Handling Data Too Large for System Memory
A database administrator is attempting to load a set of database logs into a software system. Upon processing the data, the administrator receives a notification that this process failed because the data size is too large for the system memory. Which of the following is the best way for the administrator to correct this issue?
Community Insight
The question tests recognizing that an out-of-memory failure during data ingestion is solved by processing the data in smaller pieces (chunking), with the common trap being to reach for hardware upgrades or unrelated troubleshooting steps.
When a database log load fails because the dataset exceeds available system memory, the standard data-acquisition fix is to split the file into smaller batches and process them sequentially. This CompTIA Data+ (DA-002) question establishes that chunking — not storage changes or error analysis — is the correct remedy for out-of-memory ingestion failures.
The most common wrong pick is D, increasing system memory, because scaling hardware feels like a direct fix — but it is expensive, may be impossible in a constrained environment, and does not address the ingestion technique itself. A (internal storage) is also popular yet confuses storage capacity with in-memory processing limits.
Community Discussion (1 comments)
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Expert Analysis
Why the Answer Is Correct
Chunking (option C) divides a large dataset into smaller, manageable portions that are loaded and processed one batch at a time, so no single operation exceeds the system's memory ceiling. This is the classic technique for streaming large database logs into tools that buffer data in RAM during ingestion and transformation. It requires no new hardware, no permissions, and works immediately, which makes it the "best" correction in an exam context that prizes the most efficient, self-service remedy. It also generalizes well: the same chunking pattern is used when reading CSV or log files with pandas, SQL bulk loaders, and ETL pipelines.Why the Other Options Are Wrong
Option A (use internal storage) addresses where data is persisted, not how much memory the processing step consumes; the failure occurs during processing, so swapping storage location does not relieve the in-memory load. Option B (analyze the logs for errors) is diagnostic work unrelated to the stated cause — the notification explicitly says the data size is too large, not that the content is corrupt. Option D (increase memory) might eventually work but is not the "best" answer: it is costly, may be blocked by budget or cloud limits, and treats the symptom rather than fixing the load strategy, so exams treat it as an inferior alternative to chunking.Community Comment Notes
Amelia233, whose comment carries the most community likes, argues exactly this: "Chunking means dividing a large dataset into smaller pieces and processing them separately" and notes that "Storage does not solve the memory limitation during processing." That reasoning aligns with the Data+ emphasis on practical data-acquisition troubleshooting, and the single visible vote on the thread also selected C.Official Reference
Exam Strategy
When a question describes a resource-exhaustion failure (memory, size, or timeout) during loading or processing, look for the option that reduces per-operation size — chunking, batching, or streaming — before considering hardware upgrades. Hardware scaling is almost never the "best" answer in a Data+ scenario.
Frequently Asked Questions
Why is increasing system memory not the best answer when a load fails due to size?
It is costly, may be blocked by environment limits, and only masks the problem. Chunking fixes the ingestion method itself and works with existing resources.
Does chunking apply only to database logs, or to other large files too?
It applies broadly — CSV files, log streams, and ETL pipelines are all processed in smaller batches or with streaming readers to avoid exhausting memory.
Related Analysis
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