Practice Spark DataFrames MCQ questions with detailed explanations and clear answer validation. These MCQs help you revise core concepts, compare close options, and improve accuracy for interviews, certification exams, and technical screening rounds. Use this updated 2026 set to strengthen fundamentals and confidence.
Q51. Which statement about explode is most accurate?
Select an answer to check.
Answer: Expand array column into rows.
Here, Expand array column into rows. is the right choice. Useful for nested data. It aligns directly with what the question asks about which statement about explode is most accurate. Competing choices sound plausible, but they miss the key condition.
Q52. How is explode best characterized?
Select an answer to check.
Answer: Expand array column into rows.
In this case, Expand array column into rows. is correct. Useful for nested data. It aligns directly with what the question asks about how is explode best characterized. Competing choices sound plausible, but they miss the key condition.
Q53. Which option best describes window functions?
Select an answer to check.
Answer: Per-partition ordered computations.
The best option here is Per-partition ordered computations.. Row_number, lag, etc. It aligns directly with what the question asks about which option best describes window functions. Competing choices sound plausible, but they miss the key condition.
Q54. What is the primary purpose of window functions?
Select an answer to check.
Answer: Per-partition ordered computations.
For this question, Per-partition ordered computations. is correct. Row_number, lag, etc. It aligns directly with what the question asks about what is the primary purpose of window functions. Competing choices sound plausible, but they miss the key condition.
Q55. Which statement about window functions is most accurate?
Select an answer to check.
Answer: Per-partition ordered computations.
Per-partition ordered computations. is the correct answer here. Row_number, lag, etc. It aligns directly with what the question asks about which statement about window functions is most accurate. Competing choices sound plausible, but they miss the key condition.
Q56. How is window functions best characterized?
Select an answer to check.
Answer: Per-partition ordered computations.
Here, Per-partition ordered computations. is the right choice. Row_number, lag, etc. This matches the core idea being tested around how is window functions best characterized. Competing choices sound plausible, but they miss the key condition.
Q57. Which option best describes UDFs?
Select an answer to check.
Answer: User-defined functions on columns.
In this case, User-defined functions on columns. is correct. Avoid Python UDFs when possible. This matches the core idea being tested around which option best describes udfs. Competing choices sound plausible, but they miss the key condition.
Q58. What is the primary purpose of UDFs?
Select an answer to check.
Answer: User-defined functions on columns.
The best option here is User-defined functions on columns.. Avoid Python UDFs when possible. This matches the core idea being tested around what is the primary purpose of udfs. Competing choices sound plausible, but they miss the key condition.
Q59. Which statement about UDFs is most accurate?
Select an answer to check.
Answer: User-defined functions on columns.
For this question, User-defined functions on columns. is correct. Avoid Python UDFs when possible. This matches the core idea being tested around which statement about udfs is most accurate. Competing choices sound plausible, but they miss the key condition.
Q60. How is UDFs best characterized?
Select an answer to check.
Answer: User-defined functions on columns.
User-defined functions on columns. is the correct answer here. Avoid Python UDFs when possible. This matches the core idea being tested around how is udfs best characterized. Competing choices sound plausible, but they miss the key condition.
Q61. Which option best describes UDAFs?
Select an answer to check.
Answer: User-defined aggregate functions.
Here, User-defined aggregate functions. is the right choice. Custom aggregates. That is exactly the concept behind which option best describes udafs in this context. Competing choices sound plausible, but they miss the key condition.
Q62. What is the primary purpose of UDAFs?
Select an answer to check.
Answer: User-defined aggregate functions.
In this case, User-defined aggregate functions. is correct. Custom aggregates. That is exactly the concept behind what is the primary purpose of udafs in this context. Competing choices sound plausible, but they miss the key condition.
Q63. Which statement about UDAFs is most accurate?
Select an answer to check.
Answer: User-defined aggregate functions.
The best option here is User-defined aggregate functions.. Custom aggregates. That is exactly the concept behind which statement about udafs is most accurate in this context. Competing choices sound plausible, but they miss the key condition.
Q64. How is UDAFs best characterized?
Select an answer to check.
Answer: User-defined aggregate functions.
For this question, User-defined aggregate functions. is correct. Custom aggregates. That is exactly the concept behind how is udafs best characterized in this context. Competing choices sound plausible, but they miss the key condition.
Q65. Which option best describes Pandas UDFs?
Select an answer to check.
Answer: Vectorized UDFs over Arrow batches.
Vectorized UDFs over Arrow batches. is the correct answer here. Faster Python UDFs. That is exactly the concept behind which option best describes pandas udfs in this context. Competing choices sound plausible, but they miss the key condition.
Q66. What is the primary purpose of Pandas UDFs?
Select an answer to check.
Answer: Vectorized UDFs over Arrow batches.
Here, Vectorized UDFs over Arrow batches. is the right choice. Faster Python UDFs. It fits the requirement in the prompt about what is the primary purpose of pandas udfs. Competing choices sound plausible, but they miss the key condition.
