FREE PDF QUIZ 2025 HIGH-QUALITY DATABRICKS VALID DUMPS ASSOCIATE-DEVELOPER-APACHE-SPARK-3.5 BOOK

Free PDF Quiz 2025 High-quality Databricks Valid Dumps Associate-Developer-Apache-Spark-3.5 Book

Free PDF Quiz 2025 High-quality Databricks Valid Dumps Associate-Developer-Apache-Spark-3.5 Book

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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q15-Q20):

NEW QUESTION # 15
A data scientist has identified that some records in the user profile table contain null values in any of the fields, and such records should be removed from the dataset before processing. The schema includes fields like user_id, username, date_of_birth, created_ts, etc.
The schema of the user profile table looks like this:

Which block of Spark code can be used to achieve this requirement?
Options:

  • A. filtered_df = users_raw_df.na.drop(thresh=0)
  • B. filtered_df = users_raw_df.na.drop(how='all')
  • C. filtered_df = users_raw_df.na.drop(how='all', thresh=None)
  • D. filtered_df = users_raw_df.na.drop(how='any')

Answer: D

Explanation:
na.drop(how='any')drops any row that has at least one null value.
This is exactly what's needed when the goal is to retain only fully complete records.
Usage:CopyEdit
filtered_df = users_raw_df.na.drop(how='any')
Explanation of incorrect options:
A: thresh=0 is invalid - thresh must be # 1.
B: how='all' drops only rows where all columns are null (too lenient).
D: spark.na.drop doesn't support mixing how and thresh in that way; it's incorrect syntax.
Reference:PySpark DataFrameNaFunctions.drop()


NEW QUESTION # 16
A data scientist is working on a project that requires processing large amounts of structured data, performing SQL queries, and applying machine learning algorithms. The data scientist is considering using Apache Spark for this task.
Which combination of Apache Spark modules should the data scientist use in this scenario?
Options:

  • A. Spark DataFrames, Structured Streaming, and GraphX
  • B. Spark DataFrames, Spark SQL, and MLlib
  • C. Spark SQL, Pandas API on Spark, and Structured Streaming
  • D. Spark Streaming, GraphX, and Pandas API on Spark

Answer: B

Explanation:
Comprehensive Explanation:
To cover structured data processing, SQL querying, and machine learning in Apache Spark, the correct combination of components is:
Spark DataFrames: for structured data processing
Spark SQL: to execute SQL queries over structured data
MLlib: Spark's scalable machine learning library
This trio is designed for exactly this type of use case.
Why other options are incorrect:
A: GraphX is for graph processing - not needed here.
B: Pandas API on Spark is useful, but MLlib is essential for ML, which this option omits.
C: Spark Streaming is legacy; GraphX is irrelevant here.
Reference:Apache Spark Modules Overview


NEW QUESTION # 17
A data scientist wants each record in the DataFrame to contain:
The first attempt at the code does read the text files but each record contains a single line. This code is shown below:

The entire contents of a file
The full file path
The issue: reading line-by-line rather than full text per file.
Code:
corpus = spark.read.text("/datasets/raw_txt/*")
.select('*','_metadata.file_path')
Which change will ensure one record per file?
Options:

  • A. Add the option lineSep=", " to the text() function
  • B. Add the option wholetext=True to the text() function
  • C. Add the option lineSep='n' to the text() function
  • D. Add the option wholetext=False to the text() function

Answer: B

Explanation:
To read each file as a single record, use:
spark.read.text(path, wholetext=True)
This ensures that Spark reads the entire file contents into one row.
Reference:Spark read.text() with wholetext


NEW QUESTION # 18
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 by dividing the dataset size (1 TB) by a reasonable partition size, such as
    128 MB
  • 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 to a fixed value, such as 200

Answer: A

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 # 19
A Spark engineer is troubleshooting a Spark application that has been encountering out-of-memory errors during execution. By reviewing the Spark driver logs, the engineer notices multiple "GC overhead limit exceeded" messages.
Which action should the engineer take to resolve this issue?

  • A. Increase the memory allocated to the Spark Driver.
  • B. Optimize the data processing logic by repartitioning the DataFrame.
  • C. Cache large DataFrames to persist them in memory.
  • D. Modify the Spark configuration to disable garbage collection

Answer: A

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The message"GC overhead limit exceeded"typically indicates that the JVM is spending too much time in garbage collection with little memory recovery. This suggests that the driver or executor is under-provisioned in memory.
The most effective remedy is to increase the driver memory using:
--driver-memory 4g
This is confirmed in Spark's official troubleshooting documentation:
"If you see a lot ofGC overhead limit exceedederrors in the driver logs, it's a sign that the driver is running out of memory."
-Spark Tuning Guide
Why others are incorrect:
Amay help but does not directly address the driver memory shortage.
Bis not a valid action; GC cannot be disabled.
Dincreases memory usage, worsening the problem.


NEW QUESTION # 20
......

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