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Databricks Associate-Developer-Apache-Spark-3.5練習問題 & Associate-Developer-Apache-Spark-3.5トレーニング費用

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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 (Q41-Q46):

質問 # 41
What is the risk associated with this operation when converting a large Pandas API on Spark DataFrame back to a Pandas DataFrame?

A. Data will be lost during conversion
B. The operation will load all data into the driver's memory, potentially causing memory overflow
C. The conversion will automatically distribute the data across worker nodes
D. The operation will fail if the Pandas DataFrame exceeds 1000 rows

正解：B

解説：
When you convert a large pyspark.pandas (aka Pandas API on Spark) DataFrame to a local Pandas DataFrame using .toPandas(), Spark collects all partitions to the driver.
From the Spark documentation:
"Be careful when converting large datasets to Pandas. The entire dataset will be pulled into the driver's memory." Thus, for large datasets, this can cause memory overflow or out-of-memory errors on the driver.
Final answer: D

質問 # 42
22 of 55.
A Spark application needs to read multiple Parquet files from a directory where the files have differing but compatible schemas.
The data engineer wants to create a DataFrame that includes all columns from all files.
Which code should the data engineer use to read the Parquet files and include all columns using Apache Spark?

A. spark.read.parquet("/data/parquet/").option("mergeAllCols", True)
B. spark.read.parquet("/data/parquet/")
C. spark.read.option("mergeSchema", True).parquet("/data/parquet/")
D. spark.read.format("parquet").option("inferSchema", "true").load("/data/parquet/")

正解：C

解説：
When reading Parquet files, Spark infers a unified schema automatically only if all files share identical structures.
If files have different but compatible schemas, you must enable schema merging by setting the option mergeSchema=True.
Correct syntax:
df = spark.read.option("mergeSchema", True).parquet("/data/parquet/")
This option ensures Spark merges all discovered fields across Parquet files into one unified DataFrame schema.
Why the other options are incorrect:
A: Loads files but ignores extra columns - uses only the first file's schema.
C: inferSchema applies to CSV/JSON, not Parquet.
D: mergeAllCols is not a valid Spark option.
Reference:
Spark SQL Data Sources - Parquet options (mergeSchema, path).
Databricks Exam Guide (June 2025): Section "Using Spark DataFrame APIs" - reading/writing DataFrames with schema evolution and merging.

質問 # 43
A Spark engineer must select an appropriate deployment mode for the Spark jobs.
What is the benefit of using cluster mode in Apache Spark™?

A. In cluster mode, resources are allocated from a resource manager on the cluster, enabling better performance and scalability for large jobs
B. In cluster mode, the driver runs on the client machine, which can limit the application's ability to handle large datasets efficiently.
C. In cluster mode, the driver program runs on one of the worker nodes, allowing the application to fully utilize the distributed resources of the cluster.
D. In cluster mode, the driver is responsible for executing all tasks locally without distributing them across the worker nodes.

正解：C

解説：
In Apache Spark's cluster mode:
"The driver program runs on the cluster's worker node instead of the client's local machine. This allows the driver to be close to the data and other executors, reducing network overhead and improving fault tolerance for production jobs." (Source: Apache Spark documentation - Cluster Mode Overview)
"The driver program runs on the cluster's worker node instead of the client's local machine. This allows the driver to be close to the data and other executors, reducing network overhead and improving fault tolerance for production jobs." (Source: Apache Spark documentation - Cluster Mode Overview) This deployment is ideal for production environments where the job is submitted from a gateway node, and Spark manages the driver lifecycle on the cluster itself.
Option A is partially true but less specific than D.
Option B is incorrect: the driver never executes all tasks; executors handle distributed tasks.
Option C describes client mode, not cluster mode.

質問 # 44
An engineer has two DataFrames: df1 (small) and df2 (large). A broadcast join is used:
python
CopyEdit
from pyspark.sql.functions import broadcast
result = df2.join(broadcast(df1), on='id', how='inner')
What is the purpose of using broadcast() in this scenario?
Options:

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

正解：D

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.

