hardMultiple Choice

DP-203 Practice Question: Refer to the exhibit

Exhibit

Azure Stream Analytics job diagnostics log:

{
  "time": "2023-08-01T12:00:00Z",
  "properties": {
    "jobId": "job-123",
    "jobName": "IoTStreamJob",
    "events": [
      {
        "time": "2023-08-01T11:59:00Z",
        "type": "WatermarkDelay",
        "properties": {
          "watermarkDelaySeconds": 120,
          "maxWatermarkDelaySeconds": 300
        }
      },
      {
        "time": "2023-08-01T11:59:30Z",
        "type": "InputDeserializationError",
        "properties": {
          "source": "iothub",
          "count": 15
        }
      }
    ],
    "jobOutputWatermark": "2023-08-01T11:57:00Z"
  }
}

Refer to the exhibit. A Stream Analytics job shows increasing watermark delay and input deserialization errors. Which action should be taken first to troubleshoot?

⚠ Common exam trap

A common mix-up: candidates assume increasing resources (SUs) or adjusting thresholds will fix performance issues, when the real cause is a data format mismatch that prevents any processing from succeeding.

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

✓

Check the input data schema and ensure it matches the query

Input deserialization errors indicate that the incoming data format does not match the schema defined in the Stream Analytics query. Increasing watermark delay is a symptom of this mismatch, as the job cannot parse events correctly and falls behind. Checking and aligning the input schema with the query is the first and most direct troubleshooting step.

Answer analysis

Option-by-option breakdown

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

✓
Check the input data schema and ensure it matches the query
Why this is correct
Deserialization errors indicate the incoming event format cannot be parsed against the declared input schema, which directly inflates watermark delay as unreadable events stall progress. Verifying that the input data schema matches the query's field definitions and serialisation format (JSON, Avro, CSV) addresses the root cause before scaling or partition tuning.
✗
Change the output to a different sink
Why it's wrong here
Switching the output sink cannot resolve input deserialization errors, since failures occur while reading and parsing the source stream before any output write. Sink changes suit output throttling or write failures, not malformed inbound events.
✗
Increase the number of Streaming Units (SUs)
Why it's wrong here
Adding Streaming Units scales compute throughput for CPU-bound or partitioned workloads, but deserialization errors originate from malformed input records, which extra SUs cannot parse. SU increases suit genuine backlog from insufficient parallel processing, not format failures.
✗
Set the watermark delay threshold higher
Why it's wrong here
Raising the watermark threshold conceals the symptom without addressing the deserialization errors corrupting the input stream; those malformed events still fail to parse. Threshold tuning suits late-arriving-but-valid data in event-ordering scenarios, not schema or format mismatches.

Go deeper

DP-203 Practice Question: Refer to the exhibit

Exhibit

Why each option matters

Option-by-option breakdown

Related to this question