Databricks Data Engineer Professional exam dumps

Databricks Data Engineer Professional practice question 119 of 313

Databricks Certified Data Engineer Professional. Professional level, Databricks. Free question with the correct answer and a full explanation.

Databricks Data Engineer Professional Question 119

Select 3

A data engineering team is running a Spark job on a Delta table containing billions of records, but they observe high query latency and suboptimal performance. Upon investigation, they find that the table has a large number of small files due to frequent updates and overpartitioning. Which of the following issues are likely caused by the presence of these small files?

  1. A

    Increased job execution time due to higher metadata overhead during query planning.

  2. B

    Higher memory consumption on Spark executors due to the need to load multiple small files.

  3. C

    Improved query performance because small files distribute the workload across more Spark tasks.

  4. D

    Increased I/O operations and network overhead due to processing a large number of small files.

  5. E

    Reduced shuffle spill as small files reduce the size of partitions involved in shuffles.

Show answer and explanation

Correct answers: A, B, D

Explanation

The presence of small files in a Spark job leads to inefficiencies such as increased metadata overhead during query planning, higher memory consumption on executors, and additional I/O and network overhead. These factors degrade query performance, making it essential to optimize data layout using techniques like file compaction or partition pruning.

  • A. Correct.

    Correct: A large number of small files increases metadata overhead for the Spark driver, as it must scan and plan for each file, leading to higher job execution time.

  • B. Correct.

    Correct: Small files require Spark executors to open and process many files, increasing memory consumption as more file handles and associated data structures are loaded.

  • C. Incorrect.

    Incorrect: Small files do not inherently improve performance. Instead, they cause inefficiencies by creating unnecessary overhead, contrary to distributing the workload effectively.

  • D. Correct.

    Correct: Processing a large number of small files results in higher I/O operations and network overhead, as Spark must read scattered data from disk and shuffle it across nodes.

  • E. Incorrect.

    Incorrect: Small files do not reduce shuffle spill. The size of partitions during shuffles depends on the data distribution, not the size of the files themselves.

Timed practice exam

Take a Databricks Data Engineer Professional practice test under exam conditions

60 questions in 120 minutes, drawn from this bank, with a score report and a per-question review when you finish.

Start timed exam