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利用Associate-Developer-Apache-Spark-3.5更新 -擺脫Databricks Certified Associate Developer for Apache Spark 3.5 - Python考試困擾

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>> Associate-Developer-Apache-Spark-3.5更新 <<

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最新的 Databricks Certification Associate-Developer-Apache-Spark-3.5 免費考試真題 (Q20-Q25):

問題 #20
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", substring_index(col("email"), "@", 1))
.withColumn("domain", substring_index(col("email"), "@", -1))
B. customerDF.withColumn("username", split(col("email"), "@").getItem(0))
.withColumn("domain", split(col("email"), "@").getItem(1))
C. customerDF.select(
regexp_replace(col("email"), "@", "").alias("username"),
regexp_replace(col("email"), "@", "").alias("domain")
)
D. customerDF.select(
col("email").substr(0, 5).alias("username"),
col("email").substr(-5).alias("domain")
)

答案：B

解題說明：
Comprehensive and Detailed Explanation From Exact Extract:
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.

問題 #21
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 distinct() transformation to combine similar partitions
B. Use the repartition() transformation with a lower number of partitions
C. Use the sortBy() transformation to reorganize the data
D. Use the coalesce() transformation with a lower number of partitions

答案：D

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 #22
A data engineer observes that an upstream streaming source sends duplicate records, where duplicates share the same key and have at most a 30-minute difference inevent_timestamp. The engineer adds:
dropDuplicatesWithinWatermark("event_timestamp", "30 minutes")
What is the result?

A. It removes all duplicates regardless of when they arrive
B. It is not able to handle deduplication in this scenario
C. It removes duplicates that arrive within the 30-minute window specified by the watermark
D. It accepts watermarks in seconds and the code results in an error

答案：C

解題說明：
Comprehensive and Detailed Explanation From Exact Extract:
The methoddropDuplicatesWithinWatermark()in Structured Streaming drops duplicate records based on a specified column and watermark window. The watermark defines the threshold for how late data is considered valid.
From the Spark documentation:
"dropDuplicatesWithinWatermark removes duplicates that occur within the event-time watermark window." In this case, Spark will retain the first occurrence and drop subsequent records within the 30-minute watermark window.
Final Answer: B

問題 #23
Which feature of Spark Connect is considered when designing an application to enable remote interaction with the Spark cluster?

A. It allows for remote execution of Spark jobs
B. It provides a way to run Spark applications remotely in any programming language
C. It is primarily used for data ingestion into Spark from external sources
D. It can be used to interact with any remote cluster using the REST API

答案：A

解題說明：
Comprehensive and Detailed Explanation:
Spark Connect introduces a decoupled client-server architecture. Its key feature is enabling Spark job submission and execution from remote clients - in Python, Java, etc.
From Databricks documentation:
"Spark Connect allows remote clients to connect to a Spark cluster and execute Spark jobs without being co- located with the Spark driver." A is close, but "any language" is overstated (currently supports Python, Java, etc., not literally all).
B refers to REST, which is not Spark Connect's mechanism.
D is incorrect; Spark Connect isn't focused on ingestion.
Final Answer: C

問題 #24
A data engineer wants to write a Spark job that creates a new managed table. If the table already exists, the job should fail and not modify anything.
Which save mode and method should be used?

A. saveAsTable with mode ErrorIfExists
B. saveAsTable with mode Overwrite
C. save with mode Ignore
D. save with mode ErrorIfExists

答案：A

解題說明：
Comprehensive and Detailed Explanation:
The methodsaveAsTable()creates a new table and optionally fails if the table exists.
From Spark documentation:
"The mode 'ErrorIfExists' (default) will throw an error if the table already exists." Thus:
Option A is correct.
Option B (Overwrite) would overwrite existing data - not acceptable here.
Option C and D usesave(), which doesn't create a managed table with metadata in the metastore.
Final Answer: A

問題 #25
......

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Oliver Lee Oliver Lee

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