A marketing analyst receives a daily orders file named orders_2025-08-28.json from an internal API. Each JSON record represents a single order and contains a line_items array; every element of line_items is itself an object that holds product_id, quantity and unit_price. The analyst must ingest the data into a relational reporting table that has one row per order. Based on the characteristics of the .json format, which technical challenge is the analyst MOST likely to face during the load?
Translating 64-bit integers from big-endian to little-endian format before they can be stored in the database.
Converting extended characters because JSON lacks native support for Unicode (UTF-8) encoding.
Decompressing the mandatory GZIP compression that JSON applies to all text files.
Flattening hierarchical objects and arrays that do not map cleanly to a two-dimensional row-and-column structure.
JSON is a text-based format that can nest objects and arrays inside other objects. When a JSON document includes an array-such as line_items-the structure becomes hierarchical. Relational tables, however, are two-dimensional and expect one scalar value per column, so the nested content must be "flattened" (for example, exploded into separate rows or moved to a child table) before it can be inserted.
The other options describe problems that do not inherently apply to .json files:
JSON files are plain text; GZIP or any other compression is optional, not mandatory.
Numbers in JSON are encoded as character strings, not as binary integers, so byte-order (endianness) translation is unnecessary.
The JSON standard requires UTF-8 encoding for open data exchange, so full Unicode character sets are already supported.
Therefore, the most likely obstacle is flattening the nested arrays and objects into a flat relational structure.
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CompTIA Data+ DA0-002 (V2)
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