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

In Spark SQL and DataFrame operations, the configuration parameter spark.sql.shuffle.partitions defines the number of partitions created during shuffle operations such as join, groupBy, and distinct.

The default value (in Spark 3.5) is 200.

If this number is too low, Spark creates fewer tasks, leading to idle executors and poor cluster utilization.

Increasing this value allows Spark to create more tasks that can run in parallel across executors, effectively using more cluster resources.

Correct approach:

spark.conf.set("spark.sql.shuffle.partitions", 400)

This increases the parallelism level of shuffle stages and improves overall resource utilization.

Why the other options are incorrect:

B: Reducing partitions further would decrease parallelism and worsen the underutilization issue.

C: Dynamic resource allocation scales executors up or down based on workload, but it doesn’t fix low task parallelism caused by insufficient shuffle partitions.

D: Increasing dataset size is not a tuning solution and doesn’t address task-level under-parallelization.

References (Databricks Apache Spark 3.5 – Python / Study Guide):

Spark SQL Configuration: spark.sql.shuffle.partitions — controls the number of shuffle partitions.

Databricks Exam Guide (June 2025): Section “Troubleshooting and Tuning Apache Spark DataFrame API Applications” — tuning strategies, partitioning, and optimizing cluster utilization.

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The correct approach to perform a parallelized groupBy operation across Spark worker nodes using Pandas API is via applyInPandas. This function enables grouped map operations using Pandas logic in a distributed Spark environment. It applies a user-defined function to each group of data represented as a Pandas DataFrame.

As per the Databricks documentation:

"applyInPandas() allows for vectorized operations on grouped data in Spark. It applies a user-defined function to each group of a DataFrame and outputs a new DataFrame. This is the recommended approach for using Pandas logic across grouped data with parallel execution."

Option A is correct and achieves this parallel execution.

Option B (mapInPandas) applies to the entire DataFrame, not grouped operations.

Option C uses built-in aggregation functions, which are efficient but not customizable with Pandas logic.

Option D creates a scalar Pandas UDF which does not perform a group-wise transformation.

Therefore, to run a groupBy with parallel Pandas logic on Spark workers, Option A using applyInPandas is the only correct answer.

Spark uses lazy evaluation. Transformations like filter, select, and groupBy only define the DAG (Directed Acyclic Graph). No execution occurs until an action is triggered.

The first action in the code is:reduced_df.count()

So Spark starts processing data at this line.

Pandas API on Spark (pyspark.pandas) allows interoperability with PySpark DataFrames. To convert a pyspark.pandas.DataFrame to a standard PySpark DataFrame, you use .to_spark().

Example:

df = psdf.to_spark()

This is the officially supported method as per Databricks Documentation.

Incorrect options:

B, D: Invalid or nonexistent methods.

C: Converts to a local pandas DataFrame, not a PySpark DataFrame.

RDD actions like reduce() aggregate values across all partitions and return the result to the driver.

To compute the maximum processing time, reduce() is ideal because it combines results from all tasks efficiently.

Example:

max_time = rdd_times.reduce(lambda x, y: max(x, y))

This aggregates maximum values from all executors into a single result on the driver.

Why the other options are incorrect:

A: Broadcast variables distribute read-only data; they cannot aggregate results.

B: Spark UI provides visualization, not programmatic collection.

D: Accumulators support additive operations only (e.g., counters, sums), not non-associative ones like max.

When writing DataFrames to files using the Spark DataFrameWriter API, Spark by default raises an error if the target path already exists. To explicitly overwrite existing data, you must specify the write mode as "overwrite".

Correct Syntax:

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

This command removes the existing file or directory at the specified path and writes the new output in JSON format.

Other supported save modes include:

"append" — Adds new data to existing files.

"ignore" — Skips writing if the path already exists.

"error" or "errorifexists" — Fails the job if the output path exists (default).

Why other options are incorrect:

A: Defaults to "error" mode, which fails if the path exists.

B: "append" only adds data; it does not overwrite existing data.

C: .option("overwrite") is invalid — mode("overwrite") must be used instead.

References (Databricks Apache Spark 3.5 – Python / Study Guide):

PySpark API Reference: DataFrameWriter.mode() — describes valid write modes including "overwrite".

PySpark API Reference: DataFrameWriter.json() — method to write DataFrames in JSON format.

Databricks Certified Associate Developer for Apache Spark Exam Guide (June 2025): Section “Using Spark DataFrame APIs” — Reading and writing DataFrames using save modes, schema management, and partitioning.

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Spark Connect introduces a decoupled client-server architecture. Its key feature is enabling Spark job submission and execution from remote clients — in Python, Java, etc.

From Databricks documentation:

“Spark Connect allows remote clients to connect to a Spark cluster and execute Spark jobs without being co-located with the Spark driver.”

A is close, but "any language" is overstated (currently supports Python, Java, etc., not literally all).

B refers to REST, which is not Spark Connect's mechanism.

D is incorrect; Spark Connect isn’t focused on ingestion.

Final Answer: C

The most efficient and correct approach is to define a schema using StructType and pass it to spark.read.schema(...).

This avoids schema inference overhead and ensures proper data types are enforced during read.

Example:

from pyspark.sql.types import StructType, StructField, StringType, DoubleType

schema = StructType([

StructField("order_id", StringType(), True),

StructField("amount", DoubleType(), True),

])

df = spark.read.schema(schema).json("path/to/json")

— Source: Databricks Guide – Read JSON with predefined schema

The method dropDuplicatesWithinWatermark() in Structured Streaming drops duplicate records based on a specified column and watermark window. The watermark defines the threshold for how late data is considered valid.

From the Spark documentation:

"dropDuplicatesWithinWatermark removes duplicates that occur within the event-time watermark window."

In this case, Spark will retain the first occurrence and drop subsequent records within the 30-minute watermark window.

Final Answer: B

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.