RELIABLE ASSOCIATE-DEVELOPER-APACHE-SPARK-3.5 EXAM SIMS, PASS ASSOCIATE-DEVELOPER-APACHE-SPARK-3.5 TEST GUIDE

Reliable Associate-Developer-Apache-Spark-3.5 Exam Sims, Pass Associate-Developer-Apache-Spark-3.5 Test Guide

Reliable Associate-Developer-Apache-Spark-3.5 Exam Sims, Pass Associate-Developer-Apache-Spark-3.5 Test Guide

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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q46-Q51):

NEW QUESTION # 46
A Spark engineer must select an appropriate deployment mode for the Spark jobs.
What is the benefit of using cluster mode in Apache Spark™?

  • A. In cluster mode, the driver program runs on one of the worker nodes, allowing the application to fully utilize the distributed resources of the cluster.
  • B. In cluster mode, the driver is responsible for executing all tasks locally without distributing them across the worker nodes.
  • C. In cluster mode, the driver runs on the client machine, which can limit the application's ability to handle large datasets efficiently.
  • D. In cluster mode, resources are allocated from a resource manager on the cluster, enabling better performance and scalability for large jobs

Answer: A

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
In Apache Spark's cluster mode:
"The driver program runs on the cluster's worker node instead of the client's local machine. This allows the driver to be close to the data and other executors, reducing network overhead and improving fault tolerance for production jobs." (Source: Apache Spark documentation -Cluster Mode Overview) This deployment is ideal for production environments where the job is submitted from a gateway node, and Spark manages the driver lifecycle on the cluster itself.
Option A is partially true but less specific than D.
Option B is incorrect: the driver never executes all tasks; executors handle distributed tasks.
Option C describes client mode, not cluster mode.


NEW QUESTION # 47
A data analyst wants to add a column date derived from a timestamp column.
Options:

  • A. dates_df.withColumn("date", f.from_unixtime("timestamp")).show()
  • B. dates_df.withColumn("date", f.date_format("timestamp", "yyyy-MM-dd")).show()
  • C. dates_df.withColumn("date", f.unix_timestamp("timestamp")).show()
  • D. dates_df.withColumn("date", f.to_date("timestamp")).show()

Answer: D

Explanation:
f.to_date() converts a timestamp or string to a DateType.
Ideal for extracting the date component (year-month-day) from a full timestamp.
Example:
frompyspark.sql.functionsimportto_date
dates_df.withColumn("date", to_date("timestamp"))
Reference:Spark SQL Date Functions


NEW QUESTION # 48
What is the difference betweendf.cache()anddf.persist()in Spark DataFrame?

  • A. persist()- Persists the DataFrame with the default storage level (MEMORY_AND_DISK_SER) andcache()- Can be used to set different storage levels to persist the contents of the DataFrame.
  • B. Bothcache()andpersist()can be used to set the default storage level (MEMORY_AND_DISK_SER)
  • C. cache()- Persists the DataFrame with the default storage level (MEMORY_AND_DISK) andpersist()- Can be used to set different storage levels to persist the contents of the DataFrame
  • D. Both functions perform the same operation. Thepersist()function provides improved performance asits default storage level isDISK_ONLY.

Answer: C

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
df.cache()is shorthand fordf.persist(StorageLevel.MEMORY_AND_DISK)
df.persist()allows specifying any storage level such asMEMORY_ONLY,DISK_ONLY, MEMORY_AND_DISK_SER, etc.
By default,persist()usesMEMORY_AND_DISK, unless specified otherwise.
Reference:Spark Programming Guide - Caching and Persistence


NEW QUESTION # 49
A data engineer needs to write a Streaming DataFrame as Parquet files.
Given the code:

Which code fragment should be inserted to meet the requirement?
A)

B)

C)

D)

Which code fragment should be inserted to meet the requirement?

  • A. .format("parquet")
    .option("path", "path/to/destination/dir")
  • B. .format("parquet")
    .option("location", "path/to/destination/dir")
  • C. CopyEdit
    .option("format", "parquet")
    .option("destination", "path/to/destination/dir")
  • D. .option("format", "parquet")
    .option("location", "path/to/destination/dir")

Answer: A

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
To write a structured streaming DataFrame to Parquet files, the correct way to specify the format and output directory is:
writeStream
format("parquet")
option("path", "path/to/destination/dir")
According to Spark documentation:
"When writing to file-based sinks (like Parquet), you must specify the path using the .option("path", ...) method. Unlike batch writes, .save() is not supported." Option A incorrectly uses.option("location", ...)(invalid for Parquet sink).
Option B incorrectly sets the format via.option("format", ...), which is not the correct method.
Option C repeats the same issue.
Option D is correct:.format("parquet")+.option("path", ...)is the required syntax.
Final Answer: D


NEW QUESTION # 50
Given a DataFramedfthat has 10 partitions, after running the code:
result = df.coalesce(20)
How many partitions will the result DataFrame have?

  • A. 0
  • B. 1
  • C. 2
  • D. Same number as the cluster executors

Answer: C

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The.coalesce(numPartitions)function is used to reduce the number of partitions in a DataFrame. It does not increase the number of partitions. If the specified number of partitions is greater than the current number, it will not have any effect.
From the official Spark documentation:
"coalesce() results in a narrow dependency, e.g. if you go from 1000 partitions to 100 partitions, there will not be a shuffle, instead each of the 100 new partitions will claim one or more of the current partitions." However, if you try to increase partitions using coalesce (e.g., from 10 to 20), the number of partitions remains unchanged.
Hence,df.coalesce(20)will still return a DataFrame with 10 partitions.
Reference: Apache Spark 3.5 Programming Guide # RDD and DataFrame Operations # coalesce()


NEW QUESTION # 51
......

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