DE-PRO practice questions 71–80

1. Question 71 · 202 in bank

   What is the retention of job run history?

   • AIt is retained until you export or delete job run logs
   • BIt is retained for 30 days, during which time you can deliver job run logs to DBFS or S3
   • CIt is retained for 60 days, during which you can export notebook run results to HTML
   • DIt is retained for 60 days, after which logs are archived

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   Answer: C

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2. Question 72 · 202 in bank

   A data engineer, User A, has promoted a new pipeline to production by using the REST API to programmatically create several jobs. A DevOps engineer, User B, has configured an external orchestration tool to trigger job runs through the REST API. Both users authorized the REST API calls using their personal access tokens. Which statement describes the contents of the workspace audit logs concerning these events?

   • ABecause the REST API was used for job creation and triggering runs, a Service Principal will be automatically used to identify these events.
   • BBecause User A created the jobs, their identity will be associated with both the job creation events and the job run events.
   • CBecause these events are managed separately, User A will have their identity associated with the job creation events and User B will have their identity associated with the job run events.
   • DBecause the REST API was used for job creation and triggering runs, user identity will not be captured in the audit logs.

   Show answer

   Answer: C

   References

   • Databricks documentation
   • Databricks documentation

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3. Question 73 · 202 in bank

   A production workload incrementally applies updates from an external Change Data Capture feed to a Delta Lake table as an always-on Structured Stream job. When data was initially migrated for this table, OPTIMIZE was executed and most data files were resized to 1 GB. Auto Optimize and Auto Compaction were both turned on for the streaming production job. Recent review of data files shows that most data files are under 64 MB, although each partition in the table contains at least 1 GB of data and the total table size is over 10 TB. Which of the following likely explains these smaller file sizes?

   • ADatabricks has autotuned to a smaller target file size to reduce duration of MERGE operations
   • BZ-order indices calculated on the table are preventing file compaction
   • CBloom filter indices calculated on the table are preventing file compaction
   • DDatabricks has autotuned to a smaller target file size based on the overall size of data in the table
   • EDatabricks has autotuned to a smaller target file size based on the amount of data in each partition

   Show answer

   Answer: A

   Explanation

   It is important here to understand the difference between the partition size and the data files. the partition size is 1GB which is caused by OPTIMIZE and also expected. In each partition are data files. Databricks did an attuning to these datafile and resized them to a small size to be able to do MERGE statements efficiently that's why A is the correct answer

   Source: Adapted from community discussion — Melik3

   References

   • Databricks documentation

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4. Question 74 · 202 in bank

   A distributed team of data analysts share computing resources on an interactive cluster with autoscaling configured. In order to better manage costs and query throughput, the workspace administrator is hoping to evaluate whether cluster upscaling is caused by many concurrent users or resource-intensive queries. In which location can one review the timeline for cluster resizing events?

   • AWorkspace audit logs
   • BDriver's log file
   • CGanglia
   • DCluster Event Log

   Show answer

   Answer: D

   Explanation

   Its possible to see the metricks of compute with Ganglia, but the question is about a timeline so D, Cluster Event Log seems correct.

   Source: Adapted from community discussion — imatheushenrique

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5. Question 75 · 202 in bank

   When evaluating the Ganglia Metrics for a given cluster with 3 executor nodes, which indicator would signal proper utilization of the VM's resources?

   • AThe five Minute Load Average remains consistent/flat
   • BCPU Utilization is around 75%
   • CNetwork I/O never spikes
   • DTotal Disk Space remains constant

   Show answer

   Answer: B

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6. Question 76 · 202 in bank

   The data engineer is using Spark's MEMORY_ONLY storage level. Which indicators should the data engineer look for in the Spark UI's Storage tab to signal that a cached table is not performing optimally?

   • AOn Heap Memory Usage is within 75% of Off Heap Memory Usage
   • BThe RDD Block Name includes the “*” annotation signaling a failure to cache
   • CSize on Disk is > 0
   • DThe number of Cached Partitions > the number of Spark Partitions

   Show answer

   Answer: C

   Explanation

   *THE CORRECT ANSWER IS: C* PLEASE IGNORE MY PREVIOUS ANSWER. Long story short, B is correct in the context of non-functional requirement, but the question is based in functional requirement, and sorry for the confusion.

   Source: Adapted from community discussion — Freyr

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7. Question 77 · 202 in bank

   Review the following error traceback:

   

   Which statement describes the error being raised?

   • AThere is a syntax error because the heartrate column is not correctly identified as a column.
   • BThere is no column in the table named heartrateheartrateheartrate
   • CThere is a type error because a column object cannot be multiplied.
   • DThere is a type error because a DataFrame object cannot be multiplied.

   Show answer

   Answer: B

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8. Question 78 · 202 in bank

   What is a method of installing a Python package scoped at the notebook level to all nodes in the currently active cluster?

   • ARun source env/bin/activate in a notebook setup script
   • BInstall libraries from PyPI using the cluster UI
   • CUse %pip install in a notebook cell
   • DUse %sh pip install in a notebook cell

   Show answer

   Answer: C

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9. Question 79 · 202 in bank

   Incorporating unit tests into a PySpark application requires upfront attention to the design of your jobs, or a potentially significant refactoring of existing code. Which benefit offsets this additional effort?

   • AImproves the quality of your data
   • BValidates a complete use case of your application
   • CTroubleshooting is easier since all steps are isolated and tested individually
   • DEnsures that all steps interact correctly to achieve the desired end result

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   Answer: C

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10. Question 80 · 202 in bank

    What describes integration testing?

    • AIt validates an application use case.
    • BIt validates behavior of individual elements of an application,
    • CIt requires an automated testing framework.
    • DIt validates interactions between subsystems of your application.

    Show answer

    Answer: D

    Explanation

    D. It validates interactions between subsystems of your application. An integration test is used for different softwares validation components, subsystems, or applications that has dependencies.

    Source: Adapted from community discussion — imatheushenrique

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DE-PRO practice, question 71. 202 questions available.