C_CPI Integration Suite Development Practice Question
When designing an iFlow that connects to an external OData API, which step is necessary to ensure that the integration flow handles large data sets efficiently using pagination?
⚠ Common exam trap
Many candidates incorrectly believe that increasing the JVM heap size or writing custom Groovy scripts to load entire datasets is the standard solution for handling large OData payloads.
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
✓
Use the OData adapter's pagination support features.
Pagination is critical for avoiding memory issues when processing large datasets. By configuring the OData adapter to use 'top' and 'skip' parameters, or by using the built-in pagination support, the iFlow retrieves data in chunks. This is vital because it prevents the runtime from attempting to load millions of records into RAM simultaneously, which would cause an OutOfMemoryError and crash the integration node.
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 timeout value in the HTTP adapter.
Why it's wrong here
Increasing the timeout allows for longer execution times, but it does not resolve the underlying memory pressure caused by processing a massive data set. If the system is trying to process a large payload all at once, the error will still occur regardless of how long the timeout is.
- ✓
Use the OData adapter's pagination support features.
Why this is correct
The OData adapter provides native support for server-side pagination. By enabling this, the integration flow makes multiple sequential calls to retrieve data in segments, which keeps the payload size within manageable limits for the memory heap, ensuring a stable and performant execution of the data extraction process.
- ✗
Convert the payload to binary format.
Why it's wrong here
Converting a large XML or JSON payload to binary format does not reduce the actual volume of data being processed. It only changes the encoding, which provides no relief for memory constraints. The system would still need to load the entire data set, leading to the same memory issues.
- ✗
Disable logging for the OData call.
Why it's wrong here
While disabling logging reduces the amount of data written to disk, it does not stop the integration runtime from loading the entire payload into the heap during execution. Memory issues are caused by the payload structure itself, not by the secondary logging process associated with the integration flow.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official SAP exam blueprint
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