Identify Trends in Job Performance Using the Lakeflow Jobs Run History View to Compare Current Execution Times Against Historical Baselines. DATA-ENGINEER-ASSOCIATE Practice Questions (Page 2)

Questions 6–10

1. 6foundation · easy

   When reviewing the run history view, which pattern would indicate a job's performance is improving?

   • AExecution times are constant across runs
   • BExecution times are decreasing over consecutive runs
   • CExecution times are increasing over consecutive runs
   • DExecution times are alternating between high and low
   Reveal Solution Discussion Select an answer first
2. 7application · medium

   A team is evaluating whether to optimize a job that runs daily. The run history view shows that the job's duration has been stable at 45 minutes for the past 3 months. The team wants to know if there is any variation in the execution time. What is the best way to assess this?

   • ALook at the job's status for each run to see if any failed.
   • BReview the job's cluster configuration to see if it has changed.
   • CCheck the job's average duration over the past 3 months.
   • DLook at the minimum and maximum durations in the run history view over the past 3 months.
   Reveal Solution Discussion Select an answer first
4. 8application · medium

   A team is investigating why a job's latest run took 90 minutes, while the previous 10 runs averaged 60 minutes. In the run history view, they see the latest run has a status of 'Succeeded' and a duration of 90 minutes. What additional metric in the run history view would be most useful to determine if the 90-minute run is an outlier or part of a trend?

   • AThe start time of the latest run compared to the scheduled time.
   • BThe user who triggered the latest run.
   • CThe cluster ID used for the latest run.
   • DThe duration of the previous 10 runs displayed in the same view.
   Reveal Solution Discussion Select an answer first
5. 9application · medium

   A team runs a job that processes a dataset that grows by 10% each month. The run history view shows that the job's duration has been increasing by 10% each month as well. What is the most likely conclusion?

   • AThe job is performing as expected, and the increase in duration is proportional to the data growth.
   • BThe job's cluster is too small and needs to be scaled up.
   • CThe job is experiencing a performance degradation that needs immediate attention.
   • DThe job's code has a bug that is causing it to process data inefficiently.
   Reveal Solution Discussion Select an answer first
6. 10expert · hard

   A team runs a job that processes data from a third-party API. The run history view shows that the job's duration has been increasing over the past month, but the data volume has been constant. The team has confirmed that the cluster configuration is unchanged and the Spark code has not been modified. What is the most likely cause of the increasing duration?

   • AThe job's schedule was changed to run during peak API usage hours.
   • BThe job's cluster is experiencing memory pressure due to other jobs running concurrently.
   • CThe job is now processing more data than before due to a change in the API's response format.
   • DThe third-party API is responding more slowly, causing the job to spend more time waiting for data.
   Reveal Solution Discussion Select an answer first