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Associate-Developer-Apache-Spark-3.5最新関連参考書 & Associate-Developer-Apache-Spark-3.5日本語試験対策

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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 (Q54-Q59):

質問 # 54
What is a feature of Spark Connect?

A. It supports DataStreamReader, DataStreamWriter, StreamingQuery, and Streaming APIs
B. Supports DataFrame, Functions, Column, SparkContext PySpark APIs
C. It has built-in authentication
D. It supports only PySpark applications

正解：A

解説：
Spark Connect is a client-server architecture introduced in Apache Spark 3.4, designed to decouple the client from the Spark driver, enabling remote connectivity to Spark clusters.
According to the Spark 3.5.5 documentation:
"Majority of the Streaming API is supported, including DataStreamReader, DataStreamWriter, StreamingQuery and StreamingQueryListener." This indicates that Spark Connect supports key components of Structured Streaming, allowing for robust streaming data processing capabilities.
Regarding other options:
B . While Spark Connect supports DataFrame, Functions, and Column APIs, it does not support SparkContext and RDD APIs.
C . Spark Connect supports multiple languages, including PySpark and Scala, not just PySpark.
D . Spark Connect does not have built-in authentication but is designed to work seamlessly with existing authentication infrastructures.

質問 # 55
What is a feature of Spark Connect?

A. It supports DataStreamReader, DataStreamWriter, StreamingQuery, and Streaming APIs
B. Supports DataFrame, Functions, Column, SparkContext PySpark APIs
C. It has built-in authentication
D. It supports only PySpark applications

正解：A

解説：
Comprehensive and Detailed Explanation From Exact Extract:
Spark Connect is a client-server architecture introduced in Apache Spark 3.4, designed to decouple the client from the Spark driver, enabling remote connectivity to Spark clusters.
According to the Spark 3.5.5 documentation:
"Majority of the Streaming API is supported, including DataStreamReader, DataStreamWriter, StreamingQuery and StreamingQueryListener." This indicates that Spark Connect supports key components of Structured Streaming, allowing for robust streaming data processing capabilities.
Regarding other options:
B).While Spark Connect supports DataFrame, Functions, and Column APIs, it does not support SparkContext and RDD APIs.
C).Spark Connect supports multiple languages, including PySpark and Scala, not just PySpark.
D).Spark Connect does not have built-in authentication but is designed to work seamlessly with existing authentication infrastructures.

質問 # 56
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)

Options:

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

正解：A

解説：
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

質問 # 57
A developer is trying to join two tables,sales.purchases_fctandsales.customer_dim, using the following code:

fact_df = purch_df.join(cust_df, F.col('customer_id') == F.col('custid')) The developer has discovered that customers in thepurchases_fcttable that do not exist in thecustomer_dimtable are being dropped from the joined table.
Which change should be made to the code to stop these customer records from being dropped?

A. fact_df = cust_df.join(purch_df, F.col('customer_id') == F.col('custid'))
B. fact_df = purch_df.join(cust_df, F.col('customer_id') == F.col('custid'), 'right_outer')
C. fact_df = purch_df.join(cust_df, F.col('cust_id') == F.col('customer_id'))
D. fact_df = purch_df.join(cust_df, F.col('customer_id') == F.col('custid'), 'left')

正解：D

解説：
Comprehensive and Detailed Explanation From Exact Extract:
In Spark, the default join type is an inner join, which returns only the rows with matching keys in both DataFrames. To retain all records from the left DataFrame (purch_df) and include matching records from the right DataFrame (cust_df), a left outer join should be used.
By specifying the join type as'left', the modified code ensures that all records frompurch_dfare preserved, and matching records fromcust_dfare included. Records inpurch_dfwithout a corresponding match incust_dfwill havenullvalues for the columns fromcust_df.
This approach is consistent with standard SQL join operations and is supported in PySpark's DataFrame API.

質問 # 58
A data engineer is working on a Streaming DataFrame streaming_df with the given streaming data:

Which operation is supported with streamingdf ?

A. streaming_df.groupby("Id") .count ()
B. streaming_df.orderBy("timestamp").limit(4)
C. streaming_df.filter (col("count") < 30).show()
D. streaming_df. select (countDistinct ("Name") )

正解：A

解説：
In Structured Streaming, only a limited subset of operations is supported due to the nature of unbounded data. Operations like sorting (orderBy) and global aggregation (countDistinct) require a full view of the dataset, which is not possible with streaming data unless specific watermarks or windows are defined.
Review of Each Option:
A: select(countDistinct("Name"))
Not allowed - Global aggregation like countDistinct() requires the full dataset and is not supported directly in streaming without watermark and windowing logic.
Reference: Databricks Structured Streaming Guide - Unsupported Operations.
B: groupby("Id").count()
Supported - Streaming aggregations over a key (like groupBy("Id")) are supported. Spark maintains intermediate state for each key.
Reference: Databricks Docs → Aggregations in Structured Streaming (https://docs.databricks.com/structured-streaming/aggregation.html) C . orderBy("timestamp").limit(4)
Not allowed - Sorting and limiting require a full view of the stream (which is infinite), so this is unsupported in streaming DataFrames.
Reference: Spark Structured Streaming - Unsupported Operations (ordering without watermark/window not allowed).
D: filter(col("count") < 30).show()
Not allowed - show() is a blocking operation used for debugging batch DataFrames; it's not allowed on streaming DataFrames.
Reference: Structured Streaming Programming Guide - Output operations like show() are not supported.
Reference Extract from Official Guide:
"Operations like orderBy, limit, show, and countDistinct are not supported in Structured Streaming because they require the full dataset to compute a result. Use groupBy(...).agg(...) instead for incremental aggregations."
- Databricks Structured Streaming Programming Guide

質問 # 59
......

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