Develop data processing →mediumMultiple ChoiceObjective-mapped

DP-203 Develop data processing Practice Question

Exhibit

Refer to the exhibit.

{
  "name": "AggregateProductSales",
  "properties": {
    "folder": {
      "name": "Sales"
    },
    "content": {
      "jobType": "SparkJob",
      "jobDefinition": {
        "file": "abfss://container@storage.dfs.core.windows.net/synapse/workspaces/workspace/sparkjobdefinitions/aggregate_sales.py",
        "conf": {
          "spark.dynamicAllocation.enabled": "false",
          "spark.executor.instances": 10,
          "spark.executor.cores": 4,
          "spark.executor.memory": "8g"
        }
      }
    }
  }
}

You are reviewing a Spark job definition in Azure Synapse Analytics. The job aggregates sales data. The job runs successfully but takes longer than expected. You notice that dynamic allocation is disabled and the executor instances are fixed at 10. The cluster has a maximum of 20 nodes. What is the most likely reason for the slow performance?

⚠ Common exam trap

The trap here is that candidates may overlook the explicit configuration detail (dynamic allocation disabled, fixed 10 executors) and instead focus on generic performance issues like memory or partitioning, missing the direct scaling limitation.

Answer choices

Why each option matters

Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.

Correct answer & explanation

✓

The job cannot scale out beyond 10 executors because dynamic allocation is disabled.

With dynamic allocation disabled and executor instances fixed at 10, the Spark job cannot utilize additional cluster resources even though the cluster supports up to 20 nodes. This means the job is artificially constrained to 10 executors, limiting parallelism and causing slower performance despite available compute capacity.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✗
The file path is incorrect, causing data read errors.
Why it's wrong here
The job runs successfully.
✓
The job cannot scale out beyond 10 executors because dynamic allocation is disabled.
Why this is correct
With dynamic allocation off, the job is limited to 10 executors.
✗
The job is not parallelized because of a single partition.
Why it's wrong here
The job uses multiple executors.
✗
The executor memory is too low for the aggregation.
Why it's wrong here
8 GB is typical; not likely the main issue.

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DP-203 Develop data processing Practice Question

Exhibit

Why each option matters

Option-by-option breakdown

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