Databricks Databricks-Certified-Associate-Developer-for-Apache-Spark-3.5 - Databricks Certified Associate Developer for Apache Spark 3.5-Python

Comprehensive and Detailed Explanation From Exact Extract:

ThegroupBy()operation causes a shuffle because it requires all values for a specific key to be brought together, which may involve moving data across partitions.

In contrast:

filter()andselect()are narrow transformations and do not cause shuffles.

coalesce()tries to reduce the number of partitions and avoids shuffling by moving data to fewer partitions without a full shuffle (unlikerepartition()).

Comprehensive and Detailed Explanation From Exact Extract:

Spark Connect enables decoupling of the client and Spark driver processes, allowing remote access. Spark supports configuring the remote Spark Connect server in multiple ways:

From Databricks and Spark documentation:

Option B (--remote "sc://localhost") is a valid command-line argument for thepysparkshell to connect using Spark Connect.

Option C (settingSPARK_REMOTEenvironment variable) is also a supported method to configure the remote endpoint.

Option A is incorrect because Spark Connect uses thesc://protocol, nothttps:// .

Option D requires modifying the code, which the question explicitly avoids.

Option E configures the port on the server side but doesn’t start a client connection.

Final Answers: B and C

Comprehensive and Detailed Explanation From Exact Extract:

Apache Spark employs a lazy evaluation model for transformations. This means that when transformations (e.g.,map(),filter()) are applied to a DataFrame, Spark does not execute them immediately. Instead, it builds a logical plan (lineage) of transformations to be applied.

Execution is deferred until an action (e.g.,collect(),count(),save()) is called. At that point, Spark's Catalyst optimizer analyzes the logical plan, optimizes it, and then executes the physical plan to produce the result.

This lazy evaluation strategy allows Spark to optimize the execution plan, minimize data shuffling, and improve overall performance by reducing unnecessary computations.

Comprehensive and Detailed Explanation From Exact Extract:

Operations that trigger data movement across partitions (like groupBy, join, repartition) result in a shuffle and a new stage.

From Spark documentation:

“groupBy and aggregation cause data to be shuffled across partitions to combine rows with the same key.”

Option A (groupBy + agg) → causes shuffle.

Options B, C, and D (filter, withColumn, select) → transformations that do not require shuffling; they are narrow dependencies.

Final Answer: A

Comprehensive and Detailed Explanation From Exact Extract:

Adaptive Query Execution (AQE) dynamically selects join strategies based on actual data sizes at runtime. If the size of post-shuffle partitions is below the threshold set by:

spark.sql.adaptive.maxShuffledHashJoinLocalMapThreshold

then Spark prefers to use a shuffled hash join.

From the Spark documentation:

“AQE selects a shuffled hash join when the size of post-shuffle data is small enough to fit within the configured threshold, avoiding more expensive sort-merge joins.”

Therefore:

A is wrong — Cartesian joins are only used with no join condition.

B is correct — this is the optimized join for small partitioned shuffle data under AQE.

C and D are used under other scenarios but not for this case.

Final Answer: B

Comprehensive and Detailed Explanation From Exact Extract:

To enable a Structured Streaming query to recover from failures or intentional shutdowns, it is essential to specify thecheckpointLocationoption during thewriteStreamoperation. This checkpoint location stores the progress information of the streaming query, allowing it to resume from where it left off.

According to the Databricks documentation:

"You must specify thecheckpointLocationoption before you run a streaming query, as in the following example:

option("checkpointLocation", "/path/to/checkpoint/dir")

toTable("catalog.schema.table")

— Databricks Documentation: Structured Streaming checkpoints

By setting thecheckpointLocationduringwriteStream, Spark can maintain state information and ensure exactly-once processing semantics, which are crucial for reliable streaming applications.

Comprehensive and Detailed Explanation From Exact Extract:

The provided code defines a Pandas UDF of type Series-to-Series, where a new instance of the language modelis 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 theget_translation_model()is invoked once per executor process, not per batch, significantly improving pipeline performance.

Comprehensive and Detailed Explanation From Exact Extract:

Spark's best practice is to estimate partition count based on data volume and a reasonable partition size — typically 128 MB to 256 MB per partition.

With 1 TB of data: 1 TB / 128 MB ≈ ~8000 partitions

This ensures that tasks are distributed across available CPUs for parallelism and that each task processes an optimal volume of data.

Option A (equal to cores) may result in partitions that are too large.

Option B (fixed 200) is arbitrary and may underutilize the cluster.

Option C (nodes) gives too few partitions (10), limiting parallelism.

Comprehensive and Detailed Explanation From Exact Extract:

To extract unique values from a column and sort them alphabetically:

distinct()is required to remove duplicate values.

orderBy()is needed to sort the results alphabetically (ascending by default).

Correct code:

df.select("Name").distinct().orderBy(df["Name"])

This is directly aligned with standard DataFrame API usage in PySpark, as documented in the official Databricks Spark APIs. Option A is incorrect because it may not remove duplicates. Option C omits sorting. Option D sorts in descending order, which doesn't meet the requirement for alphabetical (ascending) order.

ThepartitionBy()method in Spark organizes output into subdirectories based on unique combinations of the specified columns:

e.g.

/path/to/output/color=red/fruit=apple/part-0000.parquet

/path/to/output/color=green/fruit=banana/part-0001.parquet

This improves query performance via partition pruning.

It does not consolidate into a single file.

Null values are allowed in partitions.

It does not "append" unless.mode("append")is used.