AIF-C01 Exam Dumps : AWS Certified AI Practitioner Exam(AI1-C01)

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.

To determine customer sentiments based on written customer reviews, the company can use Amazon Comprehend and Amazon Bedrock.

Amazon Comprehend:

A natural language processing (NLP) service that uses machine learning to uncover insights and relationships in text.

Can analyze customer reviews to detect sentiments (positive, negative, neutral, or mixed) automatically.

Amazon Bedrock:

Provides access to foundational models (FMs) from multiple AI companies for tasks such as text generation, summarization, and sentiment analysis.

The company can use a pre-trained sentiment analysis model available on Amazon Bedrock for processing customer reviews.

Why Other Options are Incorrect:

A. Amazon Lex:Used for building conversational interfaces like chatbots, not for sentiment analysis.

C. Amazon Polly:Converts text to speech; it doesn't analyze sentiment.

E. Amazon Rekognition:Analyzes images and videos, not text.

Decreasing the number of tokens in the prompt reduces the cost associated with using an LLM model on Amazon Bedrock, as costs are often based on the number of tokens processed by the model.

Token Reduction Strategy:

By decreasing the number of tokens (words or characters) in each prompt, the company reduces the computational load and, therefore, the cost associated with invoking the model.

Since the model is performing well with few-shot prompting, reducing token usage without sacrificing performance can lower monthly costs.

Why Option B is Correct:

Cost Efficiency:Directly reduces the number of tokens processed, lowering costs without requiring additional adjustments.

Maintaining Performance:If the model is already performing well, a reduction in tokens should not significantly impact its performance.

Why Other Options are Incorrect:

A. Fine-tuning:Can be costly and time-consuming and is not needed if the current model is already performing well.

C. Increase the number of tokens:Would increase costs, not lower them.

D. Use Provisioned Throughput:Is unrelated to token costs and applies more to read/write capacity in databases.