File access strategy: which process is generally best when you must access a large number of records from the same file?

Introduction / Context:
Workloads that touch many records in the same file—like monthly billing, payroll, or mass updates—benefit from throughput-oriented processing. Batch jobs are optimized for sequential, large-volume work with minimal user interaction.

Given Data / Assumptions:

• The job requires accessing numerous records in one run.
• Interactivity is not essential; timeliness can be scheduled (e.g., nightly).
• System utilization and I/O efficiency matter more than immediate response.

Concept / Approach:
Batch processing amortizes setup costs across many records, leverages sequential I/O, and maximizes throughput. Real-time/online jobs suit low-latency, per-transaction needs; they are suboptimal for mass processing due to interaction overhead and locking contention.

Step-by-Step Solution:
1) Classify workload: many-record, uniform operations (e.g., compute statements).2) Choose batch for sequential scans and aggregation efficiency.3) Schedule during off-peak hours to reduce contention.4) Monitor I/O and memory to ensure sustained throughput.

Verification / Alternative check:
Performance benchmarks commonly show batch outperforming interactive modes for bulk work. ETL pipelines exemplify this pattern.

Why Other Options Are Wrong:
Real-time/online jobs prioritize latency and user interaction, not bulk throughput. Declaring all options equally optimal ignores workload characteristics.

Common Pitfalls:
Running massive updates through interactive screens, causing timeouts and user frustration.

Final Answer:
A batch job (process many records in scheduled runs).