conditions

Answer

Predicate pushdown allows Photon to use Delta Lake's file statistics to skip entire data files that cannot contain matching rows.

Question 4

Which cluster configuration setting directly controls whether Photon is enabled on a Databricks cluster?

Accepted Answer

spark.databricks.photon.enabled

Answer

The spark.databricks.photon.enabled configuration property enables or disables the Photon native execution engine on a cluster.

Question 5

How does Photon handle NULL values differently from traditional Spark execution?

Accepted Answer

Photon uses validity bitmaps to track NULLs separately from data vectors

Answer

Photon maintains a separate validity bitmap alongside each data vector, allowing NULL-aware vectorized operations without branching on every value.

(LFC) Lakehouse Fundamentals Certification Practice Test