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真實的Associate-Developer-Apache-Spark-3.5證照指南＆準確的Databricks認證培訓 -有效的Databricks Databricks Certified Associate Developer for Apache Spark 3.5 - Python

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>> Associate-Developer-Apache-Spark-3.5證照指南 <<

Associate-Developer-Apache-Spark-3.5測試引擎，最新Associate-Developer-Apache-Spark-3.5試題

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最新的 Databricks Certification Associate-Developer-Apache-Spark-3.5 免費考試真題 (Q25-Q30):

問題 #25
A data engineer wants to create a Streaming DataFrame that reads from a Kafka topic called feed.

Which code fragment should be inserted in line 5 to meet the requirement?
Code context:
spark
.readStream
.format("kafka")
.option("kafka.bootstrap.servers","host1:port1,host2:port2")
.[LINE5]
.load()
Options:

A. .option("subscribe.topic", "feed")
B. .option("topic", "feed")
C. .option("kafka.topic", "feed")
D. .option("subscribe", "feed")

答案：D

解題說明：
Comprehensive and Detailed Explanation:
To read from a specific Kafka topic using Structured Streaming, the correct syntax is:
python
CopyEdit
option("subscribe","feed")
This is explicitly defined in the Spark documentation:
"subscribe - The Kafka topic to subscribe to. Only one topic can be specified for this option." (Source:Apache Spark Structured Streaming + Kafka Integration Guide)
B)."subscribe.topic" is invalid.
C)."kafka.topic" is not a recognized option.
D)."topic" is not valid for Kafka source in Spark.

問題 #26
A data engineer is working on the DataFrame:

(Referring to the table image: it has columnsId,Name,count, andtimestamp.) Which code fragment should the engineer use to extract the unique values in theNamecolumn into an alphabetically ordered list?

A. df.select("Name").distinct().orderBy(df["Name"])
B. df.select("Name").distinct()
C. df.select("Name").orderBy(df["Name"].asc())
D. df.select("Name").distinct().orderBy(df["Name"].desc())

答案：A

解題說明：
Comprehensive and Detailed Explanation From Exact Extract:
To extract unique values from a column and sort them alphabetically:
distinct()is required to remove duplicate values.
orderBy()is needed to sort the results alphabetically (ascending by default).
Correct code:
df.select("Name").distinct().orderBy(df["Name"])
This is directly aligned with standard DataFrame API usage in PySpark, as documented in the official Databricks Spark APIs. Option A is incorrect because it may not remove duplicates. Option C omits sorting.
Option D sorts in descending order, which doesn't meet the requirement for alphabetical (ascending) order.

問題 #27
An MLOps engineer is building a Pandas UDF that applies a language model that translates English strings into Spanish. The initial code is loading the model on every call to the UDF, which is hurting the performance of the data pipeline.
The initial code is:

def in_spanish_inner(df: pd.Series) -> pd.Series:
model = get_translation_model(target_lang='es')
return df.apply(model)
in_spanish = sf.pandas_udf(in_spanish_inner, StringType())
How can the MLOps engineer change this code to reduce how many times the language model is loaded?

A. Convert the Pandas UDF to a PySpark UDF
B. Convert the Pandas UDF from a Series # Series UDF to an Iterator[Series] # Iterator[Series] UDF
C. Run thein_spanish_inner()function in amapInPandas()function call
D. Convert the Pandas UDF from a Series # Series UDF to a Series # Scalar UDF

答案：B

解題說明：
Comprehensive and Detailed Explanation From Exact Extract:
The provided code defines a Pandas UDF of type Series-to-Series, where a new instance of the language modelis created on each call, which happens per batch. This is inefficient and results in significant overhead due to repeated model initialization.
To reduce the frequency of model loading, the engineer should convert the UDF to an iterator-based Pandas UDF (Iterator[pd.Series] -> Iterator[pd.Series]). This allows the model to be loaded once per executor and reused across multiple batches, rather than once per call.
From the official Databricks documentation:
"Iterator of Series to Iterator of Series UDFs are useful when the UDF initialization is expensive... For example, loading a ML model once per executor rather than once per row/batch."
- Databricks Official Docs: Pandas UDFs
Correct implementation looks like:
python
CopyEdit
@pandas_udf("string")
def translate_udf(batch_iter: Iterator[pd.Series]) -> Iterator[pd.Series]:
model = get_translation_model(target_lang='es')
for batch in batch_iter:
yield batch.apply(model)
This refactor ensures theget_translation_model()is invoked once per executor process, not per batch, significantly improving pipeline performance.

問題 #28
Which configuration can be enabled to optimize the conversion between Pandas and PySpark DataFrames using Apache Arrow?

A. spark.conf.set("spark.sql.execution.arrow.pyspark.enabled", "true")
B. spark.conf.set("spark.pandas.arrow.enabled", "true")
C. spark.conf.set("spark.sql.arrow.pandas.enabled", "true")
D. spark.conf.set("spark.sql.execution.arrow.enabled", "true")

答案：A

解題說明：
Comprehensive and Detailed Explanation From Exact Extract:
Apache Arrow is used under the hood to optimize conversion between Pandas and PySpark DataFrames. The correct configuration setting is:
spark.conf.set("spark.sql.execution.arrow.pyspark.enabled", "true")
From the official documentation:
"This configuration must be enabled to allow for vectorized execution and efficient conversion between Pandas and PySpark using Arrow." Option B is correct.
Options A, C, and D are invalid config keys and not recognized by Spark.
Final Answer: B

問題 #29
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 conversion will automatically distribute the data across worker nodes
C. The operation will load all data into the driver's memory, potentially causing memory overflow
D. The operation will fail if the Pandas DataFrame exceeds 1000 rows

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 #30
......

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最實用的Associate-Developer-Apache-Spark-3.5認證考試資料

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