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最新的 Databricks Certification Associate-Developer-Apache-Spark-3.5 免費考試真題 (Q14-Q19):

問題 #14
What is the risk associated with this operation when converting a large Pandas API on Spark DataFrame back to a Pandas DataFrame?

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

答案：C

解題說明：
Comprehensive and Detailed Explanation From Exact Extract:
When you convert a largepyspark.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

問題 #15
The following code fragment results in an error:

Which code fragment should be used instead?

答案：C

問題 #16
A Spark application suffers from too many small tasks due to excessive partitioning. How can this be fixed without a full shuffle?
Options:

A. Use the sortBy() transformation to reorganize the data
B. Use the coalesce() transformation with a lower number of partitions
C. Use the distinct() transformation to combine similar partitions
D. Use the repartition() transformation with a lower number of partitions

答案：B

解題說明：
coalesce(n) reduces the number of partitions without triggering a full shuffle, unlike repartition().
This is ideal when reducing partition count, especially during write operations.
Reference:Spark API - coalesce

問題 #17
A data scientist wants each record in the DataFrame to contain:
The first attempt at the code does read the text files but each record contains a single line. This code is shown below:

The entire contents of a file
The full file path
The issue: reading line-by-line rather than full text per file.
Code:
corpus = spark.read.text("/datasets/raw_txt/*")
.select('*','_metadata.file_path')
Which change will ensure one record per file?
Options:

A. Add the option wholetext=True to the text() function
B. Add the option lineSep=", " to the text() function
C. Add the option lineSep=' ' to the text() function
D. Add the option wholetext=False to the text() function

答案：A

解題說明：
To read each file as a single record, use:
spark.read.text(path, wholetext=True)
This ensures that Spark reads the entire file contents into one row.
Reference:Spark read.text() with wholetext

問題 #18
A data engineer needs to write a DataFramedfto a Parquet file, partitioned by the columncountry, and overwrite any existing data at the destination path.
Which code should the data engineer use to accomplish this task in Apache Spark?

A. df.write.mode("overwrite").parquet("/data/output")
B. df.write.mode("overwrite").partitionBy("country").parquet("/data/output")
C. df.write.partitionBy("country").parquet("/data/output")
D. df.write.mode("append").partitionBy("country").parquet("/data/output")

答案：B

解題說明：
The.mode("overwrite")ensures that existing files at the path will be replaced.
partitionBy("country")optimizes queries by writing data into partitioned folders.
Correct syntax:
df.write.mode("overwrite").partitionBy("country").parquet("/data/output")
- Source:Spark SQL, DataFrames and Datasets Guide

問題 #19
......

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所以VCESoft Associate-Developer-Apache-Spark-3.5最新題庫得到了大家的信任，在這裏向廣大考生推薦這個最優秀的 Databricks 的 Associate-Developer-Apache-Spark-3.5 題庫參考資料，這是一個與真實考試一樣準確的練習題和答案相關的考試材料，也是一個能幫您通過 Databricks Associate-Developer-Apache-Spark-3.5 認證考試很好的選擇，而我們公司的Associate-Developer-Apache-Spark-3.5題庫恰巧能夠很好地解決這個問題，上面我們也提到了這套Databricks Associate-Developer-Apache-Spark-3.5題庫能夠幫助顧客更快速的通過考試，這個短時間就是只要練習我們公司的試題20〜30個小時就可以去參加Associate-Developer-Apache-Spark-3.5考試了，並且有高達98％通過率，Associate-Developer-Apache-Spark-3.5問題集練習中常見的一些錯誤，Databricks 最新Associate-Developer-Apache-Spark-3.5考證如果是的話，那麼你就不用再擔心不能通過考試了。

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準備充分的最新Associate-Developer-Apache-Spark-3.5考證和資格考試中的領先供應平臺＆更新的Associate-Developer-Apache-Spark-3.5：Databricks Certified Associate Developer for Apache Spark 3.5 - Python

而我們公司的Associate-Developer-Apache-Spark-3.5題庫恰巧能夠很好地解決這個問題，上面我們也提到了這套Databricks Associate-Developer-Apache-Spark-3.5題庫能夠幫助顧客更快速的通過考試，這個短時間就是只要練習我們公司的試題20〜30個小時就可以去參加Associate-Developer-Apache-Spark-3.5考試了，並且有高達98％通過率。

Associate-Developer-Apache-Spark-3.5問題集練習中常見的一些錯誤，如果是的話，那麼你就不用再擔心不能通過考試了。

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高效的最新Associate-Developer-Apache-Spark-3.5考證|高通過率的考試材料|專業的Associate-Developer-Apache-Spark-3.5：Databricks Certified Associate Developer for Apache Spark 3.5 - Python

Associate-Developer-Apache-Spark-3.5最新題庫 & 免費下載Associate-Developer-Apache-Spark-3.5考題

最新的 Databricks Certification Associate-Developer-Apache-Spark-3.5 免費考試真題 (Q14-Q19):

準備充分的最新Associate-Developer-Apache-Spark-3.5考證和資格考試中的領先供應平臺＆更新的Associate-Developer-Apache-Spark-3.5：Databricks Certified Associate Developer for Apache Spark 3.5 - Python