Amazon Web Services Updated AIF-C01 Exam Questions and Answers by cairo

To securely use large language models (LLMs) on Amazon Bedrock, companies should design clear and specific prompts to avoid unintended outputs and ensure proper configuration of AWS Identity and Access Management (IAM) roles and policies with the principle of least privilege. This approach limits access to sensitive resources and minimizes the potential impact of security incidents.

Option A (Correct): "Design clear and specific prompts. Configure AWS Identity and Access Management (IAM) roles and policies by using least privilege access":This is the correct answer as it directly addresses both security practices in prompt design and access management.

Option B:"Enable AWS Audit Manager for automatic model evaluation jobs" is incorrect because Audit Manager is for compliance and auditing, not directly related to secure LLM usage.

Option C:"Enable Amazon Bedrock automatic model evaluation jobs" is incorrect because Bedrock does not provide automatic model evaluation jobs specifically for security purposes.

Option D:"Use Amazon CloudWatch Logs to make models explainable and to monitor for bias" is incorrect because CloudWatch Logs are used for monitoring and not directly for making models explainable or secure.

AWS AI Practitioner References:

Secure AI Practices on AWS:AWS recommends configuring IAM roles and using least privilege access to ensure secure usage of AI models.

Exploratory data analysis (EDA) involves understanding the data by visualizing it, calculating statistics, and creating correlation matrices. This stage helps identify patterns, relationships, and anomalies in the data, which can guide further steps in the ML pipeline.

Option C (Correct): "Exploratory data analysis":This is the correct answer as the tasks described (correlation matrix, calculating statistics, visualizing data) are all part of the EDA process.

Option A:"Data pre-processing" is incorrect because it involves cleaning and transforming data, not initial analysis.

Option B:"Feature engineering" is incorrect because it involves creating new features from raw data, not analyzing the data's existing structure.

Option D:"Hyperparameter tuning" is incorrect because it refers to optimizing model parameters, not analyzing the data.

AWS AI Practitioner References:

Stages of the Machine Learning Pipeline:AWS outlines EDA as the initial phase of understanding and exploring data before moving to more specific preprocessing, feature engineering, and model training stages.

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.

Building and training a generative AI model from scratch provides the company with the most ownership and control over security responsibilities. In this scenario, the company is responsible for all aspects of the security of the data, the model, and the infrastructure.

Option D (Correct): "Building and training a generative AI model from scratch by using specific data that a customer owns":This is the correct answer because it involves complete ownership of the model, data, and infrastructure, giving the company the highest level of responsibility for security.

Option A:"Using a third-party enterprise application that has embedded generative AI features" is incorrect as the company has minimal control over the security of the AI features embedded within a third-party application.

Option B:"Building an application using an existing third-party generative AI foundation model (FM)" is incorrect because security responsibilities are shared with the third-party model provider.

Option C:"Refining an existing third-party generative AI FM by fine-tuning the model with business-specific data" is incorrect as the foundation model and part of the security responsibilities are still managed by the third party.

AWS AI Practitioner References:

Generative AI Security Scoping Matrix on AWS:AWS provides a security responsibility matrix that outlines varying levels of control and responsibility depending on the approach to developing and using AI models.