PASS GUARANTEED QUIZ 2025 DATABRICKS ASSOCIATE-DEVELOPER-APACHE-SPARK-3.5: DATABRICKS CERTIFIED ASSOCIATE DEVELOPER FOR APACHE SPARK 3.5 - PYTHON–VALID EXAM QUESTION

Pass Guaranteed Quiz 2025 Databricks Associate-Developer-Apache-Spark-3.5: Databricks Certified Associate Developer for Apache Spark 3.5 - Python–Valid Exam Question

Pass Guaranteed Quiz 2025 Databricks Associate-Developer-Apache-Spark-3.5: Databricks Certified Associate Developer for Apache Spark 3.5 - Python–Valid Exam Question

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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q27-Q32):

NEW QUESTION # 27
What is the benefit of using Pandas on Spark for data transformations?
Options:

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

Answer: C

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 # 28
A developer needs to produce a Python dictionary using data stored in a small Parquet table, which looks like this:

The resulting Python dictionary must contain a mapping of region-> region id containing the smallest 3 region_idvalues.
Which code fragment meets the requirements?
A)

B)

C)

D)

The resulting Python dictionary must contain a mapping ofregion -> region_idfor the smallest
3region_idvalues.
Which code fragment meets the requirements?

  • A. regions = dict(
    regions_df
    .select('region', 'region_id')
    .sort(desc('region_id'))
    .take(3)
    )
  • B. regions = dict(
    regions_df
    .select('region_id', 'region')
    .sort('region_id')
    .take(3)
    )
  • C. regions = dict(
    regions_df
    .select('region', 'region_id')
    .sort('region_id')
    .take(3)
    )
  • D. regions = dict(
    regions_df
    .select('region_id', 'region')
    .limit(3)
    .collect()
    )

Answer: C

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The question requires creating a dictionary where keys areregionvalues and values are the correspondingregion_idintegers. Furthermore, it asks to retrieve only the smallest 3region_idvalues.
Key observations:
select('region', 'region_id')puts the column order as expected bydict()- where the first column becomes the key and the second the value.
sort('region_id')ensures sorting in ascending order so the smallest IDs are first.
take(3)retrieves exactly 3 rows.
Wrapping the result indict(...)correctly builds the required Python dictionary:{ 'AFRICA': 0, 'AMERICA': 1,
'ASIA': 2 }.
Incorrect options:
Option B flips the order toregion_idfirst, resulting in a dictionary with integer keys - not what's asked.
Option C uses.limit(3)without sorting, which leads to non-deterministic rows based on partition layout.
Option D sorts in descending order, giving the largest rather than smallestregion_ids.
Hence, Option A meets all the requirements precisely.


NEW QUESTION # 29
A data engineer is running a batch processing job on a Spark cluster with the following configuration:
10 worker nodes
16 CPU cores per worker node
64 GB RAM per node
The data engineer wants to allocate four executors per node, each executor using four cores.
What is the total number of CPU cores used by the application?

  • A. 0
  • B. 1
  • C. 2
  • D. 3

Answer: A

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
If each of the 10 nodes runs 4 executors, and each executor is assigned 4 CPU cores:
Executors per node = 4
Cores per executor = 4
Total executors = 4 * 10 = 40
Total cores = 40 executors * 4 cores = 160 cores
However, Spark uses 1 core for overhead on each node when managing multiple executors. Thus, the practical allocation is:
Total usable executors = 4 executors/node × 10 nodes = 40
Total cores = 4 cores × 40 executors = 160
Answer is A - but the question asks specifically about "CPU cores used by the application," assuming all
allocated cores are usable (as Spark typically runs executors without internal core reservation unless explicitly configured).
However, if you are considering 4 executors/node × 4 cores = 16 cores per node, across 10 nodes, that's 160.
Final Answer: A


NEW QUESTION # 30
Given the following code snippet inmy_spark_app.py:

What is the role of the driver node?

  • A. The driver node stores the final result after computations are completed by worker nodes
  • B. The driver node only provides the user interface for monitoring the application
  • C. The driver node orchestrates the execution by transforming actions into tasks and distributing them to worker nodes
  • D. The driver node holds the DataFrame data and performs all computations locally

Answer: C

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
In the Spark architecture, the driver node is responsible for orchestrating the execution of a Spark application.
It converts user-defined transformations and actions into a logical plan, optimizes it into a physical plan, and then splits the plan into tasks that are distributed to the executor nodes.
As per Databricks and Spark documentation:
"The driver node is responsible for maintaining information about the Spark application, responding to a user's program or input, and analyzing, distributing, and scheduling work across the executors." This means:
Option A is correct because the driver schedules and coordinates the job execution.
Option B is incorrect because the driver does more than just UI monitoring.
Option C is incorrect since data and computations are distributed across executor nodes.
Option D is incorrect; results are returned to the driver but not stored long-term by it.
Reference: Databricks Certified Developer Spark 3.5 Documentation # Spark Architecture # Driver vs Executors.


NEW QUESTION # 31
A data engineer is working on a real-time analytics pipeline using Apache Spark Structured Streaming. The engineer wants to process incoming data and ensure that triggers control when the query is executed. The system needs to process data in micro-batches with a fixed interval of 5 seconds.
Which code snippet the data engineer could use to fulfil this requirement?
A)

B)

C)

D)

Options:

  • A. Uses trigger(continuous='5 seconds') - continuous processing mode.
  • B. Uses trigger() - default micro-batch trigger without interval.
  • C. Uses trigger(processingTime=5000) - invalid, as processingTime expects a string.
  • D. Uses trigger(processingTime='5 seconds') - correct micro-batch trigger with interval.

Answer: D

Explanation:
To define a micro-batch interval, the correct syntax is:
query = df.writeStream
outputMode("append")
trigger(processingTime='5 seconds')
start()
This schedules the query to execute every 5 seconds.
Continuous mode (used in Option A) is experimental and has limited sink support.
Option D is incorrect because processingTime must be a string (not an integer).
Option B triggers as fast as possible without interval control.
Reference:Spark Structured Streaming - Triggers


NEW QUESTION # 32
......

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