Free Databricks Certified Associate Developer for Apache Spark 3.5 Exam Dumps August 2026

When running in local mode (e.g., local[4]), the number inside the brackets defines how many threads Spark will use.

Using local[*] ensures Spark uses all available CPU cores for parallelism.

Example:

spark-submit --master local[*]

Dynamic allocation and executor memory apply to cluster-based deployments, not local mode.

coalesce(n) reduces the number of partitions without triggering a full shuffle, unlike repartition().

This is ideal when reducing partition count, especially during write operations.

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.

PySpark Structured Streaming Programming Guide --- supported transformations and actions.

Databricks Exam Guide (June 2025): Section ''Structured Streaming'' --- performing operations on streaming DataFrames and understanding supported transformations.

Broadcast joins work by sending the smaller DataFrame to all executors, eliminating the shuffle of the larger DataFrame.

From Spark documentation:

''Broadcast joins are efficient when one DataFrame is small enough to fit in memory. Spark avoids shuffling the larger table.''

DataFrame B (1 GB) fits within the default threshold and should be broadcasted.

It eliminates the need to shuffle the large DataFrame A.

Final Answer: B

To read each file as a single record, use:

spark.read.text(path, wholetext=True)

This ensures that Spark reads the entire file contents into one row.

To exclude sensitive (PII) columns from a DataFrame, the easiest method is to use the .drop() function with the list of column names to remove.

Correct syntax:

df_user_non_pii = df_user.drop('name', 'email', 'birthdate')

This creates a new DataFrame containing all remaining columns.

Why the other options are incorrect:

B: .dropFields() is not valid for standard DataFrames --- it's used for struct fields only.

C: .select() would keep only PII columns, not remove them.

D: .remove() does not exist in Spark DataFrame API.

PySpark DataFrame API --- drop() method for removing multiple columns.

Databricks Exam Guide (June 2025): Section ''Developing Apache Spark DataFrame/DataSet API Applications'' --- data manipulation, selecting, and dropping columns.

Pandas API on Spark (formerly Koalas) offers:

Familiar Pandas-like syntax

Distributed execution using Spark under the hood

Scalability for large datasets across the cluster

It provides the power of Spark while retaining the productivity of Pandas.