Helping Hand Questions for AIF-C01

Generative Pre-trained Transformers (GPT) are suitable for building an AI-based application that can generate SQL queries from natural language input provided by employees.

GPT for Natural Language Processing:

GPT models are designed for understanding and generating human-like text based on natural language input.

They can be fine-tuned to interpret specific tasks, such as converting natural language queries into SQL queries.

Why Option A is Correct:

Natural Language Understanding:GPT is highly effective for tasks that require understanding of human language and generating structured outputs like SQL.

User-Friendly:Requires minimal technology experience from employees, as they provide simple text input.

Why Other Options are Incorrect:

B. Residual neural network:Typically used in computer vision tasks, not for natural language-to-SQL conversion.

C. Support vector machine:Used for classification tasks, not for generating structured queries from text.

D. WaveNet:A deep generative model for audio data, unrelated to text-to-SQL tasks.

Real-time inference is designed to provide immediate, low-latency predictions, which is necessary when the company requires near real-time latency for its ML models. This option is optimal when there is a need for fast responses, even with large input data sizes and substantial processing times.

Option A (Correct): "Real-time inference":This is the correct answer because it supports low-latency requirements, which are essential for real-time applications where quick response times are needed.

Option B:"Serverless inference" is incorrect because it is more suited for intermittent, small-scale inference workloads, not for continuous, large-scale, low-latency needs.

Option C:"Asynchronous inference" is incorrect because it is used for workloads that do not require immediate responses.

Option D:"Batch transform" is incorrect as it is intended for offline, large-batch processing where immediate response is not necessary.

AWS AI Practitioner References:

Amazon SageMaker Inference Options:AWS documentation describes real-time inference as the best solution for applications that require immediate prediction results with low latency.

Amazon Textract is a service that automatically extracts text and data from scanned documents, including PDFs. It is the best choice for converting resumes from PDF format to plain text for further processing.

Amazon Textract:

Extracts text, forms, and tables from scanned documents accurately.

Ideal for automating the process of converting PDF resumes into plain text format.

Why Option A is Correct:

Automation of Text Extraction:Textract is designed to handle large volumes of documents and convert them into machine-readable text, perfect for the company's need.

Scalability and Efficiency:Supports scalability to handle a growing volume of resumes as the company expands.

Why Other Options are Incorrect:

B. Amazon Personalize:Used for creating personalized recommendations, not for text extraction.

C. Amazon Lex:A service for building conversational interfaces, not for processing documents.

D. Amazon Transcribe:Used for converting speech to text, not for extracting text from documents.

Amazon Transcribe is the correct solution for converting audio from customer calls into text, allowing the company to analyze and extract key information from the conversations.

Amazon Transcribe:

It is a fully managed automatic speech recognition (ASR) service that converts speech into text, making it easier to perform text-based analysis on audio data.

After transcribing the audio, further analysis can be performed using other AWS services like Amazon Comprehend to extract insights such as sentiment, key phrases, or entities.

Why Option B is Correct:

Conversion to Text:Transcribing audio recordings is the first step in gaining insights from spoken conversations, allowing for further processing.

Enables Further Analysis:Once the audio is transcribed into text, other tools and services can be used to analyze the content more deeply.

Why Other Options are Incorrect:

A. Amazon Lex:Is used for building conversational interfaces, not for transcribing or analyzing audio from customer calls.

C. Amazon SageMaker Model Monitor:Monitors ML models for bias and data drift, not for audio analysis.

D. Amazon Comprehend:Can analyze text but cannot transcribe audio; it would be used after transcription is completed.