Databricks Associate-Developer-Apache-Spark-3.5 Exam | Associate-Developer-Apache-Spark-3.5 Customized Lab Simulation - Official Pass Certify Advanced Associate-Developer-Apache-Spark-3.5 Testing Engine
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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q26-Q31):
NEW QUESTION # 26
Given:
python
CopyEdit
spark.sparkContext.setLogLevel("<LOG_LEVEL>")
Which set contains the suitable configuration settings for Spark driver LOG_LEVELs?
Answer: D
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
ThesetLogLevel()method ofSparkContextsets the logging level on the driver, which controls the verbosity of logs emitted during job execution. Supported levels are inherited from log4j and include the following:
ALL
DEBUG
ERROR
FATAL
INFO
OFF
TRACE
WARN
According to official Spark and Databricks documentation:
"Valid log levels include: ALL, DEBUG, ERROR, FATAL, INFO, OFF, TRACE, and WARN." Among the choices provided, only option B (ERROR, WARN, TRACE, OFF) includes four valid log levels and excludes invalid ones like "FAIL" or "NONE".
Reference: Apache Spark API docs # SparkContext.setLogLevel
NEW QUESTION # 27
Given the code fragment:
import pyspark.pandas as ps
psdf = ps.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
Which method is used to convert a Pandas API on Spark DataFrame (pyspark.pandas.DataFrame) into a standard PySpark DataFrame (pyspark.sql.DataFrame)?
Answer: D
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
Pandas API on Spark (pyspark.pandas) allows interoperability with PySpark DataFrames. To convert apyspark.pandas.DataFrameto a standard PySpark DataFrame, you use.to_spark().
Example:
df = psdf.to_spark()
This is the officially supported method as per Databricks Documentation.
Incorrect options:
B, D: Invalid or nonexistent methods.
C: Converts to a local pandas DataFrame, not a PySpark DataFrame.
NEW QUESTION # 28
Given a CSV file with the content:
And the following code:
from pyspark.sql.types import *
schema = StructType([
StructField("name", StringType()),
StructField("age", IntegerType())
])
spark.read.schema(schema).csv(path).collect()
What is the resulting output?
Answer: B
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
In Spark, when a CSV row does not match the provided schema, Spark does not raise an error by default.
Instead, it returnsnullfor fields that cannot be parsed correctly.
In the first row,"hello"cannot be cast to Integer for theagefield # Spark setsage=None In the second row,"20"is a valid integer #age=20 So the output will be:
[Row(name='bambi', age=None), Row(name='alladin', age=20)]
Final Answer: C
NEW QUESTION # 29
Which feature of Spark Connect is considered when designing an application to enable remote interaction with the Spark cluster?
Answer: C
Explanation:
Comprehensive and Detailed Explanation:
Spark Connect introduces a decoupled client-server architecture. Its key feature is enabling Spark job submission and execution from remote clients - in Python, Java, etc.
From Databricks documentation:
"Spark Connect allows remote clients to connect to a Spark cluster and execute Spark jobs without being co- located with the Spark driver." A is close, but "any language" is overstated (currently supports Python, Java, etc., not literally all).
B refers to REST, which is not Spark Connect's mechanism.
D is incorrect; Spark Connect isn't focused on ingestion.
Final Answer: C
NEW QUESTION # 30
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?
Answer: B
Explanation:
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.
NEW QUESTION # 31
......
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