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

Comprehensive and Detailed Explanation From Exact Extract:

To flatten an array of structs into individual rows and access fields within each struct, you must:

Useexplode()to expand the array so each struct becomes its own row.

Access the struct fields via dot notation (e.g.,record_exploded.sensor_id).

Option C does exactly that:

First, explode therecordarray column into a new columnrecord_exploded.

Then, access fields of the struct using the dot syntax inselect.

This is standard practice in PySpark for nested data transformation.

Final Answer: C

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 From Exact Extract:

To enable fault tolerance and ensure that Spark can resume from the last committed offset after failure, you must configure a checkpoint location using the correct option key:"checkpointLocation".

From the official Spark Structured Streaming guide:

“To make a streaming query fault-tolerant and recoverable, a checkpoint directory must be specified using.option("checkpointLocation", "/path/to/dir").”

Explanation of options:

Option A uses an invalid option name:"checkpoint"(should be"checkpointLocation")

Option B is correct: it setscheckpointLocationproperly

Option C lacks checkpointing and won’t resume after failure

Option D also lacks checkpointing configuration

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:

When using theMEMORY_AND_DISKstorage level, Spark attempts to cache as much of the DataFrame in memory as possible. If the DataFrame does not fit entirely in memory, Spark will store the remaining partitions on disk. This allows processing to continue, albeit with a performance overhead due to disk I/O.

As per the Spark documentation:

"MEMORY_AND_DISK: It stores partitions that do not fit in memory on disk and keeps the rest in memory. This can be useful when working with datasets that are larger than the available memory."

— Perficient Blogs: Spark - StorageLevel

This behavior ensures that Spark can handle datasets larger than the available memory by spilling excess data to disk, thus preventing job failures due to memory constraints.

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:

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:

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.

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:

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.