Databricks-Certified-Associate-Developer-for-Apache-Spark-3.5 Exam Dumps : Databricks Certified Associate Developer for Apache Spark 3.5 – Python

To view the schema of a Parquet file, you must use the DataFrameReader to load the Parquet data and call the .printSchema() method.

Correct syntax:

spark.read.parquet("events.parquet").printSchema()

This command loads the file metadata (without triggering a full read) and prints the column names, data types, and nullability information in a tree format.

Why the other options are incorrect:

A/D: SQL queries can’t directly introspect file schemas.

B: .show() displays data rows, not schema.

Adaptive Query Execution (AQE) in Spark 3.x automatically optimizes query plans at runtime based on the actual data characteristics observed during job execution.

Key features of AQE include:

Dynamic switching of join strategies: Changes between sort-merge join and broadcast join based on actual shuffle sizes.

Coalescing shuffle partitions: Reduces small tasks and improves parallelism efficiency.

Handling skew joins: Dynamically splits large partitions to avoid data skew.

Thus, the most accurate answer describing AQE’s function is “dynamically switching join strategies.”

Why the other options are incorrect:

B: Table statistics are collected manually or by the metastore, not by AQE.

C: AQE benefits multi-stage jobs involving shuffles, not single-stage jobs.

D: Delta file optimization is handled by Databricks utilities, not AQE.

The correct way to overwrite an existing file using the DataFrameWriter is:

df.write.mode("overwrite").json("path/to/file")

Option D is also technically valid, but Option A is the most concise and idiomatic PySpark syntax.