Amazon AIF-C01 Practice Test - Questions Answers, Page 9
List of questions
Question 81
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A company wants to deploy a conversational chatbot to answer customer questions. The chatbot is based on a fine-tuned Amazon SageMaker JumpStart model. The application must comply with multiple regulatory frameworks.
Which capabilities can the company show compliance for? (Select TWO.)
Question 82
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A company has a database of petabytes of unstructured data from internal sources. The company wants to transform this data into a structured format so that its data scientists can perform machine learning (ML) tasks.
Which service will meet these requirements?
Question 83
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A company has thousands of customer support interactions per day and wants to analyze these interactions to identify frequently asked questions and develop insights.
Which AWS service can the company use to meet this requirement?
Question 84
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A company is training a foundation model (FM). The company wants to increase the accuracy of the model up to a specific acceptance level.
Which solution will meet these requirements?
Question 85
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A company has built a chatbot that can respond to natural language questions with images. The company wants to ensure that the chatbot does not return inappropriate or unwanted images.
Which solution will meet these requirements?
Question 86
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A law firm wants to build an AI application by using large language models (LLMs). The application will read legal documents and extract key points from the documents.
Which solution meets these requirements?
Question 87
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A company wants to classify human genes into 20 categories based on gene characteristics. The company needs an ML algorithm to document how the inner mechanism of the model affects the output.
Which ML algorithm meets these requirements?
Question 88
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What does an F1 score measure in the context of foundation model (FM) performance?
Model precision and recall
Model speed in generating responses
Financial cost of operating the model
Energy efficiency of the model's computations
Explanation:
The F1 score is a metric used to evaluate the performance of a classification model by considering both precision and recall. Precision measures the accuracy of positive predictions (i.e., the proportion of true positive predictions among all positive predictions made by the model), while recall measures the model's ability to identify all relevant positive instances (i.e., the proportion of true positive predictions among all actual positive instances). The F1 score is the harmonic mean of precision and recall, providing a single metric that balances both concerns. This is particularly useful when dealing with imbalanced datasets or when the cost of false positives and false negatives is significant. Options B, C, and D pertain to other aspects of model performance but are not related to the F1 score.
Question 89
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Which AWS feature records details about ML instance data for governance and reporting?
Amazon SageMaker Model Cards
Amazon SageMaker Debugger
Amazon SageMaker Model Monitor
Amazon SageMaker JumpStart
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.
Question 90
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Which option is a benefit of using Amazon SageMaker Model Cards to document AI models?
Providing a visually appealing summary of a model's capabilities.
Standardizing information about a model's purpose, performance, and limitations.
Reducing the overall computational requirements of a model.
Physically storing models for archival purposes.
Explanation:
Amazon SageMaker Model Cards provide a standardized way to document important details about an AI model, such as its purpose, performance, intended usage, and known limitations. This enables transparency and compliance while fostering better communication between stakeholders. It does not store models physically or optimize computational requirements.
Reference: AWS SageMaker Model Cards Documentation.
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