REAL DATABRICKS ASSOCIATE-DEVELOPER-APACHE-SPARK-3.5 EXAM - ASSOCIATE-DEVELOPER-APACHE-SPARK-3.5 SURE PASS

Real Databricks Associate-Developer-Apache-Spark-3.5 Exam - Associate-Developer-Apache-Spark-3.5 Sure Pass

Real Databricks Associate-Developer-Apache-Spark-3.5 Exam - Associate-Developer-Apache-Spark-3.5 Sure Pass

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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q52-Q57):

NEW QUESTION # 52
Which command overwrites an existing JSON file when writing a DataFrame?

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

Answer: A

Explanation:
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


NEW QUESTION # 53
A data scientist is working with a Spark DataFrame called customerDF that contains customer information.
The DataFrame has a column named email with customer email addresses. The data scientist needs to split this column into username and domain parts.
Which code snippet splits the email column into username and domain columns?

  • A. customerDF.select(
    col("email").substr(0, 5).alias("username"),
    col("email").substr(-5).alias("domain")
    )
  • B. customerDF.withColumn("username", substring_index(col("email"), "@", 1))
    .withColumn("domain", substring_index(col("email"), "@", -1))
  • C. customerDF.withColumn("username", split(col("email"), "@").getItem(0))
    .withColumn("domain", split(col("email"), "@").getItem(1))
  • D. customerDF.select(
    regexp_replace(col("email"), "@", "").alias("username"),
    regexp_replace(col("email"), "@", "").alias("domain")
    )

Answer: C

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
Option B is the correct and idiomatic approach in PySpark to split a string column (like email) based on a delimiter such as "@".
The split(col("email"), "@") function returns an array with two elements: username and domain.
getItem(0) retrieves the first part (username).
getItem(1) retrieves the second part (domain).
withColumn() is used to create new columns from the extracted values.
Example from official Databricks Spark documentation on splitting columns:
from pyspark.sql.functions import split, col
df.withColumn("username", split(col("email"), "@").getItem(0))
withColumn("domain", split(col("email"), "@").getItem(1))
##Why other options are incorrect:
A uses fixed substring indices (substr(0, 5)), which won't correctly extract usernames and domains of varying lengths.
C uses substring_index, which is available but less idiomatic for splitting emails and is slightly less readable.
D removes "@" from the email entirely, losing the separation between username and domain, and ends up duplicating values in both fields.
Therefore, Option B is the most accurate and reliable solution according to Apache Spark 3.5 best practices.


NEW QUESTION # 54
What is a feature of Spark Connect?

  • A. It supports DataStreamReader, DataStreamWriter, StreamingQuery, and Streaming APIs
  • B. It has built-in authentication
  • C. Supports DataFrame, Functions, Column, SparkContext PySpark APIs
  • D. It supports only PySpark applications

Answer: A

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
Spark Connect is a client-server architecture introduced in Apache Spark 3.4, designed to decouple the client from the Spark driver, enabling remote connectivity to Spark clusters.
According to the Spark 3.5.5 documentation:
"Majority of the Streaming API is supported, including DataStreamReader, DataStreamWriter, StreamingQuery and StreamingQueryListener." This indicates that Spark Connect supports key components of Structured Streaming, allowing for robust streaming data processing capabilities.
Regarding other options:
B).While Spark Connect supports DataFrame, Functions, and Column APIs, it does not support SparkContext and RDD APIs.
C).Spark Connect supports multiple languages, including PySpark and Scala, not just PySpark.
D).Spark Connect does not have built-in authentication but is designed to work seamlessly with existing authentication infrastructures.


NEW QUESTION # 55
Which Spark configuration controls the number of tasks that can run in parallel on the executor?
Options:

  • A. spark.executor.memory
  • B. spark.executor.cores
  • C. spark.driver.cores
  • D. spark.task.maxFailures

Answer: B

Explanation:
spark.executor.cores determines how many concurrent tasks an executor can run.
For example, if set to 4, each executor can run up to 4 tasks in parallel.
Other settings:
spark.task.maxFailures controls task retry logic.
spark.driver.cores is for the driver, not executors.
spark.executor.memory sets memory limits, not task concurrency.
Reference:Apache Spark Configuration


NEW QUESTION # 56
A Data Analyst needs to retrieve employees with 5 or more years of tenure.
Which code snippet filters and shows the list?

  • A. employees_df.filter(employees_df.tenure >= 5).show()
  • B. employees_df.where(employees_df.tenure >= 5)
  • C. employees_df.filter(employees_df.tenure >= 5).collect()
  • D. filter(employees_df.tenure >= 5)

Answer: A

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
To filter rows based on a condition and display them in Spark, usefilter(...).show():
employees_df.filter(employees_df.tenure >= 5).show()
Option A is correct and shows the results.
Option B filters but doesn't display them.
Option C uses Python's built-infilter, not Spark.
Option D collects the results to the driver, which is unnecessary if.show()is sufficient.
Final Answer: A


NEW QUESTION # 57
......

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