ASSOCIATE-DEVELOPER-APACHE-SPARK-3.5 RELIABLE TEST SAMPLE - ASSOCIATE-DEVELOPER-APACHE-SPARK-3.5 PDF CRAM EXAM

Associate-Developer-Apache-Spark-3.5 Reliable Test Sample - Associate-Developer-Apache-Spark-3.5 PDF Cram Exam

Associate-Developer-Apache-Spark-3.5 Reliable Test Sample - Associate-Developer-Apache-Spark-3.5 PDF Cram Exam

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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q35-Q40):

NEW QUESTION # 35
Given this view definition:
df.createOrReplaceTempView("users_vw")
Which approach can be used to query the users_vw view after the session is terminated?
Options:

  • A. Recreate the users_vw and query the data using Spark
  • B. Save the users_vw definition and query using Spark
  • C. Query the users_vw using Spark
  • D. Persist the users_vw data as a table

Answer: D

Explanation:
Temp views likecreateOrReplaceTempVieware session-scoped.
They disappear once the Spark session ends.
To retain data across sessions, it must be persisted:
df.write.saveAsTable("users_vw")
Thus, the view needs to be persisted as a table to survive session termination.
Reference:Databricks - Temp vs Global vs Permanent Views


NEW QUESTION # 36
A Spark DataFramedfis cached using theMEMORY_AND_DISKstorage level, but the DataFrame is too large to fit entirely in memory.
What is the likely behavior when Spark runs out of memory to store the DataFrame?

  • A. Spark will store as much data as possible in memory and spill the rest to disk when memory is full, continuing processing with performance overhead.
  • B. Spark stores the frequently accessed rows in memory and less frequently accessed rows on disk, utilizing both resources to offer balanced performance.
  • C. Spark splits the DataFrame evenly between memory and disk, ensuring balanced storage utilization.
  • D. Spark duplicates the DataFrame in both memory and disk. If it doesn't fit in memory, the DataFrame is stored and retrieved from the disk entirely.

Answer: A

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
When using theMEMORY_AND_DISKstorage level, Spark attempts to cache as much of the DataFrame in memory as possible. If the DataFrame does not fit entirely in memory, Spark will store the remaining partitions on disk. This allows processing to continue, albeit with a performance overhead due to disk I/O.
As per the Spark documentation:
"MEMORY_AND_DISK: It stores partitions that do not fit in memory on disk and keeps the rest in memory.
This can be useful when working with datasets that are larger than the available memory."
- Perficient Blogs: Spark - StorageLevel
This behavior ensures that Spark can handle datasets larger than the available memory by spilling excess data to disk, thus preventing job failures due to memory constraints.


NEW QUESTION # 37
A data engineer replaces the exact percentile() function with approx_percentile() to improve performance, but the results are drifting too far from expected values.
Which change should be made to solve the issue?

  • A. Decrease the first value of the percentage parameter to increase the accuracy of the percentile ranges
  • B. Decrease the value of the accuracy parameter in order to decrease the memory usage but also improve the accuracy
  • C. Increase the value of the accuracy parameter in order to increase the memory usage but also improve the accuracy
  • D. Increase the last value of the percentage parameter to increase the accuracy of the percentile ranges

Answer: C

Explanation:
Comprehensive and Detailed Explanation:
The approx_percentile function in Spark is a performance-optimized alternative to percentile. It takes an optional accuracy parameter:
approx_percentile(column, percentage, accuracy)
Higher accuracy values # more precise results, but increased memory/computation.
Lower values # faster but less accurate.
From the documentation:
"Increasing the accuracy improves precision but increases memory usage." Final Answer: D


NEW QUESTION # 38
Which configuration can be enabled to optimize the conversion between Pandas and PySpark DataFrames using Apache Arrow?

  • A. spark.conf.set("spark.sql.execution.arrow.pyspark.enabled", "true")
  • B. spark.conf.set("spark.sql.execution.arrow.enabled", "true")
  • C. spark.conf.set("spark.sql.arrow.pandas.enabled", "true")
  • D. spark.conf.set("spark.pandas.arrow.enabled", "true")

Answer: A

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
Apache Arrow is used under the hood to optimize conversion between Pandas and PySpark DataFrames. The correct configuration setting is:
spark.conf.set("spark.sql.execution.arrow.pyspark.enabled", "true")
From the official documentation:
"This configuration must be enabled to allow for vectorized execution and efficient conversion between Pandas and PySpark using Arrow." Option B is correct.
Options A, C, and D are invalid config keys and not recognized by Spark.
Final Answer: B


NEW QUESTION # 39
A data scientist at a financial services company is working with a Spark DataFrame containing transaction records. The DataFrame has millions of rows and includes columns fortransaction_id,account_number, transaction_amount, andtimestamp. Due to an issue with the source system, some transactions were accidentally recorded multiple times with identical information across all fields. The data scientist needs to remove rows with duplicates across all fields to ensure accurate financial reporting.
Which approach should the data scientist use to deduplicate the orders using PySpark?

  • A. df = df.groupBy("transaction_id").agg(F.first("account_number"), F.first("transaction_amount"), F.first ("timestamp"))
  • B. df = df.dropDuplicates(["transaction_amount"])
  • C. df = df.dropDuplicates()
  • D. df = df.filter(F.col("transaction_id").isNotNull())

Answer: C

Explanation:
dropDuplicates() with no column list removes duplicates based on all columns.
It's the most efficient and semantically correct way to deduplicate records that are completely identical across all fields.
From the PySpark documentation:
dropDuplicates(): Return a new DataFrame with duplicate rows removed, considering all columns if none are specified.
- Source:PySpark DataFrame.dropDuplicates() API


NEW QUESTION # 40
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