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

Comprehensive and Detailed Explanation:

Adaptive Query Execution (AQE) is a Spark 3.x feature that dynamically optimizes query plans at runtime. One of its core features is:

Dynamically switching join strategies (e.g., from sort-merge to broadcast) based on runtime statistics.

Other AQE capabilities include:

Coalescing shuffle partitions

Skew join handling

Option A is correct.

Option B refers to statistics collection, which is not AQE's primary function.

Option C is too broad and not AQE-specific.

Option D refers to Delta Lake optimizations, unrelated to AQE.

Final Answer: A

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.

Comprehensive and Detailed Explanation From Exact Extract:

The correct way to aggregate information (e.g., max value) from distributed workers back to the driver is using RDD actions such asreduce()oraggregate().

From the documentation:

“To perform global aggregations on distributed data, actions likereduce()are commonly used to collect summaries such as min/max/avg.”

Accumulators (Option B) do not support max operations directly and are not intended for such analytics.

Broadcast (Option C) is used to send data to workers, not collect from them.

Spark UI (Option D) is a monitoring tool — not an analytics collection interface.

Final Answer: A

Comprehensive Explanation:

To cover structured data processing, SQL querying, and machine learning in Apache Spark, the correct combination of components is:

Spark DataFrames: for structured data processing

Spark SQL: to execute SQL queries over structured data

MLlib: Spark's scalable machine learning library

This trio is designed for exactly this type of use case.

Why other options are incorrect:

A: GraphX is for graph processing — not needed here.

B: Pandas API on Spark is useful, but MLlib is essential for ML, which this option omits.

C: Spark Streaming is legacy; GraphX is irrelevant here.

To persist a table and specify the save path, use:

users.write.option("path","/some/path").saveAsTable("default_table")

The .option("path", ...) must be applied before calling saveAsTable.

Option A uses invalid syntax (write(path=...)).

Option B applies.option()after.saveAsTable()—which is too late.

Option D uses incorrect syntax (no path parameter in saveAsTable).

Comprehensive and Detailed Explanation From Exact Extract:

To filter rows based on a condition and display them in Spark, usefilter(...).show():

employees_df.filter(employees_df.tenure >= 5).show()

Option A is correct and shows the results.

Option B filters but doesn’t display them.

Option C uses Python’s built-infilter, not Spark.

Option D collects the results to the driver, which is unnecessary if.show()is sufficient.

Final Answer: A

Comprehensive and Detailed Explanation From Exact Extract:

The default broadcast join threshold in Spark is:

spark.sql.autoBroadcastJoinThreshold = 10MB

Sincedf2is only 8 MB (less than 10 MB), Spark will automatically apply a broadcast join without requiring explicit hints.

From the Spark documentation:

“If one side of the join is smaller than the broadcast threshold, Spark will automatically broadcast it to all executors.”

A is incorrect because Spark does support auto broadcast even with static plans.

B is correct: Spark will automatically broadcast df2.

C and D are incorrect because Spark's default logic handles this optimization.

Final Answer: B

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

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 --masterlocal[*]

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

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:

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

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@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.

dropDuplicates() with no column list removes duplicates based on all columns.

It's the most efficient and semantically correct way to deduplicate records that are completely identical across all fields.

From the PySpark documentation:

dropDuplicates(): Return a new DataFrame with duplicate rows removed, considering all columns if none are specified.

— Source:PySpark DataFrame.dropDuplicates() API

spark.executor.cores determines how many concurrent tasks an executor can run.

For example, if set to 4, each executor can run up to 4 tasks in parallel.

Other settings:

spark.task.maxFailures controls task retry logic.

spark.driver.cores is for the driver, not executors.

spark.executor.memory sets memory limits, not task concurrency.

Comprehensive and Detailed Explanation From Exact Extract:

In Spark, when a CSV row does not match the provided schema, Spark does not raise an error by default. Instead, it returnsnullfor fields that cannot be parsed correctly.

In the first row,"hello"cannot be cast to Integer for theagefield → Spark setsage=None

In the second row,"20"is a valid integer →age=20

So the output will be:

[Row(name='bambi', age=None), Row(name='alladin', age=20)]

Final Answer: C

Comprehensive and Detailed Explanation From Exact Extract:

The correct answer isDbecause it uses proper time-based window aggregation along with watermarking, which is the required pattern in Spark Structured Streaming for time-based aggregations over event-time data.

From the Spark 3.5 documentation on structured streaming:

"You can define sliding windows on event-time columns, and usegroupByalong withwindow()to compute aggregates over those windows. To deal with late data, you usewithWatermark()to specify how late data is allowed to arrive."

(Source:Structured Streaming Programming Guide)

In optionD, the use of:

python

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groupBy("sensor_id", window("timestamp","5 minutes"))

agg(avg("temperature").alias("avg_temp"))

ensures that for eachsensor_id, the average temperature is calculated over 5-minute event-time windows. To complete the logic, it is assumed thatwithWatermark("timestamp", "5 minutes")is used earlier in the pipeline to handle late events.

Explanation of why other options are incorrect:

Option AusesWindow.partitionBywhich applies to static DataFrames or batch queries and is not suitable for streaming aggregations.

Option Bdoes not apply a time window, thus does not compute the rolling average over 5 minutes.

Option Cincorrectly applieswithWatermark()after an aggregation and does not include any time window, thus missing the time-based grouping required.

Therefore,Option Dis the only one that meets all requirements for computing a time-windowed streaming aggregation.

To define a micro-batch interval, the correct syntax is:

query = df.writeStream \

outputMode("append") \

trigger(processingTime='5 seconds') \

start()

This schedules the query to execute every 5 seconds.

Continuous mode (used in Option A) is experimental and has limited sink support.

Option D is incorrect because processingTime must be a string (not an integer).

Option B triggers as fast as possible without interval control.

Comprehensive and Detailed Explanation From Exact Extract:

The question requires creating a dictionary where keys areregionvalues and values are the correspondingregion_idintegers. Furthermore, it asks to retrieve only the smallest 3region_idvalues.

Key observations:

select('region', 'region_id')puts the column order as expected bydict()— where the first column becomes the key and the second the value.

sort('region_id')ensures sorting in ascending order so the smallest IDs are first.

take(3)retrieves exactly 3 rows.

Wrapping the result indict(...)correctly builds the required Python dictionary:{ 'AFRICA': 0, 'AMERICA': 1, 'ASIA': 2 }.

Incorrect options:

Option B flips the order toregion_idfirst, resulting in a dictionary with integer keys — not what's asked.

Option C uses.limit(3)without sorting, which leads to non-deterministic rows based on partition layout.

Option D sorts in descending order, giving the largest rather than smallestregion_ids.

Hence, Option A meets all the requirements precisely.

f.to_date() converts a timestamp or string to a DateType.

Ideal for extracting the date component (year-month-day) from a full timestamp.

Example:

frompyspark.sql.functionsimportto_date

dates_df.withColumn("date", to_date("timestamp"))

Comprehensive and Detailed Explanation From Exact Extract:

Shuffling occurs in operations likegroupBy,reduceByKey, orjoin—which cause data to be moved across partitions. Therepartition()operation can also cause a shuffle, but in this context, it follows an aggregation.

InOption D, thegroupByfollowed byaggresults in a shuffle due to grouping across nodes.

Therepartition(10)is a partitioning transformation but does not involve a new shuffle since the data is already grouped.

This sequence — shuffle (groupBy) followed by non-shuffling (repartition) — is correct.

Option A does the opposite: thefilterdoes not cause a shuffle, butgroupBydoes — this makes it the wrong order.

Comprehensive and Detailed Explanation:

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