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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q39-Q44):

NEW QUESTION # 39
A data engineer uses a broadcast variable to share a DataFrame containing millions of rows across executors for lookup purposes. What will be the outcome?

A. The job will hang indefinitely as Spark will struggle to distribute and serialize such a large broadcast variable to all executors
B. The job may fail if the memory on each executor is not large enough to accommodate the DataFrame being broadcasted
C. The job may fail because the driver does not have enough CPU cores to serialize the large DataFrame
D. The job may fail if the executors do not have enough CPU cores to process the broadcasted dataset

Answer: B

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
In Apache Spark, broadcast variables are used to efficiently distribute large, read-only data to all worker nodes. However, broadcasting very large datasets can lead to memory issues on executors if the data does not fit into the available memory.
According to the Spark documentation:
"Broadcast variables allow the programmer to keep a read-only variable cached on each machine rather than shipping a copy of it with tasks. This can greatly reduce the amount of data sent over the network." However, it also notes:
"Using the broadcast functionality available in SparkContext can greatly reduce the size of each serialized task, and the cost of launching a job over a cluster. If your tasks use any large object from the driver program inside of them (e.g., a static lookup table), consider turning it into a broadcast variable." But caution is advised when broadcasting large datasets:
"Broadcasting large variables can cause out-of-memory errors if the data does not fit in the memory of each executor." Therefore, if the broadcasted DataFrame containing millions of rows exceeds the memory capacity of the executors, the job may fail due to memory constraints.
Reference:Spark 3.5.5 Documentation - Tuning

NEW QUESTION # 40
A developer wants to refactor some older Spark code to leverage built-in functions introduced in Spark 3.5.0.
The existing code performs array manipulations manually. Which of the following code snippets utilizes new built-in functions in Spark 3.5.0 for array operations?

A. result_df = prices_df
.agg(F.count_if(F.col("spot_price") >= F.lit(min_price)))
B. result_df = prices_df
.withColumn("valid_price", F.when(F.col("spot_price") > F.lit(min_price), 1).otherwise(0))
C. result_df = prices_df
.agg(F.min("spot_price"), F.max("spot_price"))
D. result_df = prices_df
.agg(F.count("spot_price").alias("spot_price"))
.filter(F.col("spot_price") > F.lit("min_price"))

Answer: A

Explanation:
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.

NEW QUESTION # 41
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.DoubleType())
def simple_udf(t: float) -> float:
return t * 3.14159
B. @F.udf(T.IntegerType())
def simple_udf(t: float) -> float:
return t * 3.14159
C. @F.udf(T.IntegerType())
def simple_udf(t: int) -> int:
return t * 3.14159
D. @F.udf(T.DoubleType())
def simple_udf(t: int) -> int:
return t * 3.14159

Answer: A

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 # 42
Given this code:

.withWatermark("event_time","10 minutes")
.groupBy(window("event_time","15 minutes"))
.count()
What happens to data that arrives after the watermark threshold?
Options:

A. Data arriving more than 10 minutes after the latest watermark will still be included in the aggregation but will be placed into the next window.
B. Any data arriving more than 10 minutes after the watermark threshold will be ignored and not included in the aggregation.
C. Records that arrive later than the watermark threshold (10 minutes) will automatically be included in the aggregation if they fall within the 15-minute window.
D. The watermark ensures that late data arriving within 10 minutes of the latest event_time will be processed and included in the windowed aggregation.

Answer: B

Explanation:
According to Spark's watermarking rules:
"Records that are older than the watermark (event time < current watermark) are considered too late and are dropped." So, if a record'sevent_timeis earlier than (max event_time seen so far - 10 minutes), it is discarded.
Reference:Structured Streaming - Handling Late Data

NEW QUESTION # 43
A data engineer noticed improved performance after upgrading from Spark 3.0 to Spark 3.5. The engineer found that Adaptive Query Execution (AQE) was enabled.
Which operation is AQE implementing to improve performance?

A. Optimizing the layout of Delta files on disk
B. Improving the performance of single-stage Spark jobs
C. Collecting persistent table statistics and storing them in the metastore for future use
D. Dynamically switching join strategies

Answer: D

Explanation:
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

NEW QUESTION # 44
......

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