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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q79-Q84):

NEW QUESTION # 79
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. Enable dynamic resource allocation to scale resources as needed
  • B. Increase the value of spark.sql.shuffle.partitions
  • C. Reduce the value of spark.sql.shuffle.partitions
  • D. Increase the size of the dataset to create more partitions

Answer: B

Explanation:
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


NEW QUESTION # 80
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. Optimizing the layout of Delta files on disk
  • C. Collecting persistent table statistics and storing them in the metastore for future use
  • D. Dynamically switching join strategies

Answer: D

Explanation:
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


NEW QUESTION # 81
A data scientist at a financial services company is working with a Spark DataFrame containing transaction records. The DataFrame has millions of rows and includes columns fortransaction_id,account_number, transaction_amount, andtimestamp. Due to an issue with the source system, some transactions were accidentally recorded multiple times with identical information across all fields. The data scientist needs to remove rows with duplicates across all fields to ensure accurate financial reporting.
Which approach should the data scientist use to deduplicate the orders using PySpark?

  • A. df = df.filter(F.col("transaction_id").isNotNull())
  • B. df = df.dropDuplicates(["transaction_amount"])
  • C. df = df.dropDuplicates()
  • D. df = df.groupBy("transaction_id").agg(F.first("account_number"), F.first("transaction_amount"), F.first ("timestamp"))

Answer: C

Explanation:
dropDuplicates() with no column list removes duplicates based on all columns.
It's the most efficient and semantically correct way to deduplicate records that are completely identical across all fields.
From the PySpark documentation:
dropDuplicates(): Return a new DataFrame with duplicate rows removed, considering all columns if none are specified.
- Source:PySpark DataFrame.dropDuplicates() API


NEW QUESTION # 82
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 distinct() transformation to combine similar partitions
  • C. Use the coalesce() transformation with a lower number of partitions
  • D. Use the sortBy() transformation to reorganize the data

Answer: C

Explanation:
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.
Reference:Spark API - coalesce


NEW QUESTION # 83
A data engineer is streaming data from Kafka and requires:
Minimal latency
Exactly-once processing guarantees
Which trigger mode should be used?

  • A. .trigger(continuous=True)
  • B. .trigger(processingTime='1 second')
  • C. .trigger(continuous='1 second')
  • D. .trigger(availableNow=True)

Answer: B

Explanation:
Comprehensive and Detailed Explanation:
Exactly-once guarantees in Spark Structured Streaming require micro-batch mode (default), not continuous mode.
Continuous mode (.trigger(continuous=...)) only supports at-least-once semantics and lacks full fault- tolerance.
trigger(availableNow=True)is a batch-style trigger, not suited for low-latency streaming.
So:
Option A uses micro-batching with a tight trigger interval # minimal latency + exactly-once guarantee.
Final Answer: A


NEW QUESTION # 84
......

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