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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q33-Q38):

NEW QUESTION # 33
A data engineer has been asked to produce a Parquet table which is overwritten every day with the latest data.
The downstream consumer of this Parquet table has a hard requirement that the data in this table is produced with all records sorted by themarket_timefield.
Which line of Spark code will produce a Parquet table that meets these requirements?

A. final_df
.sort("market_time")
.coalesce(1)
.write
.format("parquet")
.mode("overwrite")
.saveAsTable("output.market_events")
B. final_df
.sortWithinPartitions("market_time")
.write
.format("parquet")
.mode("overwrite")
.saveAsTable("output.market_events")
C. final_df
.sort("market_time")
.write
.format("parquet")
.mode("overwrite")
.saveAsTable("output.market_events")
D. final_df
.orderBy("market_time")
.write
.format("parquet")
.mode("overwrite")
.saveAsTable("output.market_events")

Answer: B

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
To ensure that data written out to disk is sorted, it is important to consider how Spark writes data when saving to Parquet tables. The methods.sort()or.orderBy()apply a global sort but do not guarantee that the sorting will persist in the final output files unless certain conditions are met (e.g. a single partition via.coalesce(1)- which is not scalable).
Instead, the proper method in distributed Spark processing to ensure rows are sorted within their respective partitions when written out is:
sortWithinPartitions("column_name")
According to Apache Spark documentation:
"sortWithinPartitions()ensures each partition is sorted by the specified columns. This is useful for downstream systems that require sorted files." This method works efficiently in distributed settings, avoids the performance bottleneck of global sorting (as in.orderBy()or.sort()), and guarantees each output partition has sorted records - which meets the requirement of consistently sorted data.
Thus:
Option A and B do not guarantee the persisted file contents are sorted.
Option C introduces a bottleneck via.coalesce(1)(single partition).
Option D correctly applies sorting within partitions and is scalable.
Reference: Databricks & Apache Spark 3.5 Documentation # DataFrame API # sortWithinPartitions()

NEW QUESTION # 34
A Spark developer is building an app to monitor task performance. They need to track the maximum task processing time per worker node and consolidate it on the driver for analysis.
Which technique should be used?

A. Configure the Spark UI to automatically collect maximum times
B. Broadcast a variable to share the maximum time among workers
C. Use an RDD action like reduce() to compute the maximum time
D. Use an accumulator to record the maximum time on the driver

Answer: C

Explanation:
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

NEW QUESTION # 35
The following code fragment results in an error:
@F.udf(T.IntegerType())
def simple_udf(t: str) -> str:
return answer * 3.14159
Which code fragment should be used instead?

A. @F.udf(T.IntegerType())
def simple_udf(t: float) -> float:
return t * 3.14159
B. @F.udf(T.IntegerType())
def simple_udf(t: int) -> int:
return t * 3.14159
C. @F.udf(T.DoubleType())
def simple_udf(t: float) -> float:
return t * 3.14159
D. @F.udf(T.DoubleType())
def simple_udf(t: int) -> int:
return t * 3.14159

Answer: C

Explanation:
Comprehensive and Detailed Explanation:
The original code has several issues:
It references a variable answer that is undefined.
The function is annotated to return a str, but the logic attempts numeric multiplication.
The UDF return type is declared as T.IntegerType() but the function performs a floating-point operation, which is incompatible.
Option B correctly:
Uses DoubleType to reflect the fact that the multiplication involves a float (3.14159).
Declares the input as float, which aligns with the multiplication.
Returns a float, which matches both the logic and the schema type annotation.
This structure aligns with how PySpark expects User Defined Functions (UDFs) to be declared:
"To define a UDF you must specify a Python function and provide the return type using the relevant Spark SQL type (e.g., DoubleType for float results)." Example from official documentation:
from pyspark.sql.functions import udf
from pyspark.sql.types import DoubleType
@udf(returnType=DoubleType())
def multiply_by_pi(x: float) -> float:
return x * 3.14159
This makes Option B the syntactically and semantically correct choice.

NEW QUESTION # 36
In the code block below,aggDFcontains aggregations on a streaming DataFrame:

Which output mode at line 3 ensures that the entire result table is written to the console during each trigger execution?

A. aggregate
B. append
C. replace
D. complete

Answer: D

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The correct output mode for streaming aggregations that need to output the full updated results at each trigger is"complete".
From the official documentation:
"complete: The entire updated result table will be output to the sink every time there is a trigger." This is ideal for aggregations, such as counts or averages grouped by a key, where the result table changes incrementally over time.
append: only outputs newly added rows
replace and aggregate: invalid values for output mode
Reference: Spark Structured Streaming Programming Guide # Output Modes

NEW QUESTION # 37
A data engineer is reviewing a Spark application that applies several transformations to a DataFrame but notices that the job does not start executing immediately.
Which two characteristics of Apache Spark's execution model explain this behavior?
Choose 2 answers:

A. Transformations are evaluated lazily.
B. The Spark engine optimizes the execution plan during the transformations, causing delays.
C. Transformations are executed immediately to build the lineage graph.
D. Only actions trigger the execution of the transformation pipeline.
E. The Spark engine requires manual intervention to start executing transformations.

Answer: A,D

Explanation:
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.

NEW QUESTION # 38
......

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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q33-Q38):

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