質問 # 45
A data scientist is working with a Spark DataFrame called customerDF that contains customer information. The DataFrame has a column named email with customer email addresses. The data scientist needs to split this column into username and domain parts.
Which code snippet splits the email column into username and domain columns?

A. customerDF.withColumn("username", split(col("email"), "@").getItem(0))
.withColumn("domain", split(col("email"), "@").getItem(1))
B. customerDF.select(
col("email").substr(0, 5).alias("username"),
col("email").substr(-5).alias("domain")
)
C. customerDF.withColumn("username", substring_index(col("email"), "@", 1))
.withColumn("domain", substring_index(col("email"), "@", -1))
D. customerDF.select(
regexp_replace(col("email"), "@", "").alias("username"),
regexp_replace(col("email"), "@", "").alias("domain")
)

正解：A

解説：
Option B is the correct and idiomatic approach in PySpark to split a string column (like email) based on a delimiter such as "@".
The split(col("email"), "@") function returns an array with two elements: username and domain.
getItem(0) retrieves the first part (username).
getItem(1) retrieves the second part (domain).
withColumn() is used to create new columns from the extracted values.
Example from official Databricks Spark documentation on splitting columns:
from pyspark.sql.functions import split, col
df.withColumn("username", split(col("email"), "@").getItem(0))
.withColumn("domain", split(col("email"), "@").getItem(1))
Why other options are incorrect:
A uses fixed substring indices (substr(0, 5)), which won't correctly extract usernames and domains of varying lengths.
C uses substring_index, which is available but less idiomatic for splitting emails and is slightly less readable.
D removes "@" from the email entirely, losing the separation between username and domain, and ends up duplicating values in both fields.
Therefore, Option B is the most accurate and reliable solution according to Apache Spark 3.5 best practices.

質問 # 46
......

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Associate-Developer-Apache-Spark-3.5トレーニング費用: https://www.it-passports.com/Associate-Developer-Apache-Spark-3.5.html

Databricks Associate-Developer-Apache-Spark-3.5練習問題さまざまなテキストタイプと、デモでそれらにアプローチする最善の方法を認識することは非常に重要です、Associate-Developer-Apache-Spark-3.5試験問題のさまざまなバージョンを知りたい場合があります、Databricks Associate-Developer-Apache-Spark-3.5練習問題この試験に合格すれば君の専門知識がとても強いを証明し得ます、他のサイトと比較して、It-Passports Associate-Developer-Apache-Spark-3.5トレーニング費用は皆さんにもっと信頼されています、Databricks Associate-Developer-Apache-Spark-3.5練習問題前へ進みたくないですか、Databricks Associate-Developer-Apache-Spark-3.5練習問題自分の練習を通して、試験のまえにうろたえないでしょう、IT業界では広く認可されている試験として、Associate-Developer-Apache-Spark-3.5認定試験はDatabricksの中の最も重要な試験の一つです。

そこで大の男が上司の指先を咥えたまま上目遣いで微笑んでいるのだ、この近くの質屋の親父です質屋、さまざまなテキストタイプと、デモでそれらにアプローチする最善の方法を認識することは非常に重要です、Associate-Developer-Apache-Spark-3.5試験問題のさまざまなバージョンを知りたい場合があります。

認定するAssociate-Developer-Apache-Spark-3.5練習問題一回合格-ハイパスレートのAssociate-Developer-Apache-Spark-3.5トレーニング費用

この試験に合格すれば君の専門知識がとても強いを証明しAssociate-Developer-Apache-Spark-3.5得ます、他のサイトと比較して、It-Passportsは皆さんにもっと信頼されています、前へ進みたくないですか。

P.S.It-PassportsがGoogle Driveで共有している無料の2025 Databricks Associate-Developer-Apache-Spark-3.5ダンプ：https://drive.google.com/open?id=12rnEpGFYyvKOee9hgQteBonyBe3Wxq6h

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