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

Pandas API on Spark (formerly Koalas) offers:

Familiar Pandas-like syntax

Distributed execution using Spark under the hood

Scalability for large datasets across the cluster

It provides the power of Spark while retaining the productivity of Pandas.

Comprehensive and Detailed Explanation From Exact Extract:

ThesetLogLevel()method ofSparkContextsets the logging level on the driver, which controls the verbosity of logs emitted during job execution. Supported levels are inherited from log4j and include the following:

ALL

DEBUG

ERROR

FATAL

INFO

OFF

TRACE

WARN

According to official Spark and Databricks documentation:

"Valid log levels include: ALL, DEBUG, ERROR, FATAL, INFO, OFF, TRACE, and WARN."

Among the choices provided, only option B (ERROR, WARN, TRACE, OFF) includes four valid log levels and excludes invalid ones like "FAIL" or "NONE".

Comprehensive and Detailed Explanation From Exact Extract:

The correct answer isBbecause it uses the new function count_if, introduced in Spark 3.5.0, which simplifies conditional counting within aggregations.

F.count_if(condition) counts the number of rows that meet the specified boolean condition.

In this example, it directly counts how many times spot_price >= min_price evaluates to true, replacing the older verbose combination of when/otherwise and filtering or summing.

Official Spark 3.5.0 documentation notes the addition of count_if to simplify this kind of logic:

“Added count_if aggregate function to count only the rows where a boolean condition holds (SPARK-43773).”

Why other options are incorrect or outdated:

Auses a legacy-style method of adding a flag column (when().otherwise()), which is verbose compared to count_if.

Cperforms a simple min/max aggregation—useful but unrelated to conditional array operations or the updated functionality.

Dincorrectly applies .filter() after .agg() which will cause an error, and misuses string "min_price" rather than the variable.

Therefore,Bis the only option leveraging new functionality from Spark 3.5.0 correctly and efficiently.

Comprehensive and Detailed Explanation From Exact Extract:

The message"GC overhead limit exceeded"typically indicates that the JVM is spending too much time in garbage collection with little memory recovery. This suggests that the driver or executor is under-provisioned in memory.

The most effective remedy is to increase the driver memory using:

--driver-memory 4g

This is confirmed in Spark's official troubleshooting documentation:

“If you see a lot ofGC overhead limit exceedederrors in the driver logs, it’s a sign that the driver is running out of memory.”

—Spark Tuning Guide

Why others are incorrect:

Amay help but does not directly address the driver memory shortage.

Bis not a valid action; GC cannot be disabled.

Dincreases memory usage, worsening the problem.

Comprehensive and Detailed Explanation From Exact Extract:

The correct way to load specific columns from an ORC file is to first load the file using.load()and then apply.select()on the resulting DataFrame. This is valid with.read.format("orc")or the shortcut.read.orc().

df = spark.read.format("orc").load("/file/test_data.orc").select("col1","col2")

Why others are incorrect:

Aperforms selection after filtering, but doesn’t match the intention to minimize memory at load.

Bincorrectly tries to use.select()before.load(), which is invalid.

Cuses a non-existent.selected()method.

Dcorrectly loads and then selects.

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:

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.

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.

Comprehensive and Detailed Explanation:

Exactly-once guarantees in Spark Structured Streaming require micro-batch mode (default), not continuous mode.

Continuous mode (.trigger(continuous=...)) only supports at-least-once semantics and lacks full fault-tolerance.

trigger(availableNow=True)is a batch-style trigger, not suited for low-latency streaming.

So:

Option A uses micro-batching with a tight trigger interval → minimal latency + exactly-once guarantee.

Final Answer: A

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

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 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.

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 functiondate_sub(start, days)subtracts the number of days from the start date. If a negative number is passed, the behavior becomes a date addition.

Example:

SELECT date_sub('2024-05-01', -5)

-- Returns: 2024-05-06

So, a negative value effectively adds the absolute number of days to the date.

Which operation is supported with streaming_df?

A. streaming_df.select(countDistinct("Name"))

B. streaming_df.groupby("Id").count()

C. streaming_df.orderBy("timestamp").limit(4)

D. streaming_df.filter(col("count") < 30).show()

Answer: B

Comprehensive and Detailed Explanation:

In Structured Streaming, only a limited subset of operations is supported due to the nature of unbounded data. Operations like sorting (orderBy) and global aggregation (countDistinct) require a full view of the dataset, which is not possible with streaming data unless specific watermarks or windows are defined.

Review of Each Option:

A. select(countDistinct("Name"))

Not allowed — Global aggregation like countDistinct() requires the full dataset and is not supported directly in streaming without watermark and windowing logic.

Comprehensive and Detailed Explanation From Exact Extract:

When you convert a largepyspark.pandas(aka Pandas API on Spark) DataFrame to a local Pandas DataFrame using.toPandas(), Spark collects all partitions to the driver.

From the Spark documentation:

“Be careful when converting large datasets to Pandas. The entire dataset will be pulled into the driver’s memory.”

Thus, for large datasets, this can cause memory overflow or out-of-memory errors on the driver.

Final Answer: D

Comprehensive and Detailed Explanation From Exact Extract:

To sort a PySpark DataFrame by multiple columns with mixed sort directions, the correct usage is:

python

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df.orderBy("age","salary", ascending=[True,False])

agewill be sorted in ascending order

salarywill be sorted in descending order

TheorderBy()andsort()methods in PySpark accept a list of booleans to specify the sort direction for each column.

Documentation Reference:PySpark API - DataFrame.orderBy

Comprehensive and Detailed Explanation From Exact Extract:

df.cache()is shorthand fordf.persist(StorageLevel.MEMORY_AND_DISK)

df.persist()allows specifying any storage level such asMEMORY_ONLY,DISK_ONLY,MEMORY_AND_DISK_SER, etc.

By default,persist()usesMEMORY_AND_DISK, unless specified otherwise.

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.

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

Comprehensive and Detailed Explanation:

To remove specific columns from a PySpark DataFrame, the drop() method is used. This method returns a new DataFrame without the specified columns. The correct syntax for dropping multiple columns is to pass each column name as a separate argument to the drop() method.

Correct Usage:

df_user_non_pii = df_user.drop("first_name", "last_name", "email", "birthdate")

This line of code will return a new DataFrame df_user_non_pii that excludes the specified PII columns.

Explanation of Options:

A.Correct. Uses the drop() method with multiple column names passed as separate arguments, which is the standard and correct usage in PySpark.

B.Although it appears similar to Option A, if the column names are not enclosed in quotes or if there's a syntax error (e.g., missing quotes or incorrect variable names), it would result in an error. However, as written, it's identical to Option A and thus also correct.

C.Incorrect. The dropfields() method is not a method of the DataFrame class in PySpark. It's used with StructType columns to drop fields from nested structures, not top-level DataFrame columns.

D.Incorrect. Passing a single string with comma-separated column names to dropfields() is not valid syntax in PySpark.

Comprehensive and Detailed Explanation From Exact Extract:

To write a structured streaming DataFrame to Parquet files, the correct way to specify the format and output directory is:

writeStream

format("parquet")

option("path", "path/to/destination/dir")

According to Spark documentation:

“When writing to file-based sinks (like Parquet), you must specify the path using the .option("path", ...) method. Unlike batch writes, .save() is not supported.”

Option A incorrectly uses.option("location", ...)(invalid for Parquet sink).

Option B incorrectly sets the format via.option("format", ...), which is not the correct method.

Option C repeats the same issue.

Option D is correct:.format("parquet")+.option("path", ...)is the required syntax.

Final Answer: D

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 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

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