Databricks Certification Databricks-Certified-Associate-Developer-for-Apache-Spark-3.5 Exam Dumps

Spark supports multiple broadcast joins in a single query plan, as long as each broadcasted DataFrame is small enough to fit under the configured threshold.

Execution Plan:

Spark broadcasts df2 to all executors.

Joins df1 (big) with broadcasted df2.

Then broadcasts df3 and performs another join with the intermediate result.

The result is efficient and avoids shuffling large data.

Why the other options are incorrect:

B: Multiple broadcast joins are supported in Spark 3.x.

C: The join condition is correct since all use id as the key.

D: broadcast() can be used inline; it’s valid syntax.

The SparkSession is the entry point to Spark functionality in modern versions (2.x and later). It unifies the SparkContext, SQLContext, and HiveContext into a single object.

Best Practice:

Use one SparkSession for the entire application.

Create it once at the start using SparkSession.builder.getOrCreate().

Reuse it across all transformations and actions.

Stop it only after all operations are completed.

Example:

from pyspark.sql import SparkSession

spark = SparkSession.builder.appName("MyApp").getOrCreate()

# Perform transformations and actions

spark.stop()

Why the other options are incorrect:

B: SparkSession is the recommended interface; SparkContext alone is deprecated for SQL/DataFrame APIs.

C: Creating multiple sessions increases overhead and wastes resources.

D: Restarting SparkSession breaks lineage and adds unnecessary startup costs.

To handle JSON files with evolving or differing schemas, Spark SQL supports the option mergeSchema 'true', which merges all fields across files into a unified schema.

Correct syntax:

CREATE EXTERNAL TABLE users

USING json

OPTIONS (path '/data/input.json', mergeSchema 'true');

This creates an external table directly on the JSON data, inferring schema automatically and merging variations.

Why the other options are incorrect:

B: Missing schema merge configuration — fails with inconsistent files.

C: inferSchema applies to CSV/other file types, not JSON.

D: mergeAll is not a valid Spark SQL option.

In Structured Streaming, only transformation operations are allowed on streaming DataFrames. These include select(), filter(), where(), groupBy(), withColumn(), etc.

Example of supported transformation:

filtered_df = streaming_df.filter("count < 30")

However, actions such as count(), show(), and collect() are not supported directly on streaming DataFrames because streaming queries are unbounded and never finish until stopped.

To perform aggregations, the query must be executed through writeStream and an output sink.

Why the other options are incorrect:

A: count() is an action, not allowed directly on streaming DataFrames.

C: countDistinct() is a stateful aggregation, not supported outside of a proper streaming query.

D: show() is also an action, unsupported on streaming queries.