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Associate-Developer-Apache-Spark-3.5考題寶典 - Associate-Developer-Apache-Spark-3.5熱門題庫

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最新的 Databricks Certification Associate-Developer-Apache-Spark-3.5 免費考試真題 (Q19-Q24):

問題 #19
A data engineer noticed improved performance after upgrading from Spark 3.0 to Spark 3.5. The engineer found that Adaptive Query Execution (AQE) was enabled.
Which operation is AQE implementing to improve performance?

A. Improving the performance of single-stage Spark jobs
B. Dynamically switching join strategies
C. Optimizing the layout of Delta files on disk
D. Collecting persistent table statistics and storing them in the metastore for future use

答案：B

解題說明：
Comprehensive and Detailed Explanation:
Adaptive Query Execution (AQE) is a Spark 3.x feature that dynamically optimizes query plans at runtime.
One of its core features is:
Dynamically switching join strategies (e.g., from sort-merge to broadcast) based on runtime statistics.
Other AQE capabilities include:
Coalescing shuffle partitions
Skew join handling
Option A is correct.
Option B refers to statistics collection, which is not AQE's primary function.
Option C is too broad and not AQE-specific.
Option D refers to Delta Lake optimizations, unrelated to AQE.
Final Answer: A

問題 #20
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 shuffled hash join
B. A broadcast nested loop join
C. A Cartesian join
D. A sort-merge join

答案：A

解題說明：
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

問題 #21
Which command overwrites an existing JSON file when writing a DataFrame?

A. df.write.json("path/to/file", overwrite=True)
B. df.write.mode("overwrite").json("path/to/file")
C. df.write.format("json").save("path/to/file", mode="overwrite")
D. df.write.overwrite.json("path/to/file")

答案：B

解題說明：
The correct way to overwrite an existing file using the DataFrameWriter is:
df.write.mode("overwrite").json("path/to/file")
Option D is also technically valid, but Option A is the most concise and idiomatic PySpark syntax.
Reference:PySpark DataFrameWriter API

問題 #22
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("append").partitionBy("country").parquet("/data/output")
B. df.write.mode("overwrite").parquet("/data/output")
C. df.write.mode("overwrite").partitionBy("country").parquet("/data/output")
D. df.write.partitionBy("country").parquet("/data/output")

答案：C

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 #23
A data engineer is reviewing a Spark application that applies several transformations to a DataFrame but notices that the job does not start executing immediately.
Which two characteristics of Apache Spark's execution model explain this behavior?
Choose 2 answers:

A. The Spark engine requires manual intervention to start executing transformations.
B. Only actions trigger the execution of the transformation pipeline.
C. Transformations are executed immediately to build the lineage graph.
D. Transformations are evaluated lazily.
E. The Spark engine optimizes the execution plan during the transformations, causing delays.

答案：B,D

解題說明：
Comprehensive and Detailed Explanation From Exact Extract:
Apache Spark employs a lazy evaluation model for transformations. This means that when transformations (e.
g.,map(),filter()) are applied to a DataFrame, Spark does not execute them immediately. Instead, it builds a logical plan (lineage) of transformations to be applied.
Execution is deferred until an action (e.g.,collect(),count(),save()) is called. At that point, Spark's Catalyst optimizer analyzes the logical plan, optimizes it, and then executes the physical plan to produce the result.
This lazy evaluation strategy allows Spark to optimize the execution plan, minimize data shuffling, and improve overall performance by reducing unnecessary computations.

問題 #24
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