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Hohe Qualität von Associate-Developer-Apache-Spark-3.5 Prüfung und Antworten

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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Associate-Developer-Apache-Spark-3.5 Prüfungsfragen mit Lösungen (Q44-Q49):

44. Frage
Which command overwrites an existing JSON file when writing a DataFrame?

A. df.write.json("path/to/file", overwrite=True)
B. df.write.mode("overwrite").json("path/to/file")
C. df.write.format("json").save("path/to/file", mode="overwrite")
D. df.write.overwrite.json("path/to/file")

Antwort: B

Begründung:
The correct way to overwrite an existing file using the DataFrameWriter is:
df.write.mode("overwrite").json("path/to/file")
Option D is also technically valid, but Option A is the most concise and idiomatic PySpark syntax.
Reference:PySpark DataFrameWriter API

45. Frage
A developer initializes a SparkSession:

spark = SparkSession.builder
.appName("Analytics Application")
.getOrCreate()
Which statement describes thesparkSparkSession?

A. A new SparkSession is created every time thegetOrCreate()method is invoked.
B. If a SparkSession already exists, this code will return the existing session instead of creating a new one.
C. A SparkSession is unique for eachappName, and callinggetOrCreate()with the same name will return an existing SparkSession once it has been created.
D. ThegetOrCreate()method explicitly destroys any existing SparkSession and creates a new one.

Antwort: B

Begründung:
Comprehensive and Detailed Explanation From Exact Extract:
According to the PySpark API documentation:
"getOrCreate(): Gets an existing SparkSession or, if there is no existing one, creates a new one based on the options set in this builder." This means Spark maintains a global singleton session within a JVM process. Repeated calls togetOrCreate() return the same session, unless explicitly stopped.
Option A is incorrect: the method does not destroy any session.
Option B incorrectly ties uniqueness toappName, which does not influence session reusability.
Option D is incorrect: it contradicts the fundamental behavior ofgetOrCreate().
(Source:PySpark SparkSession API Docs)

46. Frage
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

Antwort: B

Begründung:
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

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

Antwort: D

Begründung:
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.

48. Frage
A Spark engineer is troubleshooting a Spark application that has been encountering out-of-memory errors during execution. By reviewing the Spark driver logs, the engineer notices multiple "GC overhead limit exceeded" messages.
Which action should the engineer take to resolve this issue?

A. Optimize the data processing logic by repartitioning the DataFrame.
B. Modify the Spark configuration to disable garbage collection
C. Increase the memory allocated to the Spark Driver.
D. Cache large DataFrames to persist them in memory.

Antwort: C

Begründung:
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.

49. Frage
......

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