PDE Practice Question: Refer to the exhibit

Exhibit

Refer to the exhibit.
{
  "insertId": "abc123",
  "jsonPayload": {
    "message": "Error processing element: expected integer at field 'temperature', got string 'hot'",
    "workerId": "worker-5",
    "step": "ParseAndValidate"
  },
  "resource": {
    "type": "dataflow_step",
    "labels": {
      "job_id": "job-1234",
      "step_id": "s2"
    }
  }
}

Refer to the exhibit. A Dataflow pipeline is failing intermittently with the shown error. Which step should the team take to ensure data quality and prevent such errors?

⚠ Common exam trap

Google Cloud often tests the distinction between scaling solutions (like increasing workers) and data quality patterns (like dead letter queues), trapping candidates who confuse performance optimization with error handling.

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

✓

Modify the pipeline to handle parsing failures by sending invalid records to a dead letter queue.

The error indicates that the pipeline is failing due to malformed or unparseable data. By sending invalid records to a dead letter queue (DLQ), the pipeline can continue processing valid data while capturing and isolating bad records for later analysis or reprocessing. This pattern is a standard data quality practice in Apache Beam and Dataflow, ensuring that transient or corrupt data does not cause pipeline failures.

Answer analysis

Option-by-option breakdown

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

✗
Increase the number of workers to process the data faster.
Why it's wrong here
More workers may reduce lag but do not fix the parsing error.
✗
Add a monitoring alert on the 'system_lag' metric.
Why it's wrong here
System lag measures processing delay, not data quality errors.
✗
Use a strongly typed schema for the PCollection and let Beam automatically reject malformed data.
Why it's wrong here
Beam schemas validate types but do not automatically reject; you must add error handling logic.
✓
Modify the pipeline to handle parsing failures by sending invalid records to a dead letter queue.
Why this is correct
A dead letter queue isolates bad data for later inspection without failing the pipeline.

Go deeper

PDE Practice Question: Refer to the exhibit

Exhibit

Why each option matters

Option-by-option breakdown

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