Associate-Developer-Apache-Spark-3.5 PDF考古題

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最新的 Databricks Certification Associate-Developer-Apache-Spark-3.5 免費考試真題 (Q80-Q85):

問題 #80
A data scientist is analyzing a large dataset and has written a PySpark script that includes several transformations and actions on a DataFrame. The script ends with acollect()action to retrieve the results.
How does Apache Spark™'s execution hierarchy process the operations when the data scientist runs this script?

A. Thecollect()action triggers a job, which is divided into stages at shuffle boundaries, and each stage is split into tasks that operate on individual data partitions.
B. The script is first divided into multiple applications, then each application is split into jobs, stages, and finally tasks.
C. The entire script is treated as a single job, which is then divided into multiple stages, and each stage is further divided into tasks based on data partitions.
D. Spark creates a single task for each transformation and action in the script, and these tasks are grouped into stages and jobs based on their dependencies.

答案：A

解題說明：
Comprehensive and Detailed Explanation From Exact Extract:
In Apache Spark, the execution hierarchy is structured as follows:
Application: The highest-level unit, representing the user program built on Spark.
Job: Triggered by an action (e.g.,collect(),count()). Each action corresponds to a job.
Stage: A job is divided into stages based on shuffle boundaries. Each stage contains tasks that can be executed in parallel.
Task: The smallest unit of work, representing a single operation applied to a partition of the data.
When thecollect()action is invoked, Spark initiates a job. This job is then divided into stages at points where data shuffling is required (i.e., wide transformations). Each stage comprises tasks that are distributed across the cluster's executors, operating on individual data partitions.
This hierarchical execution model allows Spark to efficiently process large-scale data by parallelizing tasks and optimizing resource utilization.

問題 #81
An engineer has two DataFrames: df1 (small) and df2 (large). A broadcast join is used:
python
CopyEdit
frompyspark.sql.functionsimportbroadcast
result = df2.join(broadcast(df1), on='id', how='inner')
What is the purpose of using broadcast() in this scenario?
Options:

A. It filters the id values before performing the join.
B. It ensures that the join happens only when the id values are identical.
C. It increases the partition size for df1 and df2.
D. It reduces the number of shuffle operations by replicating the smaller DataFrame to all nodes.

答案：D

解題說明：
broadcast(df1) tells Spark to send the small DataFrame (df1) to all worker nodes.
This eliminates the need for shuffling df1 during the join.
Broadcast joins are optimized for scenarios with one large and one small table.
Reference:Spark SQL Performance Tuning Guide - Broadcast Joins

問題 #82
What is the relationship between jobs, stages, and tasks during execution in Apache Spark?
Options:

A. A stage contains multiple tasks, and each task contains multiple jobs.
B. A job contains multiple tasks, and each task contains multiple stages.
C. A job contains multiple stages, and each stage contains multiple tasks.
D. A stage contains multiple jobs, and each job contains multiple tasks.

答案：C

解題說明：
A Sparkjobis triggered by an action (e.g., count, show).
The job is broken intostages, typically one per shuffle boundary.
Eachstageis divided into multipletasks, which are distributed across worker nodes.
Reference:Spark Execution Model

問題 #83
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

答案：C

解題說明：
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

問題 #84
Given the code fragment:

import pyspark.pandas as ps
psdf = ps.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
Which method is used to convert a Pandas API on Spark DataFrame (pyspark.pandas.DataFrame) into a standard PySpark DataFrame (pyspark.sql.DataFrame)?

A. psdf.to_pandas()
B. psdf.to_spark()
C. psdf.to_dataframe()
D. psdf.to_pyspark()

答案：B

解題說明：
Comprehensive and Detailed Explanation From Exact Extract:
Pandas API on Spark (pyspark.pandas) allows interoperability with PySpark DataFrames. To convert apyspark.pandas.DataFrameto a standard PySpark DataFrame, you use.to_spark().
Example:
df = psdf.to_spark()
This is the officially supported method as per Databricks Documentation.
Incorrect options:
B, D: Invalid or nonexistent methods.
C: Converts to a local pandas DataFrame, not a PySpark DataFrame.

問題 #85
......

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