AWS Certified Cloud Practitioner

Explanation:

For a mobile app designed for visually impaired users that needs to understand spoken input and provide voice responses, the core requirement is speech recognition (converting speech to text) and potentially text-to-speech capabilities.

Let's analyze each option:

A. Use a deep learning neural network to perform speech recognition. ✅

• Correct: Deep learning neural networks are highly effective for speech recognition tasks. They can accurately convert spoken language into text, which is essential for understanding user commands.
• Modern speech recognition systems like Amazon Transcribe, Google Speech-to-Text, or Apple's Siri use deep learning models for this purpose.

B. Build ML models to search for patterns in numeric data. ❌

• Incorrect: This is for analyzing numerical datasets (like financial data, sensor readings) to find trends or anomalies, not for processing audio input.

C. Use generative AI summarization to generate human-like text. ❌

• Incorrect: While generative AI could help create responses, it doesn't address the core requirement of understanding spoken input. This is more about text generation than speech recognition.

D. Build custom models for image classification and recognition. ❌

• Incorrect: This is completely unrelated to the requirements. Image classification is for visual data, while the app needs to process audio input for visually impaired users.

Key Points:

1. The primary requirement is speech-to-text conversion to understand user commands.
2. Deep learning neural networks (particularly recurrent neural networks and transformers) excel at speech recognition tasks.
3. After understanding the speech, the app would also need text-to-speech capabilities to provide voice responses, though this wasn't explicitly mentioned in the options.
4. AWS services like Amazon Transcribe (for speech-to-text) and Amazon Polly (for text-to-speech) would be appropriate solutions for such an application.