Q67. Which statement about Pandas UDFs is most accurate?
Select an answer to check.
Answer: Vectorized UDFs over Arrow batches.
In this case, Vectorized UDFs over Arrow batches. is correct. Faster Python UDFs. It fits the requirement in the prompt about which statement about pandas udfs is most accurate. Competing choices sound plausible, but they miss the key condition.
Q68. How is Pandas UDFs best characterized?
Select an answer to check.
Answer: Vectorized UDFs over Arrow batches.
The best option here is Vectorized UDFs over Arrow batches.. Faster Python UDFs. It fits the requirement in the prompt about how is pandas udfs best characterized. Competing choices sound plausible, but they miss the key condition.
Q69. Which option best describes DataFrame caching?
Select an answer to check.
Answer: cache() / persist() for reuse.
For this question, cache() / persist() for reuse. is correct. Use when reused multiple times. It fits the requirement in the prompt about which option best describes dataframe caching. Competing choices sound plausible, but they miss the key condition.
Q70. What is the primary purpose of DataFrame caching?
Select an answer to check.
Answer: cache() / persist() for reuse.
cache() / persist() for reuse. is the correct answer here. Use when reused multiple times. It fits the requirement in the prompt about what is the primary purpose of dataframe caching. Competing choices sound plausible, but they miss the key condition.
Q71. Which statement about DataFrame caching is most accurate?
Select an answer to check.
Answer: cache() / persist() for reuse.
Here, cache() / persist() for reuse. is the right choice. Use when reused multiple times. This is the most accurate statement for which statement about dataframe caching is most accurate. Competing choices sound plausible, but they miss the key condition.
Q72. How is DataFrame caching best characterized?
Select an answer to check.
Answer: cache() / persist() for reuse.
In this case, cache() / persist() for reuse. is correct. Use when reused multiple times. This is the most accurate statement for how is dataframe caching best characterized. Competing choices sound plausible, but they miss the key condition.
Q73. Which option best describes schema inference?
Select an answer to check.
Answer: Infer schema from data on read.
The best option here is Infer schema from data on read.. Costly; pass schema in prod. This is the most accurate statement for which option best describes schema inference. Competing choices sound plausible, but they miss the key condition.
Q74. What is the primary purpose of schema inference?
Select an answer to check.
Answer: Infer schema from data on read.
For this question, Infer schema from data on read. is correct. Costly; pass schema in prod. This is the most accurate statement for what is the primary purpose of schema inference. Competing choices sound plausible, but they miss the key condition.
Q75. Which statement about schema inference is most accurate?
Select an answer to check.
Answer: Infer schema from data on read.
Infer schema from data on read. is the correct answer here. Costly; pass schema in prod. This is the most accurate statement for which statement about schema inference is most accurate. Competing choices sound plausible, but they miss the key condition.
Q76. How is schema inference best characterized?
Select an answer to check.
Answer: Infer schema from data on read.
Here, Infer schema from data on read. is the right choice. Costly; pass schema in prod. It aligns directly with what the question asks about how is schema inference best characterized. The remaining choices fail because they don’t satisfy the full definition.
Q77. Which option best describes explicit schema?
Select an answer to check.
Answer: Provide StructType to read.
In this case, Provide StructType to read. is correct. Faster and safer. It aligns directly with what the question asks about which option best describes explicit schema. The remaining choices fail because they don’t satisfy the full definition.
Q78. What is the primary purpose of explicit schema?
Select an answer to check.
Answer: Provide StructType to read.
The best option here is Provide StructType to read.. Faster and safer. It aligns directly with what the question asks about what is the primary purpose of explicit schema. The remaining choices fail because they don’t satisfy the full definition.
Q79. Which statement about explicit schema is most accurate?
Select an answer to check.
Answer: Provide StructType to read.
For this question, Provide StructType to read. is correct. Faster and safer. It aligns directly with what the question asks about which statement about explicit schema is most accurate. The remaining choices fail because they don’t satisfy the full definition.
Q80. How is explicit schema best characterized?
Select an answer to check.
Answer: Provide StructType to read.
Provide StructType to read. is the correct answer here. Faster and safer. It aligns directly with what the question asks about how is explicit schema best characterized. The remaining choices fail because they don’t satisfy the full definition.
Here, spark.read.format(...).load() / df.write.format(...).save(). is the right choice. Many built-in formats. This matches the core idea being tested around which option best describes read/write api. The remaining choices fail because they don’t satisfy the full definition.
Q82. What is the primary purpose of read/write API?
In this case, spark.read.format(...).load() / df.write.format(...).save(). is correct. Many built-in formats. This matches the core idea being tested around what is the primary purpose of read/write api. The remaining choices fail because they don’t satisfy the full definition.
Q83. Which statement about read/write API is most accurate?
The best option here is spark.read.format(...).load() / df.write.format(...).save().. Many built-in formats. This matches the core idea being tested around which statement about read/write api is most accurate. The remaining choices fail because they don’t satisfy the full definition.
