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

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.

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.

To ensure that data written out to disk is sorted, it is important to consider how Spark writes data when saving to Parquet tables. The methods .sort() or .orderBy() apply a global sort but do not guarantee that the sorting will persist in the final output files unless certain conditions are met (e.g. a single partition via .coalesce(1) — which is not scalable).

Instead, the proper method in distributed Spark processing to ensure rows are sorted within their respective partitions when written out is:

sortWithinPartitions("column_name")

According to Apache Spark documentation:

"sortWithinPartitions() ensures each partition is sorted by the specified columns. This is useful for downstream systems that require sorted files."

This method works efficiently in distributed settings, avoids the performance bottleneck of global sorting (as in .orderBy() or .sort()), and guarantees each output partition has sorted records — which meets the requirement of consistently sorted data.

Thus:

Option A and B do not guarantee the persisted file contents are sorted.

Option C introduces a bottleneck via .coalesce(1) (single partition).

Option D correctly applies sorting within partitions and is scalable.

In Spark, a shuffle occurs when data needs to be redistributed across partitions or nodes, typically due to operations that require grouping, joining, or sorting.

groupBy triggers a shuffle because it aggregates data based on key values, requiring data from multiple partitions to be moved across the cluster.

Operations like filter and select are narrow transformations (no shuffle).

coalesce can reduce the number of partitions without a full shuffle (unlike repartition).

Why the other options are incorrect:

A (filter) – Narrow transformation, no shuffle.

B (select) – Simple projection, no data movement.

D (coalesce) – Reduces partitions locally without shuffle.

Spark Connect enables remote execution of Spark jobs by decoupling the client from the driver using the Spark Connect protocol (gRPC).

It allows users to run Spark code from different environments (like notebooks, IDEs, or remote clients) while executing jobs on the cluster.

Key Features:

Enables remote interaction between client and Spark driver.

Supports interactive development and lightweight client sessions.

Improves developer productivity without needing driver resources locally.

Why the other options are incorrect:

A: Spark Connect is not limited to ingestion tasks.

B: It allows multi-language clients (Python, Scala, etc.) but runs via Spark Connect API, not arbitrary remote code.

C: Uses gRPC protocol, not REST.

Option B is the correct and idiomatic approach in PySpark to split a string column (like email) based on a delimiter such as "@".

The split(col("email"), "@") function returns an array with two elements: username and domain.

getItem(0) retrieves the first part (username).

getItem(1) retrieves the second part (domain).

withColumn() is used to create new columns from the extracted values.

Example from official Databricks Spark documentation on splitting columns:

from pyspark.sql.functions import split, col

df.withColumn("username", split(col("email"), "@").getItem(0)) \

withColumn("domain", split(col("email"), "@").getItem(1))

⚠️ Why other options are incorrect:

A uses fixed substring indices (substr(0, 5)), which won’t correctly extract usernames and domains of varying lengths.

C uses substring_index, which is available but less idiomatic for splitting emails and is slightly less readable.

D removes "@" from the email entirely, losing the separation between username and domain, and ends up duplicating values in both fields.

Therefore, Option B is the most accurate and reliable solution according to Apache Spark 3.5 best practices.

To read from a specific Kafka topic using Structured Streaming, the correct syntax is:

python

CopyEdit

option("subscribe", "feed")

This is explicitly defined in the Spark documentation:

"subscribe – The Kafka topic to subscribe to. Only one topic can be specified for this option."

(Source: Apache Spark Structured Streaming + Kafka Integration Guide)

B. "subscribe.topic" is invalid.

C. "kafka.topic" is not a recognized option.

D. "topic" is not valid for Kafka source in Spark.

dedup_df = iot_bronze_df.dropDuplicates(["event_ts", "sensor_id", "metric_value"])

dropDuplicates accepts a list of columns to use for deduplication.

This ensures only unique records based on the specified keys are retained.