SF-PD2 Process Automation, Logic, and Integration Practice Question
A developer is designing a solution to process large volumes of incoming JSON data from an external system. The data must be transformed and loaded into multiple related custom objects. The processing must handle up to 1 million records per day and be resilient to failures. Which combination of features should the developer use to meet these requirements?
⚠ Common exam trap
The trap here is underestimating the scalability of Queueable Apex or overestimating Flow limits, leading to a solution that fails at high volumes.
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 a Scheduled Apex job that invokes a Batch Apex class to process the data, with Database.Stateful to maintain state across batches.
Batch Apex is the optimal solution for processing large data volumes. It breaks the data into chunks, respects governor limits, and can maintain state with Database.Stateful. Scheduling the batch ensures daily processing. Other options either have scalability limits (Queueable, Flow) or are not designed for bulk data (Platform Events). The developer should also consider using Database.AllowsCallouts if needed.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use a Scheduled Apex job that invokes a Batch Apex class to process the data, with Database.Stateful to maintain state across batches.
Why this is correct
Batch Apex is designed for processing large data volumes. By implementing Database.Stateful, the class can maintain state across batch executions, which is useful for tracking progress or accumulating results. Scheduling the batch allows daily processing. This approach is scalable, handles governor limits by chunking, and provides resilience through batch retries if configured.
- ✗
Use Platform Events to publish each record and an Apex trigger to process them asynchronously.
Why it's wrong here
Platform Events are great for decoupling, but publishing 1 million events per day could hit event delivery limits and is not designed for bulk data processing. Triggers on Platform Events are asynchronous but each event is processed individually, leading to high overhead. This approach is not efficient for large-scale data transformation and loading.
- ✗
Use a Flow with a Loop element to process each record and an Apex Action to perform DML operations.
Why it's wrong here
Flows have governor limits that make them unsuitable for processing 1 million records. Loops in Flows are limited by the number of elements executed and can hit CPU time limits. Flows are best for declarative automation on smaller datasets. Using an Apex Action within a Flow does not solve the scalability issue; the Flow itself would fail.
- ✗
Use a Queueable Apex job that chains itself to process records in chunks, with a final job to update related objects.
Why it's wrong here
Queueable Apex is good for asynchronous processing and chaining, but it has a limit of 50 jobs in the chain, and each job can process up to 200 records if using a collection. For 1 million records, this would require many chained jobs and could hit limits. It is less efficient than Batch Apex for large volumes and lacks built-in chunking and state management.
About these practice questions
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Salesforce exam blueprint
This SF-PD2 practice question is part of Courseiva's free Salesforce certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the SF-PD2 exam.