Which AI model translates natural language text into SQL queries?

A company has terabytes of data in a database that the company can use for business analysis. The company wants to build an AI-based application that can build a SQL query from input text that employees provide. The employees have minimal experience with technology. Which solution meets these requirements?

  1. Generative pre-trained transformers (GPT) Source Reference Answer
  2. Residual neural network
  3. Support vector machine
  4. WaveNet

Community Votes

A
100%

100% of anonymous learners picked answer A. Votes are pick records left by other test-takers — they are not the verified answer.

Community Insight

This question tests your ability to match AI model types to real-world tasks; the key is recognizing that text-to-SQL is a natural language processing (generation) task, which is the core strength of GPT models.

Generative pre-trained transformers (GPT) are the ideal AI solution for converting natural language input from non-technical employees into structured SQL queries, leveraging advanced natural language understanding and generation capabilities.

Some candidates confuse GPT with other models like Residual Neural Networks or Support Vector Machines, mistakenly thinking any ML model can handle free-form natural language to structured query translation.

Community Discussion (4 comments)

Jessiii 👍 1 Selected: A
Generative pre-trained transformers (GPT): GPT models are well-suited for natural language processing tasks, such as generating SQL queries from input text. These models are designed to understand and generate human-like text, which makes them ideal for translating text input into structured outputs like SQL queries. With minimal training, GPT models can be fine-tuned for specific tasks, such as query generation, and can help employees with minimal technical experience by understanding natural language inputs and converting them into SQL queries.
85b5b55 👍 1 Selected: A
GPT helps to produces the NL based responsed based on the input text.
Moon 👍 1 Selected: A
The best solution for building an AI-based application that translates natural language (employee input text) into SQL queries is A. Generative pre-trained transformers (GPT). Here's why: GPT's strength in natural language processing: GPT models are specifically designed for understanding and generating human language. They excel at tasks like text translation, question answering, and, crucially, code generation from natural language descriptions. This makes them ideal for converting employee input into SQL queries.
jove 👍 2 Selected: A
Generative pre-trained transformers (GPT) are powerful natural language processing models that excel in understanding and generating human-like text. In this scenario, a GPT model can be trained or fine-tuned to take natural language input from employees and convert it into structured SQL queries. This makes it accessible for users who may not have technical expertise, allowing them to retrieve the data they need from the database using simple, conversational prompts.

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Expert Analysis

Why Generative Pre-trained Transformers (GPT) is the Correct Answer

The scenario describes a natural language to SQL translation task — employees with minimal technical experience will type plain English questions, and the system must produce valid SQL queries. This is fundamentally a natural language understanding and generation problem.

Generative pre-trained transformers (GPT) are large language models specifically architected for understanding and producing human-like text. They excel at tasks such as:

  • Text-to-code / text-to-SQL generation
  • Question answering
  • Summarization and translation
With fine-tuning or prompt engineering, a GPT model can reliably map natural language questions (e.g., "Show me all sales from last quarter") into structured SQL statements. This directly satisfies the requirement of empowering non-technical employees to query terabytes of data without writing SQL themselves.

Why the Other Options Are Incorrect

  • Residual Neural Network (ResNet): Primarily used for image recognition and computer vision tasks. It has no inherent capability for natural language understanding or query generation.
  • Support Vector Machine (SVM): A classical supervised learning algorithm used for classification and regression on structured, tabular data. It cannot interpret free-form text or generate SQL.
  • WaveNet: A deep generative model developed by Google for audio synthesis (e.g., text-to-speech). It operates on raw audio waveforms and is completely unrelated to text or database query generation.

Community Consensus

All community voters (100%) selected A, and comments consistently highlight GPT's strength in natural language processing and its ability to be fine-tuned for text-to-SQL tasks, confirming this as the unambiguous correct answer.

Official Reference

https://platform.openai.com/docs/guides/gpt

Exam Strategy

When an exam question describes converting free-form human language into structured output (code, queries, commands), immediately look for generative AI or NLP-based models like GPT. Eliminate models tied to images, audio, or traditional classification tasks.

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