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Databricks Associate-Developer-Apache-Spark-3.5関連問題資料 & Associate-Developer-Apache-Spark-3.5模擬問題

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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 (Q25-Q30):

質問 # 25
A data analyst wants to add a column date derived from a timestamp column.
Options:

A. dates_df.withColumn("date", f.date_format("timestamp", "yyyy-MM-dd")).show()
B. dates_df.withColumn("date", f.from_unixtime("timestamp")).show()
C. dates_df.withColumn("date", f.unix_timestamp("timestamp")).show()
D. dates_df.withColumn("date", f.to_date("timestamp")).show()

正解：D

解説：
f.to_date() converts a timestamp or string to a DateType.
Ideal for extracting the date component (year-month-day) from a full timestamp.
Example:
frompyspark.sql.functionsimportto_date
dates_df.withColumn("date", to_date("timestamp"))
Reference:Spark SQL Date Functions

質問 # 26
A data scientist is working on a large dataset in Apache Spark using PySpark. The data scientist has a DataFramedfwith columnsuser_id,product_id, andpurchase_amountand needs to perform some operations on this data efficiently.
Which sequence of operations results in transformations that require a shuffle followed by transformations that do not?

A. df.withColumn("discount", df.purchase_amount * 0.1).select("discount")
B. df.withColumn("purchase_date", current_date()).where("total_purchase > 50")
C. df.groupBy("user_id").agg(sum("purchase_amount").alias("total_purchase")).repartition(10)
D. df.filter(df.purchase_amount > 100).groupBy("user_id").sum("purchase_amount")

正解：C

解説：
Comprehensive and Detailed Explanation From Exact Extract:
Shuffling occurs in operations likegroupBy,reduceByKey, orjoin-which cause data to be moved across partitions. Therepartition()operation can also cause a shuffle, but in this context, it follows an aggregation.
InOption D, thegroupByfollowed byaggresults in a shuffle due to grouping across nodes.
Therepartition(10)is a partitioning transformation but does not involve a new shuffle since the data is already grouped.
This sequence - shuffle (groupBy) followed by non-shuffling (repartition) - is correct.
Option A does the opposite: thefilterdoes not cause a shuffle, butgroupBydoes - this makes it the wrong order.

質問 # 27
A developer notices that all the post-shuffle partitions in a dataset are smaller than the value set forspark.sql.
adaptive.maxShuffledHashJoinLocalMapThreshold.
Which type of join will Adaptive Query Execution (AQE) choose in this case?

A. A Cartesian join
B. A shuffled hash join
C. A sort-merge join
D. A broadcast nested loop join

正解：B

解説：
Comprehensive and Detailed Explanation From Exact Extract:
Adaptive Query Execution (AQE) dynamically selects join strategies based on actual data sizes at runtime. If the size of post-shuffle partitions is below the threshold set by:
spark.sql.adaptive.maxShuffledHashJoinLocalMapThreshold
then Spark prefers to use a shuffled hash join.
From the Spark documentation:
"AQE selects a shuffled hash join when the size of post-shuffle data is small enough to fit within the configured threshold, avoiding more expensive sort-merge joins." Therefore:
A is wrong - Cartesian joins are only used with no join condition.
B is correct - this is the optimized join for small partitioned shuffle data under AQE.
C and D are used under other scenarios but not for this case.
Final Answer: B

質問 # 28
A developer is working with a pandas DataFrame containing user behavior data from a web application.
Which approach should be used for executing agroupByoperation in parallel across all workers in Apache Spark 3.5?
A)
Use the applylnPandas API
B)

A. Use theapplyInPandasAPI:
df.groupby("user_id").applyInPandas(mean_func, schema="user_id long, value double").show()
B. Use themapInPandasAPI:
df.mapInPandas(mean_func, schema="user_id long, value double").show()
C. Use a Pandas UDF:
@pandas_udf("double")
def mean_func(value: pd.Series) -> float:
return value.mean()
df.groupby("user_id").agg(mean_func(df["value"])).show()
D. Use a regular Spark UDF:
from pyspark.sql.functions import mean
df.groupBy("user_id").agg(mean("value")).show()

正解：A

解説：
Comprehensive and Detailed Explanation From Exact Extract:
The correct approach to perform a parallelizedgroupByoperation across Spark worker nodes using Pandas API is viaapplyInPandas. This function enables grouped map operations using Pandas logic in a distributed Spark environment. It applies a user-defined function to each group of data represented as a Pandas DataFrame.
As per the Databricks documentation:
"applyInPandas()allows for vectorized operations on grouped data in Spark. It applies a user-defined function to each group of a DataFrame and outputs a new DataFrame. This is the recommended approach for using Pandas logic across grouped data with parallel execution." Option A is correct and achieves this parallel execution.
Option B (mapInPandas) applies to the entire DataFrame, not grouped operations.
Option C uses built-in aggregation functions, which are efficient but not customizable with Pandas logic.
Option D creates a scalar Pandas UDF which does not perform a group-wise transformation.
Therefore, to run agroupBywith parallel Pandas logic on Spark workers, Option A usingapplyInPandasis the only correct answer.
Reference: Apache Spark 3.5 Documentation # Pandas API on Spark # Grouped Map Pandas UDFs (applyInPandas)

質問 # 29
Which UDF implementation calculates the length of strings in a Spark DataFrame?

A. spark.udf.register("stringLength", lambda s: len(s))
B. df.withColumn("length", spark.udf("len", StringType()))
C. df.withColumn("length", udf(lambda s: len(s), StringType()))
D. df.select(length(col("stringColumn")).alias("length"))

正解：D

解説：
Comprehensive and Detailed Explanation From Exact Extract:
Option B uses Spark's built-in SQL function length(), which is efficient and avoids the overhead of a Python UDF:
from pyspark.sql.functions import length, col
df.select(length(col("stringColumn")).alias("length"))
Explanation of other options:
Option A is incorrect syntax;spark.udfis not called this way.
Option C registers a UDF but doesn't apply it in the DataFrame transformation.
Option D is syntactically valid but uses a Python UDF which is less efficient than built-in functions.
Final Answer: B

質問 # 30
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