For this question, spark.read.format(...).load() / df.write.format(...).save(). is correct. Many built-in formats. This matches the core idea being tested around how is read/write api best characterized. The remaining choices fail because they don’t satisfy the full definition.
Q85. Which option best describes partitionBy on write?
Select an answer to check.
Answer: Partition output by a column.
Partition output by a column. is the correct answer here. Improves downstream pruning. This matches the core idea being tested around which option best describes partitionby on write. The remaining choices fail because they don’t satisfy the full definition.
Q86. What is the primary purpose of partitionBy on write?
Select an answer to check.
Answer: Partition output by a column.
Here, Partition output by a column. is the right choice. Improves downstream pruning. That is exactly the concept behind what is the primary purpose of partitionby on in this context. The remaining choices fail because they don’t satisfy the full definition.
Q87. Which statement about partitionBy on write is most accurate?
Select an answer to check.
Answer: Partition output by a column.
In this case, Partition output by a column. is correct. Improves downstream pruning. That is exactly the concept behind which statement about partitionby on write is most in this context. The remaining choices fail because they don’t satisfy the full definition.
Q88. How is partitionBy on write best characterized?
Select an answer to check.
Answer: Partition output by a column.
The best option here is Partition output by a column.. Improves downstream pruning. That is exactly the concept behind how is partitionby on write best characterized in this context. The remaining choices fail because they don’t satisfy the full definition.
Q89. Which option best describes bucketBy on write?
Select an answer to check.
Answer: Bucket data by hash for joins.
For this question, Bucket data by hash for joins. is correct. Hive-compatible bucketing. That is exactly the concept behind which option best describes bucketby on write in this context. The remaining choices fail because they don’t satisfy the full definition.
Q90. What is the primary purpose of bucketBy on write?
Select an answer to check.
Answer: Bucket data by hash for joins.
Bucket data by hash for joins. is the correct answer here. Hive-compatible bucketing. That is exactly the concept behind what is the primary purpose of bucketby on in this context. The remaining choices fail because they don’t satisfy the full definition.
Q91. Which statement about bucketBy on write is most accurate?
Select an answer to check.
Answer: Bucket data by hash for joins.
Here, Bucket data by hash for joins. is the right choice. Hive-compatible bucketing. It fits the requirement in the prompt about which statement about bucketby on write is most. The remaining choices fail because they don’t satisfy the full definition.
Q92. How is bucketBy on write best characterized?
Select an answer to check.
Answer: Bucket data by hash for joins.
In this case, Bucket data by hash for joins. is correct. Hive-compatible bucketing. It fits the requirement in the prompt about how is bucketby on write best characterized. The remaining choices fail because they don’t satisfy the full definition.
Q93. Which option best describes DataFrame vs SQL?
Select an answer to check.
Answer: Both compile via Catalyst.
The best option here is Both compile via Catalyst.. Equivalent performance. It fits the requirement in the prompt about which option best describes dataframe vs sql. The remaining choices fail because they don’t satisfy the full definition.
Q94. What is the primary purpose of DataFrame vs SQL?
Select an answer to check.
Answer: Both compile via Catalyst.
For this question, Both compile via Catalyst. is correct. Equivalent performance. It fits the requirement in the prompt about what is the primary purpose of dataframe vs. The remaining choices fail because they don’t satisfy the full definition.
Q95. Which statement about DataFrame vs SQL is most accurate?
Select an answer to check.
Answer: Both compile via Catalyst.
Both compile via Catalyst. is the correct answer here. Equivalent performance. It fits the requirement in the prompt about which statement about dataframe vs sql is most. The remaining choices fail because they don’t satisfy the full definition.
Q96. How is DataFrame vs SQL best characterized?
Select an answer to check.
Answer: Both compile via Catalyst.
Here, Both compile via Catalyst. is the right choice. Equivalent performance. This is the most accurate statement for how is dataframe vs sql best characterized. The remaining choices fail because they don’t satisfy the full definition.
Q97. Which option best describes explain?
Select an answer to check.
Answer: Show physical/logical plan.
In this case, Show physical/logical plan. is correct. Use to debug performance. This is the most accurate statement for which option best describes explain. The remaining choices fail because they don’t satisfy the full definition.
Q98. What is the primary purpose of explain?
Select an answer to check.
Answer: Show physical/logical plan.
The best option here is Show physical/logical plan.. Use to debug performance. This is the most accurate statement for what is the primary purpose of explain. The remaining choices fail because they don’t satisfy the full definition.
Q99. Which statement about explain is most accurate?
Select an answer to check.
Answer: Show physical/logical plan.
For this question, Show physical/logical plan. is correct. Use to debug performance. This is the most accurate statement for which statement about explain is most accurate. The remaining choices fail because they don’t satisfy the full definition.
Q100. How is explain best characterized?
Select an answer to check.
Answer: Show physical/logical plan.
Show physical/logical plan. is the correct answer here. Use to debug performance. This is the most accurate statement for how is explain best characterized. The remaining choices fail because they don’t satisfy the full definition.