Pass the Databricks Databricks Certification Databricks-Certified-Associate-Developer-for-Apache-Spark-3.5 Questions and answers with CertsForce

Structured Streaming supports three output modes:

Append: Writes only new rows since the last trigger.

Update: Writes only updated rows.

Complete: Writes the entire result table after every trigger execution.

For aggregations like groupBy().count(), only complete mode outputs the entire table each time.

Example:

aggDF.writeStream \

outputMode("complete") \

format("console") \

start()

Why the other options are incorrect:

A: “AGGREGATE” is not a valid output mode.

C: “REPLACE” does not exist.

D: “APPEND” writes only new rows, not the full table.

Spark Connect is a client-server architecture introduced in Apache Spark 3.4, designed to decouple the client from the Spark driver, enabling remote connectivity to Spark clusters.

According to the Spark 3.5.5 documentation:

"Majority of the Streaming API is supported, including DataStreamReader, DataStreamWriter, StreamingQuery and StreamingQueryListener."

This indicates that Spark Connect supports key components of Structured Streaming, allowing for robust streaming data processing capabilities.

Regarding other options:

B. While Spark Connect supports DataFrame, Functions, and Column APIs, it does not support SparkContext and RDD APIs.

C. Spark Connect supports multiple languages, including PySpark and Scala, not just PySpark.

D. Spark Connect does not have built-in authentication but is designed to work seamlessly with existing authentication infrastructures.

The Spark History Server provides a web UI for viewing past completed applications, including event logs, stages, and performance metrics.

If disabled:

Spark jobs still run normally,

But users lose the ability to review historical job metrics, DAGs, or logs after completion.

Thus, debugging, performance analysis, and audit capabilities are lost.

Why the other options are incorrect:

A: Disabling History Server doesn’t manage logs.

B/D: Minimal overhead; disabling doesn’t improve runtime speed or executor performance.

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.

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.

Underutilization with timeout warnings often indicates insufficient parallelism — meaning there aren’t enough executors to process all tasks concurrently.

Solution:

Increase the number of executors to allow more parallel task execution and better resource utilization.

Example configuration:

--conf spark.executor.instances=8

This distributes the workload more effectively across cluster nodes and reduces idle time for pending tasks.

Why the other options are incorrect:

A: Extending timeouts hides the symptom, not the root cause (lack of executors).

B: More memory per executor won’t fix scheduling bottlenecks.

C: Reducing partition size may increase overhead and does not fix resource imbalance.

In Apache Spark, the execution hierarchy is structured as follows:

Application: The highest-level unit, representing the user program built on Spark.

Job: Triggered by an action (e.g., collect(), count()). Each action corresponds to a job.

Stage: A job is divided into stages based on shuffle boundaries. Each stage contains tasks that can be executed in parallel.

Task: The smallest unit of work, representing a single operation applied to a partition of the data.

When the collect() action is invoked, Spark initiates a job. This job is then divided into stages at points where data shuffling is required (i.e., wide transformations). Each stage comprises tasks that are distributed across the cluster's executors, operating on individual data partitions.

This hierarchical execution model allows Spark to efficiently process large-scale data by parallelizing tasks and optimizing resource utilization.

Pandas API on Spark provides a distributed implementation of the Pandas DataFrame API on top of Apache Spark.

Advantages:

Executes transformations in parallel across all nodes and cores in the cluster.

Maintains Pandas-like syntax, making it easy for Python users to transition.

Enables scaling of existing Pandas code to handle large datasets without memory limits.

Therefore, it combines Pandas usability with Spark’s distributed power, offering both speed and scalability.

Why the other options are incorrect:

B: While it uses Python, that’s not its main advantage.

C: It runs distributed across the cluster, not on a single node.

D: Pandas API on Spark uses lazy evaluation, not eager computation.

The provided code defines a Pandas UDF of type Series-to-Series, where a new instance of the language model is created on each call, which happens per batch. This is inefficient and results in significant overhead due to repeated model initialization.

To reduce the frequency of model loading, the engineer should convert the UDF to an iterator-based Pandas UDF (Iterator[pd.Series] -> Iterator[pd.Series]). This allows the model to be loaded once per executor and reused across multiple batches, rather than once per call.

From the official Databricks documentation:

“Iterator of Series to Iterator of Series UDFs are useful when the UDF initialization is expensive… For example, loading a ML model once per executor rather than once per row/batch.”

— Databricks Official Docs: Pandas UDFs

Correct implementation looks like:

python

CopyEdit

@pandas_udf("string")

def translate_udf(batch_iter: Iterator[pd.Series]) -> Iterator[pd.Series]:

model = get_translation_model(target_lang='es')

for batch in batch_iter:

yield batch.apply(model)

This refactor ensures the get_translation_model() is invoked once per executor process, not per batch, significantly improving pipeline performance.

Adaptive Query Execution (AQE) is a powerful optimization framework introduced in Apache Spark 3.0 and enabled by default since Spark 3.2. It dynamically adjusts query execution plans based on runtime statistics, leading to significant performance improvements. The key benefits of AQE include:

Dynamic Join Strategy Selection: AQE can switch join strategies at runtime. For instance, it can convert a sort-merge join to a broadcast hash join if it detects that one side of the join is small enough to be broadcasted, thus optimizing the join operation .

Handling Skewed Data: AQE detects skewed partitions during join operations and splits them into smaller partitions. This approach balances the workload across tasks, preventing scenarios where certain tasks take significantly longer due to data skew .

Coalescing Post-Shuffle Partitions: AQE dynamically coalesces small shuffle partitions into larger ones based on the actual data size, reducing the overhead of managing numerous small tasks and improving overall query performance .

These runtime optimizations allow Spark to adapt to the actual data characteristics during query execution, leading to more efficient resource utilization and faster query processing times.