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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q57-Q62):

NEW QUESTION # 57
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.DoubleType())
def simple_udf(t: float) -> float:
return t * 3.14159
C. @F.udf(T.DoubleType())
def simple_udf(t: int) -> int:
return t * 3.14159
D. @F.udf(T.IntegerType())
def simple_udf(t: int) -> int:
return t * 3.14159

Answer: B

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 # 58
A data engineer is running a batch processing job on a Spark cluster with the following configuration:
10 worker nodes
16 CPU cores per worker node
64 GB RAM per node
The data engineer wants to allocate four executors per node, each executor using four cores.
What is the total number of CPU cores used by the application?

A. 0
B. 1
C. 2
D. 3

Answer: D

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
If each of the 10 nodes runs 4 executors, and each executor is assigned 4 CPU cores:
Executors per node = 4
Cores per executor = 4
Total executors = 4 * 10 = 40
Total cores = 40 executors * 4 cores = 160 cores
However, Spark uses 1 core for overhead on each node when managing multiple executors. Thus, the practical allocation is:
Total usable executors = 4 executors/node × 10 nodes = 40
Total cores = 4 cores × 40 executors = 160
Answer is A - but the question asks specifically about "CPU cores used by the application," assuming all
allocated cores are usable (as Spark typically runs executors without internal core reservation unless explicitly configured).
However, if you are considering 4 executors/node × 4 cores = 16 cores per node, across 10 nodes, that's 160.
Final Answer: A

NEW QUESTION # 59
A developer notices that all the post-shuffle partitions in a dataset are smaller than the value set for spark.sql.adaptive.maxShuffledHashJoinLocalMapThreshold.
Which type of join will Adaptive Query Execution (AQE) choose in this case?

A. A shuffled hash join
B. A sort-merge join
C. A broadcast nested loop join
D. A Cartesian join

Answer: A

Explanation:
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

NEW QUESTION # 60
37 of 55.
A data scientist is working with a Spark DataFrame called customerDF that contains customer information.
The DataFrame has a column named email with customer email addresses.
The data scientist needs to split this column into username and domain parts.
Which code snippet splits the email column into username and domain columns?

A. customerDF = customerDF.withColumn("username", regexp_replace(col("email"), "@", ""))
B. customerDF = customerDF
.withColumn("username", split(col("email"), "@").getItem(0))
.withColumn("domain", split(col("email"), "@").getItem(1))
C. customerDF = customerDF.withColumn("domain", col("email").split("@")[1])
D. customerDF = customerDF.select("email").alias("username", "domain")

Answer: B

Explanation:
The split() function in PySpark splits strings into an array based on a given delimiter.
Then, .getItem(index) extracts a specific element from the array.
Correct usage:
from pyspark.sql.functions import split, col
customerDF = customerDF
.withColumn("username", split(col("email"), "@").getItem(0))
.withColumn("domain", split(col("email"), "@").getItem(1))
This creates two new columns derived from the email field:
"username" → text before @
"domain" → text after @
Why the other options are incorrect:
B: regexp_replace only replaces text; does not split into multiple columns.
C: .select() cannot alias multiple derived columns like this.
D: Column objects are not native Python strings; cannot use standard .split().
Reference:
PySpark SQL Functions - split() and getItem().
Databricks Exam Guide (June 2025): Section "Developing Apache Spark DataFrame/DataSet API Applications" - manipulating and splitting column data.

NEW QUESTION # 61
48 of 55.
A data engineer needs to join multiple DataFrames and has written the following code:
from pyspark.sql.functions import broadcast
data1 = [(1, "A"), (2, "B")]
data2 = [(1, "X"), (2, "Y")]
data3 = [(1, "M"), (2, "N")]
df1 = spark.createDataFrame(data1, ["id", "val1"])
df2 = spark.createDataFrame(data2, ["id", "val2"])
df3 = spark.createDataFrame(data3, ["id", "val3"])
df_joined = df1.join(broadcast(df2), "id", "inner")
.join(broadcast(df3), "id", "inner")
What will be the output of this code?

A. The code will work correctly and perform two broadcast joins simultaneously to join df1 with df2, and then the result with df3.
B. The code will fail because only one broadcast join can be performed at a time.
C. The code will fail because the second join condition (df2.id == df3.id) is incorrect.
D. The code will result in an error because broadcast() must be called before the joins, not inline.

Answer: A

Explanation:
Spark supports multiple broadcast joins in a single query plan, as long as each broadcasted DataFrame is small enough to fit under the configured threshold.
Execution Plan:
Spark broadcasts df2 to all executors.
Joins df1 (big) with broadcasted df2.
Then broadcasts df3 and performs another join with the intermediate result.
The result is efficient and avoids shuffling large data.
Why the other options are incorrect:
B: Multiple broadcast joins are supported in Spark 3.x.
C: The join condition is correct since all use id as the key.
D: broadcast() can be used inline; it's valid syntax.
Reference:
PySpark SQL Functions - broadcast() usage.
Databricks Exam Guide (June 2025): Section "Developing Apache Spark DataFrame/DataSet API Applications" - multiple broadcast join optimization.

NEW QUESTION # 62
......

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