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

NEW QUESTION # 57
Which feature of Spark Connect is considered when designing an application to enable remote interaction with the Spark cluster?

  • A. It is primarily used for data ingestion into Spark from external sources
  • B. It can be used to interact with any remote cluster using the REST API
  • C. It allows for remote execution of Spark jobs
  • D. It provides a way to run Spark applications remotely in any programming language

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 # 58
A Spark application suffers from too many small tasks due to excessive partitioning. How can this be fixed without a full shuffle?
Options:

  • A. Use the repartition() transformation with a lower number of partitions
  • B. Use the sortBy() transformation to reorganize the data
  • C. Use the distinct() transformation to combine similar partitions
  • D. Use the coalesce() transformation with a lower number of partitions

Answer: D

Explanation:
coalesce(n) reduces the number of partitions without triggering a full shuffle, unlike repartition().
This is ideal when reducing partition count, especially during write operations.
Reference:Spark API - coalesce


NEW QUESTION # 59
The following code fragment results in an error:

Which code fragment should be used instead?

  • A.
  • B.
  • C.
  • D.

Answer: B


NEW QUESTION # 60
What is the benefit of using Pandas on Spark for data transformations?
Options:

  • A. It is available only with Python, thereby reducing the learning curve.
  • B. It computes results immediately using eager execution, making it simple to use.
  • C. It runs on a single node only, utilizing the memory with memory-bound DataFrames and hence cost- efficient.
  • D. It executes queries faster using all the available cores in the cluster as well as provides Pandas's rich set of features.

Answer: D

Explanation:
Pandas API on Spark (formerly Koalas) offers:
Familiar Pandas-like syntax
Distributed execution using Spark under the hood
Scalability for large datasets across the cluster
It provides the power of Spark while retaining the productivity of Pandas.
Reference:Pandas API on Spark Guide


NEW QUESTION # 61
Which UDF implementation calculates the length of strings in a Spark DataFrame?

  • A. df.select(length(col("stringColumn")).alias("length"))
  • B. df.withColumn("length", udf(lambda s: len(s), StringType()))
  • C. spark.udf.register("stringLength", lambda s: len(s))
  • D. df.withColumn("length", spark.udf("len", StringType()))

Answer: A

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
Option B uses Spark's built-in SQL function length(), which is efficient and avoids the overhead of a Python UDF:
from pyspark.sql.functions import length, col
df.select(length(col("stringColumn")).alias("length"))
Explanation of other options:
Option A is incorrect syntax;spark.udfis not called this way.
Option C registers a UDF but doesn't apply it in the DataFrame transformation.
Option D is syntactically valid but uses a Python UDF which is less efficient than built-in functions.
Final Answer: B


NEW QUESTION # 62
......

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