AIF-C01 Latest Practice Materials, Vce AIF-C01 Torrent

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Amazon AWS Certified AI Practitioner Sample Questions (Q73-Q78):

NEW QUESTION # 73
A social media company wants to use a large language model (LLM) for content moderation. The company wants to evaluate the LLM outputs for bias and potential discrimination against specific groups or individuals.
Which data source should the company use to evaluate the LLM outputs with the LEAST administrative effort?

A. Content moderation guidelines
B. User-generated content
C. Moderation logs
D. Benchmark datasets

Answer: D

Explanation:
Benchmark datasets are pre-validated datasets specifically designed to evaluate machine learning models for bias, fairness, and potential discrimination. These datasets are the most efficient tool for assessing an LLM's performance against known standards with minimal administrative effort.
* Option D (Correct): "Benchmark datasets": This is the correct answer because using standardized benchmark datasets allows the company to evaluate model outputs for bias with minimal administrative overhead.
* Option A: "User-generated content" is incorrect because it is unstructured and would require significant effort to analyze for bias.
* Option B: "Moderation logs" is incorrect because they represent historical data and do not provide a standardized basis for evaluating bias.
* Option C: "Content moderation guidelines" is incorrect because they provide qualitative criteria rather than a quantitative basis for evaluation.
AWS AI Practitioner References:
* Evaluating AI Models for Bias on AWS: AWS supports using benchmark datasets to assess model fairness and detect potential bias efficiently.

NEW QUESTION # 74
A company is using the Generative AI Security Scoping Matrix to assess security responsibilities for its solutions. The company has identified four different solution scopes based on the matrix.
Which solution scope gives the company the MOST ownership of security responsibilities?

A. Refining an existing third-party generative AI foundation model (FM) by fine-tuning the model by using data specific to the business.
B. Using a third-party enterprise application that has embedded generative AI features.
C. Building and training a generative AI model from scratch by using specific data that a customer owns.
D. Building an application by using an existing third-party generative AI foundation model (FM).

Answer: C

Explanation:
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.

NEW QUESTION # 75
Which metric measures the runtime efficiency of operating AI models?

A. Customer satisfaction score (CSAT)
B. Average response time
C. Number of training instances
D. Training time for each epoch

Answer: B

Explanation:
The average response time is the correct metric for measuring the runtime efficiency of operating AI models.
* Average Response Time:
* Refers to the time taken by the model to generate an output after receiving an input. It is a key metric for evaluating the performance and efficiency of AI models in production.
* A lower average response time indicates a more efficient model that can handle queries quickly.
* Why Option C is Correct:
* Measures Runtime Efficiency: Directly indicates how fast the model processes inputs and delivers outputs, which is critical for real-time applications.
* Performance Indicator: Helps identify potential bottlenecks and optimize model performance.
* Why Other Options are Incorrect:
* A. Customer satisfaction score (CSAT): Measures customer satisfaction, not model runtime efficiency.
* B. Training time for each epoch: Measures training efficiency, not runtime efficiency during model operation.
* D. Number of training instances: Refers to data used during training, not operational efficiency.

NEW QUESTION # 76
Which AWS feature records details about ML instance data for governance and reporting?

A. Amazon SageMaker JumpStart
B. Amazon SageMaker Debugger
C. Amazon SageMaker Model Monitor
D. Amazon SageMaker Model Cards

Answer: D

Explanation:
Amazon SageMaker Model Cards provide a centralized and standardized repository for documenting machine learning models. They capture key details such as the model's intended use, training and evaluation datasets, performance metrics, ethical considerations, and other relevant information. This documentation facilitates governance and reporting by ensuring that all stakeholders have access to consistent and comprehensive information about each model. While Amazon SageMaker Debugger is used for real-time debugging and monitoring during training, and Amazon SageMaker Model Monitor tracks deployed models for data and prediction quality, neither offers the comprehensive documentation capabilities of Model Cards. Amazon SageMaker JumpStart provides pre-built models and solutions but does not focus on governance documentation.

NEW QUESTION # 77
A financial institution is using Amazon Bedrock to develop an AI application. The application is hosted in a VPC. To meet regulatory compliance standards, the VPC is not allowed access to any internet traffic.
Which AWS service or feature will meet these requirements?

A. AWS PrivateLink
B. Internet gateway
C. Amazon CloudFront
D. Amazon Macie

Answer: A

NEW QUESTION # 78
......

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AIF-C01 Latest Practice Materials, Vce AIF-C01 Torrent

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Amazon AWS Certified AI Practitioner Sample Questions (Q73-Q78):

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