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Associate-Developer-Apache-Spark-3.5専門知識内容、Associate-Developer-Apache-Spark-3.5対応受験

あなたはいまDatabricksのAssociate-Developer-Apache-Spark-3.5認定試験にどうやって合格できるかということで首を傾けているのですか。DatabricksのAssociate-Developer-Apache-Spark-3.5認定試験は現在のいろいろなIT認定試験における最も価値のある資格の一つです。ここ数十年間では、インターネット・テクノロジーは世界中の人々の注目を集めているのです。それがもう現代生活の不可欠な一部となりました。その中で、Databricksの認証資格は広範な国際的な認可を得ました。ですから、IT業界で仕事している皆さんはDatabricksの認定試験を受験して資格を取得することを通して、彼らの知識やスキルを向上させます。Associate-Developer-Apache-Spark-3.5認定試験はDatabricksの最も重要な試験の一つです。この資格は皆さんに大きな利益をもたらすことができます。

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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 (Q72-Q77):

質問 # 72
A data engineer is working with a large JSON dataset containing order information. The dataset is stored in a distributed file system and needs to be loaded into a Spark DataFrame for analysis. The data engineer wants to ensure that the schema is correctly defined and that the data is read efficiently.
Which approach should the data scientist use to efficiently load the JSON data into a Spark DataFrame with a predefined schema?

A. Define a StructType schema and use spark.read.schema(predefinedSchema).json() to load the data.
B. Use spark.read.json() to load the data, then use DataFrame.printSchema() to view the inferred schema, and finally use DataFrame.cast() to modify column types.
C. Use spark.read.format("json").load() and then use DataFrame.withColumn() to cast each column to the desired data type.
D. Use spark.read.json() with the inferSchema option set to true

正解：A

解説：
The most efficient and correct approach is to define a schema using StructType and pass it tospark.read.
schema(...).
This avoids schema inference overhead and ensures proper data types are enforced during read.
Example:
frompyspark.sql.typesimportStructType, StructField, StringType, DoubleType schema = StructType([ StructField("order_id", StringType(),True), StructField("amount", DoubleType(),True),
])
df = spark.read.schema(schema).json("path/to/json")
- Source:Databricks Guide - Read JSON with predefined schema

質問 # 73
A data engineer is asked to build an ingestion pipeline for a set of Parquet files delivered by an upstream team on a nightly basis. The data is stored in a directory structure with a base path of "/path/events/data". The upstream team drops daily data into the underlying subdirectories following the convention year/month/day.
A few examples of the directory structure are:

Which of the following code snippets will read all the data within the directory structure?

A. df = spark.read.option("inferSchema", "true").parquet("/path/events/data/")
B. df = spark.read.parquet("/path/events/data/")
C. df = spark.read.option("recursiveFileLookup", "true").parquet("/path/events/data/")
D. df = spark.read.parquet("/path/events/data/*")

正解：C

解説：
Comprehensive and Detailed Explanation From Exact Extract:
To read all files recursively within a nested directory structure, Spark requires therecursiveFileLookupoption to be explicitly enabled. According to Databricks official documentation, when dealing with deeply nested Parquet files in a directory tree (as shown in this example), you should set:
df = spark.read.option("recursiveFileLookup", "true").parquet("/path/events/data/") This ensures that Spark searches through all subdirectories under/path/events/data/and reads any Parquet files it finds, regardless of the folder depth.
Option A is incorrect because while it includes an option,inferSchemais irrelevant here and does not enable recursive file reading.
Option C is incorrect because wildcards may not reliably match deep nested structures beyond one directory level.
Option D is incorrect because it will only read files directly within/path/events/data/and not subdirectories like
/2023/01/01.
Databricks documentation reference:
"To read files recursively from nested folders, set therecursiveFileLookupoption to true. This is useful when data is organized in hierarchical folder structures" - Databricks documentation on Parquet files ingestion and options.

質問 # 74
A data engineer needs to write a Streaming DataFrame as Parquet files.
Given the code:

Which code fragment should be inserted to meet the requirement?
A)

Which code fragment should be inserted to meet the requirement?

