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Associate-Developer-Apache-Spark-3.5絶対合格 & Associate-Developer-Apache-Spark-3.5テスト模擬問題集

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Associate-Developer-Apache-Spark-3.5テスト模擬問題集、Associate-Developer-Apache-Spark-3.5資格参考書

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Databricks Certified Associate Developer for Apache Spark 3.5 - Python 認定 Associate-Developer-Apache-Spark-3.5 試験問題 (Q11-Q16):

質問 # 11
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(how='all', thresh=None)
B. filtered_df = users_raw_df.na.drop(thresh=0)
C. filtered_df = users_raw_df.na.drop(how='all')
D. filtered_df = users_raw_df.na.drop(how='any')

正解：D

解説：
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()

質問 # 12
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 to a fixed value, such as 200
B. Set the number of partitions by dividing the dataset size (1 TB) by a reasonable partition size, such as
128 MB
C. Set the number of partitions equal to the total number of CPUs in the cluster
D. Set the number of partitions equal to the number of nodes in the cluster

正解：B

解説：
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

質問 # 13
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 increases the partition size for df1 and df2.
C. It reduces the number of shuffle operations by replicating the smaller DataFrame to all nodes.
D. It ensures that the join happens only when the id values are identical.

正解：C

解説：
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

質問 # 14
A data engineer uses a broadcast variable to share a DataFrame containing millions of rows across executors for lookup purposes. What will be the outcome?

A. The job will hang indefinitely as Spark will struggle to distribute and serialize such a large broadcast variable to all executors
B. The job may fail if the executors do not have enough CPU cores to process the broadcasted dataset
C. The job may fail because the driver does not have enough CPU cores to serialize the large DataFrame
D. The job may fail if the memory on each executor is not large enough to accommodate the DataFrame being broadcasted

正解：D

解説：
Comprehensive and Detailed Explanation From Exact Extract:
In Apache Spark, broadcast variables are used to efficiently distribute large, read-only data to all worker nodes. However, broadcasting very large datasets can lead to memory issues on executors if the data does not fit into the available memory.
According to the Spark documentation:
"Broadcast variables allow the programmer to keep a read-only variable cached on each machine rather than shipping a copy of it with tasks. This can greatly reduce the amount of data sent over the network." However, it also notes:
"Using the broadcast functionality available in SparkContext can greatly reduce the size of each serialized task, and the cost of launching a job over a cluster. If your tasks use any large object from the driver program inside of them (e.g., a static lookup table), consider turning it into a broadcast variable." But caution is advised when broadcasting large datasets:
"Broadcasting large variables can cause out-of-memory errors if the data does not fit in the memory of each executor." Therefore, if the broadcasted DataFrame containing millions of rows exceeds the memory capacity of the executors, the job may fail due to memory constraints.
Reference:Spark 3.5.5 Documentation - Tuning

質問 # 15
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. The script is first divided into multiple applications, then each application is split into jobs, stages, and finally tasks.
B. 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.
C. 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.
D. 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.

正解：B

解説：
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.

質問 # 16
......

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