ハイパスレートのAssociate-Developer-Apache-Spark-3.5勉強時間試験-試験の準備方法-効率的なAssociate-Developer-Apache-Spark-3.5問題数どのようにDatabricks Associate-Developer-Apache-Spark-3.5試験に準備すると悩んでいますか。我々社のAssociate-Developer-Apache-Spark-3.5問題集を参考した後、ほっとしました。弊社のAssociate-Developer-Apache-Spark-3.5ソフト版問題集はかねてより多くのIT事業をしている人々は順調にDatabricks Associate-Developer-Apache-Spark-3.5資格認定を取得させます。試験にパースする原因は我々問題集の全面的で最新版です。 Databricks Certified Associate Developer for Apache Spark 3.5 - Python 認定 Associate-Developer-Apache-Spark-3.5 試験問題 (Q128-Q133):質問 # 128
27 of 55.
A data engineer needs to add all the rows from one table to all the rows from another, but not all the columns in the first table exist in the second table.
The error message is:
AnalysisException: UNION can only be performed on tables with the same number of columns.
The existing code is:
au_df.union(nz_df)
The DataFrame au_df has one extra column that does not exist in the DataFrame nz_df, but otherwise both DataFrames have the same column names and data types.
What should the data engineer fix in the code to ensure the combined DataFrame can be produced as expected?
A. df = au_df.unionByName(nz_df, allowMissingColumns=True)
B. df = au_df.union(nz_df, allowMissingColumns=True)
C. df = au_df.unionByName(nz_df, allowMissingColumns=False)
D. df = au_df.unionAll(nz_df)
正解：A
解説：
When two DataFrames have different column sets, the normal union() or unionAll() functions fail unless both have exactly the same columns in the same order.
Solution: Use unionByName() with allowMissingColumns=True.
This aligns columns by name and automatically adds missing columns with null values.
Correct syntax:
combined_df = au_df.unionByName(nz_df, allowMissingColumns=True)
This ensures the union works even if one DataFrame has extra or missing columns.
Why the other options are incorrect:
B: unionAll() is deprecated; also requires identical schemas.
C: With allowMissingColumns=False, Spark still throws a mismatch error.
D: union() doesn't accept the allowMissingColumns argument.
Reference:
PySpark API - DataFrame.unionByName() with allowMissingColumns option.
Databricks Exam Guide (June 2025): Section "Developing Apache Spark DataFrame/DataSet API Applications" - combining DataFrames and schema alignment.
質問 # 129
22 of 55.
A Spark application needs to read multiple Parquet files from a directory where the files have differing but compatible schemas.
The data engineer wants to create a DataFrame that includes all columns from all files.
Which code should the data engineer use to read the Parquet files and include all columns using Apache Spark?
A. spark.read.parquet("/data/parquet/")
B. spark.read.format("parquet").option("inferSchema", "true").load("/data/parquet/")
C. spark.read.option("mergeSchema", True).parquet("/data/parquet/")
D. spark.read.parquet("/data/parquet/").option("mergeAllCols", True)
正解：C
解説：
When reading Parquet files, Spark infers a unified schema automatically only if all files share identical structures.
If files have different but compatible schemas, you must enable schema merging by setting the option mergeSchema=True.
Correct syntax:
df = spark.read.option("mergeSchema", True).parquet("/data/parquet/")
This option ensures Spark merges all discovered fields across Parquet files into one unified DataFrame schema.
Why the other options are incorrect:
A: Loads files but ignores extra columns - uses only the first file's schema.
C: inferSchema applies to CSV/JSON, not Parquet.
D: mergeAllCols is not a valid Spark option.
Reference:
Spark SQL Data Sources - Parquet options (mergeSchema, path).
Databricks Exam Guide (June 2025): Section "Using Spark DataFrame APIs" - reading/writing DataFrames with schema evolution and merging.
質問 # 130
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 repartition() transformation with a lower number of partitions
B. Use the coalesce() transformation with a lower number of partitions
C. Use the distinct() transformation to combine similar partitions
D. Use the sortBy() transformation to reorganize the data
正解：B
解説：
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.
質問 # 131
A developer is running Spark SQL queries and notices underutilization of resources. Executors are idle, and the number of tasks per stage is low.
What should the developer do to improve cluster utilization?
A. Reduce the value of spark.sql.shuffle.partitions
B. Increase the value of spark.sql.shuffle.partitions
C. Increase the size of the dataset to create more partitions
D. Enable dynamic resource allocation to scale resources as needed
正解：B
解説：
Comprehensive and Detailed Explanation From Exact Extract:
The number of tasks is controlled by the number of partitions. By default,spark.sql.shuffle.partitionsis 200. If stages are showing very few tasks (less than total cores), you may not be leveraging full parallelism.
From the Spark tuning guide:
"To improve performance, especially for large clusters, increasespark.sql.shuffle.partitionsto create more tasks and parallelism." Thus:
A is correct: increasing shuffle partitions increases parallelism
B is wrong: it further reduces parallelism
C is invalid: increasing dataset size doesn't guarantee more partitions D is irrelevant to task count per stage Final Answer: A
質問 # 132
A data engineer is running a Spark job to process a dataset of 1 TB stored in distributed storage. The cluster has 10 nodes, each with 16 CPUs. Spark UI shows:
Low number of Active Tasks
Many tasks complete in milliseconds
Fewer tasks than available CPUs
Which approach should be used to adjust the partitioning for optimal resource allocation?
A. Set the number of partitions by dividing the dataset size (1 TB) by a reasonable partition size, such as 128 MB
B. Set the number of partitions equal to the total number of CPUs in the cluster
C. Set the number of partitions to a fixed value, such as 200
D. Set the number of partitions equal to the number of nodes in the cluster
正解：A
解説：
Spark's best practice is to estimate partition count based on data volume and a reasonable partition size - typically 128 MB to 256 MB per partition.
With 1 TB of data: 1 TB / 128 MB ≈ ~8000 partitions
This ensures that tasks are distributed across available CPUs for parallelism and that each task processes an optimal volume of data.
Option A (equal to cores) may result in partitions that are too large.
Option B (fixed 200) is arbitrary and may underutilize the cluster.
Option C (nodes) gives too few partitions (10), limiting parallelism.
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