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【Hardware】 Associate-Developer-Apache-Spark-3.5 Exam Cost - Associate-Developer-Apache-Spar

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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q48-Q53):NEW QUESTION # 48
20 of 55.
What is the difference between df.cache() and df.persist() in Spark DataFrame?
  • A. cache() - Persists the DataFrame with the default storage level (MEMORY_AND_DISK_DESER), and persist() - Can be used to set different storage levels to persist the contents of the DataFrame.
  • B. Both functions perform the same operation. The persist() function provides improved performance as its default storage level is DISK_ONLY.
  • C. Both cache() and persist() can be used to set the default storage level (MEMORY_AND_DISK_DESER).
  • D. persist() - Persists the DataFrame with the default storage level (MEMORY_AND_DISK_DESER), and cache() - Can be used to set different storage levels.
Answer: A
Explanation:
Both cache() and persist() are Spark DataFrame storage operations that store computed results in memory (and optionally on disk) to speed up subsequent actions on the same DataFrame.
Key difference:
cache() is a shorthand for persist(StorageLevel.MEMORY_AND_DISK).
persist() allows specifying different storage levels, such as MEMORY_ONLY, DISK_ONLY, or MEMORY_AND_DISK_SER.
Example:
df.cache() # Uses MEMORY_AND_DISK by default
df.persist(StorageLevel.MEMORY_ONLY) # Custom storage level
Both trigger caching upon an action (e.g., count(), collect()).
Why the other options are incorrect:
A: persist() default is not DISK_ONLY; default storage level is MEMORY_AND_DISK.
B/C: cache() cannot set arbitrary levels; only persist() can.
Reference:
PySpark API Reference - DataFrame.cache() and DataFrame.persist().
Databricks Exam Guide (June 2025): Section "Developing Apache Spark DataFrame/DataSet API Applications" - caching, persistence, and storage levels.

NEW QUESTION # 49
A data engineer wants to create a Streaming DataFrame that reads from a Kafka topic called feed.

Which code fragment should be inserted in line 5 to meet the requirement?
Code context:
spark
.readStream
.format("kafka")
.option("kafka.bootstrap.servers", "host1:port1,host2:port2")
.[LINE 5]
.load()
Options:
  • A. .option("subscribe.topic", "feed")
  • B. .option("kafka.topic", "feed")
  • C. .option("subscribe", "feed")
  • D. .option("topic", "feed")
Answer: C
Explanation:
To read from a specific Kafka topic using Structured Streaming, the correct syntax is:
python
CopyEdit
.option("subscribe", "feed")
This is explicitly defined in the Spark documentation:
"subscribe - The Kafka topic to subscribe to. Only one topic can be specified for this option." (Source: Apache Spark Structured Streaming + Kafka Integration Guide)
"subscribe - The Kafka topic to subscribe to. Only one topic can be specified for this option." (Source: Apache Spark Structured Streaming + Kafka Integration Guide) B . "subscribe.topic" is invalid.
C . "kafka.topic" is not a recognized option.
D . "topic" is not valid for Kafka source in Spark.

NEW QUESTION # 50
A data engineer is running a Spark job to process a dataset of 1 TB stored in distributed storage. The cluster has 10 nodes, each with 16 CPUs. Spark UI shows:
Low number of Active Tasks
Many tasks complete in milliseconds
Fewer tasks than available CPUs
Which approach should be used to adjust the partitioning for optimal resource allocation?
  • A. Set the number of partitions to a fixed value, such as 200
  • B. Set the number of partitions equal to the number of nodes in the cluster
  • C. Set the number of partitions equal to the total number of CPUs in the cluster
  • D. Set the number of partitions by dividing the dataset size (1 TB) by a reasonable partition size, such as
    128 MB
Answer: D
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
Spark's best practice is to estimate partition count based on data volume and a reasonable partition size - typically 128 MB to 256 MB per partition.
With 1 TB of data: 1 TB / 128 MB # ~8000 partitions
This ensures that tasks are distributed across available CPUs for parallelism and that each task processes an optimal volume of data.
Option A (equal to cores) may result in partitions that are too large.
Option B (fixed 200) is arbitrary and may underutilize the cluster.
Option C (nodes) gives too few partitions (10), limiting parallelism.
Reference: Databricks Spark Tuning Guide # Partitioning Strategy

NEW QUESTION # 51
A data engineer needs to persist a file-based data source to a specific location. However, by default, Spark writes to the warehouse directory (e.g., /user/hive/warehouse). To override this, the engineer must explicitly define the file path.
Which line of code ensures the data is saved to a specific location?
Options:
  • A. users.write(path="/some/path").saveAsTable("default_table")
  • B. users.write.saveAsTable("default_table").option("path", "/some/path")
  • C. users.write.option("path", "/some/path").saveAsTable("default_table")
  • D. users.write.saveAsTable("default_table", path="/some/path")
Answer: C
Explanation:
To persist a table and specify the save path, use:
users.write.option("path","/some/path").saveAsTable("default_table")
The .option("path", ...) must be applied before calling saveAsTable.
Option A uses invalid syntax (write(path=...)).
Option B applies.option()after.saveAsTable()-which is too late.
Option D uses incorrect syntax (no path parameter in saveAsTable).
Reference:Spark SQL - Save as Table

NEW QUESTION # 52
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?
  • A. The code throws an error due to a schema mismatch.
  • B. [Row(name='alladin', age=20)]
  • C. [Row(name='bambi'), Row(name='alladin', age=20)]
  • D. [Row(name='bambi', age=None), Row(name='alladin', age=20)]
Answer: D
Explanation:
In Spark, when a CSV row does not match the provided schema, Spark does not raise an error by default. Instead, it returns null for fields that cannot be parsed correctly.
In the first row, "hello" cannot be cast to Integer for the age field → Spark sets age=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 # 53
......
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