DP-203 Develop data processing Practice Question
Exhibit
{
"name": "SalesAggregation",
"properties": {
"activities": [
{
"name": "Notebook1",
"type": "SynapseNotebook",
"dependsOn": [],
"typeProperties": {
"notebook": "SalesAggregationNotebook",
"parameters": {}
},
"linkedServiceName": {
"referenceName": "mySparkPool",
"type": "LinkedServiceReference"
}
}
],
"parameters": {
"inputDate": {
"type": "string",
"defaultValue": "2024-01-01"
}
}
}
}Refer to the exhibit. You have an Azure Synapse pipeline that runs a Spark notebook daily. The notebook uses the inputDate parameter to filter data. The notebook successfully processes data for '2024-01-01' but fails for '2024-01-02' with an error that the 'sales' table does not exist. The 'sales' table is created daily by a preceding job. What is the most likely cause?
⚠ Common exam trap
Candidates often assume the error is due to missing dependencies or permissions, but the real issue is a logical mismatch in table naming conventions between the table creation job and the notebook's expected table name.
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 notebook expects a table named 'sales_20240102' but the preceding job creates 'sales_20240101'
The error indicates that the notebook is looking for a table named 'sales_20240102' (based on the inputDate parameter for '2024-01-02'), but the preceding job creates a table named 'sales_20240101' (the previous day's table). This mismatch occurs because the notebook dynamically constructs the table name using the inputDate parameter, and the preceding job likely creates the table with a date suffix that does not align with the current inputDate. The correct answer is A because the table naming convention is inconsistent between the two processes.
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 notebook expects a table named 'sales_20240102' but the preceding job creates 'sales_20240101'
Why this is correct
The notebook likely constructs table name from the date parameter, and the table for the new date hasn't been created.
- ✗
The notebook activity should have a dependency on the job that creates the table
Why it's wrong here
While adding a dependency could help, the root cause is that the table for the new date doesn't exist yet.
- ✗
The Spark pool does not have permissions to read the storage account where the table data is stored
Why it's wrong here
Permission issues would cause a different error, not table not found.
- ✗
The pipeline parameter 'inputDate' is not being passed to the notebook correctly
Why it's wrong here
If the parameter were not passed, the notebook would use the default value, which would still work for that date.
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