A. .format("parquet")
.option("path", "path/to/destination/dir")
B. .option("format", "parquet")
.option("location", "path/to/destination/dir")
C. CopyEdit
.option("format", "parquet")
.option("destination", "path/to/destination/dir")
D. .format("parquet")
.option("location", "path/to/destination/dir")

正解：A

解説：
Comprehensive and Detailed Explanation From Exact Extract:
To write a structured streaming DataFrame to Parquet files, the correct way to specify the format and output directory is:
writeStream
format("parquet")
option("path", "path/to/destination/dir")
According to Spark documentation:
"When writing to file-based sinks (like Parquet), you must specify the path using the .option("path", ...) method. Unlike batch writes, .save() is not supported." Option A incorrectly uses.option("location", ...)(invalid for Parquet sink).
Option B incorrectly sets the format via.option("format", ...), which is not the correct method.
Option C repeats the same issue.
Option D is correct:.format("parquet")+.option("path", ...)is the required syntax.
Final Answer: D

質問 # 75
A data engineer wants to process a streaming DataFrame that receives sensor readings every second with columnssensor_id,temperature, andtimestamp. The engineer needs to calculate the average temperature for each sensor over the last 5 minutes while the data is streaming.
Which code implementation achieves the requirement?
Options from the images provided:

正解：C

解説：
Comprehensive and Detailed Explanation From Exact Extract:
The correct answer isDbecause it uses proper time-based window aggregation along with watermarking, which is the required pattern in Spark Structured Streaming for time-based aggregations over event-time data.
From the Spark 3.5 documentation on structured streaming:
"You can define sliding windows on event-time columns, and usegroupByalong withwindow()to compute aggregates over those windows. To deal with late data, you usewithWatermark()to specify how late data is allowed to arrive." (Source:Structured Streaming Programming Guide) In optionD, the use of:
python
CopyEdit
groupBy("sensor_id", window("timestamp","5 minutes"))
agg(avg("temperature").alias("avg_temp"))
ensures that for eachsensor_id, the average temperature is calculated over 5-minute event-time windows. To complete the logic, it is assumed thatwithWatermark("timestamp", "5 minutes")is used earlier in the pipeline to handle late events.
Explanation of why other options are incorrect:
Option AusesWindow.partitionBywhich applies to static DataFrames or batch queries and is not suitable for streaming aggregations.
Option Bdoes not apply a time window, thus does not compute the rolling average over 5 minutes.
Option Cincorrectly applieswithWatermark()after an aggregation and does not include any time window, thus missing the time-based grouping required.
Therefore,Option Dis the only one that meets all requirements for computing a time-windowed streaming aggregation.

質問 # 76
A data engineer wants to create an external table from a JSON file located at/data/input.jsonwith the following requirements:
Create an external table namedusers
Automatically infer schema
Merge records with differing schemas
Which code snippet should the engineer use?
Options:

A. CREATE EXTERNAL TABLE users USING json OPTIONS (path '/data/input.json', schemaMerge
'true')
B. CREATE TABLE users USING json OPTIONS (path '/data/input.json')
C. CREATE EXTERNAL TABLE users USING json OPTIONS (path '/data/input.json', mergeSchema
'true')
D. CREATE EXTERNAL TABLE users USING json OPTIONS (path '/data/input.json')

正解：C

解説：
To create an external table and enable schema merging, the correct syntax is:
CREATEEXTERNALTABLEusers
USINGjson
OPTIONS (
path'/data/input.json',
mergeSchema'true'
)
mergeSchemais the correct option key (notschemaMerge)
EXTERNALallows Spark to query files without managing their lifecycle
Reference:Spark SQL DDL - JSON and Schema Merging

質問 # 77
......

当社Databricksの専門家は長い間Associate-Developer-Apache-Spark-3.5試験に集中しており、新しい知識を見落とすことはありません。教材の内容は常に最新の状態に保たれています。 Associate-Developer-Apache-Spark-3.5学習ガイドの購入後に新しい情報が出ても心配する必要はありません。新しいバージョンがある場合は、メールでお知らせします。私たちの多大な努力により、私たちの教材はAssociate-Developer-Apache-Spark-3.5試験に絞られ、対象にされました。したがって、無駄なAssociate-Developer-Apache-Spark-3.5のDatabricks Certified Associate Developer for Apache Spark 3.5 - Python試験資料情報に時間を浪費することを心配する必要はありません。

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Associate-Developer-Apache-Spark-3.5学習教材のアフターサービスも専門家によって提供されます、その権威性は言うまでもありません、更新されたAssociate-Developer-Apache-Spark-3.5学習資料を得ることができ、取得方法、一週24時間のサービスは弊社の態度を示しています。

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