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Amazon AIF-C01 Exam Syllabus Topics:

Topic
Details

Topic 1

Security, Compliance, and Governance for AI Solutions: This domain covers the security measures, compliance requirements, and governance practices essential for managing AI solutions. It targets security professionals, compliance officers, and IT managers responsible for safeguarding AI systems, ensuring regulatory compliance, and implementing effective governance frameworks.

Topic 2

Applications of Foundation Models: This domain examines how foundation models, like large language models, are used in practical applications. It is designed for those who need to understand the real-world implementation of these models, including solution architects and data engineers who work with AI technologies to solve complex problems.

Topic 3

Fundamentals of Generative AI: This domain explores the basics of generative AI, focusing on techniques for creating new content from learned patterns, including text and image generation. It targets professionals interested in understanding generative models, such as developers and researchers in AI.

Topic 4

Fundamentals of AI and ML: This domain covers the fundamental concepts of artificial intelligence (AI) and machine learning (ML), including core algorithms and principles. It is aimed at individuals new to AI and ML, such as entry-level data scientists and IT professionals.

Topic 5

Guidelines for Responsible AI: This domain highlights the ethical considerations and best practices for deploying AI solutions responsibly, including ensuring fairness and transparency. It is aimed at AI practitioners, including data scientists and compliance officers, who are involved in the development and deployment of AI systems and need to adhere to ethical standards.

Amazon AWS Certified AI Practitioner Sample Questions (Q64-Q69):

NEW QUESTION # 64
Which option describes embeddings in the context of AI?

A. A method for compressing large datasets
B. A numerical method for data representation in a reduced dimensionality space
C. An encryption method for securing sensitive data
D. A method for visualizing high-dimensional data

Answer: B

Explanation:
Embeddings in AI refer to numerical representations of data (e.g., text, images) in a lower-dimensional space, capturing semantic or contextual relationships. They are widely used in NLP and other AI tasks to represent complex data in a format that models can process efficiently.
Exact Extract from AWS AI Documents:
From the AWS AI Practitioner Learning Path:
"Embeddings are numerical representations of data in a reduced dimensionality space. In natural language processing, for example, word or sentence embeddings capture semantic relationships, enabling models to process text efficiently for tasks like classification or similarity search." (Source: AWS AI Practitioner Learning Path, Module on AI Concepts) Detailed Explanation:
* Option A: A method for compressing large datasetsWhile embeddings reduce dimensionality, their primary purpose is not data compression but rather to represent data in a way that preserves meaningful relationships. This option is incorrect.
* Option B: An encryption method for securing sensitive dataEmbeddings are not related to encryption or data security. They are used for data representation, making this option incorrect.
* Option C: A method for visualizing high-dimensional dataWhile embeddings can sometimes be used in visualization (e.g., t-SNE), their primary role is data representation for model processing, not visualization. This option is misleading.
* Option D: A numerical method for data representation in a reduced dimensionality spaceThis is the correct answer. Embeddings transform complex data into lower-dimensional numerical vectors, preserving semantic or contextual information for use in AI models.
References:
AWS AI Practitioner Learning Path: Module on AI Concepts
Amazon Comprehend Developer Guide: Embeddings for Text Analysis (https://docs.aws.amazon.com
/comprehend/latest/dg/embeddings.html)
AWS Documentation: What are Embeddings? (https://aws.amazon.com/what-is/embeddings/)

NEW QUESTION # 65
An ML research team develops custom ML models. The model artifacts are shared with other teams for integration into products and services. The ML team retains the model training code and data. The ML team wants to builk a mechanism that the ML team can use to audit models.
Which solution should the ML team use when publishing the custom ML models?

A. Use AWS A] Service Cards for transparency and understanding models.
B. Create model training scripts. Commit the model training scripts to a Git repository.
C. Create documents with the relevant information. Store the documents in Amazon S3.
D. Create Amazon SageMaker Model Cards with Intended uses and training and inference details.

Answer: D

Explanation:
The ML research team needs a mechanism to audit custom ML models while sharing model artifacts with other teams. Amazon SageMaker Model Cards provide a structured way todocument model details, including intended uses, training data, and inference performance, making them ideal for auditing and ensuring transparency when publishing models.
Exact Extract from AWS AI Documents:
From the Amazon SageMaker Developer Guide:
"Amazon SageMaker Model Cards enable you to document critical details about your machine learning models, such as intended uses, training data, evaluation metrics, and inference details. Model Cards support auditing by providing a centralized record that can be reviewed by teams to understand model behavior and limitations." (Source: Amazon SageMaker Developer Guide, SageMaker Model Cards) Detailed Explanation:
* Option A: Create documents with the relevant information. Store the documents in Amazon S3.
While storing documents in S3 is feasible, it lacks the structured format and integration with SageMaker that Model Cards provide, making it less suitable for auditing purposes.
* Option B: Use AWS AI Service Cards for transparency and understanding models.AWS AI Service Cards are not a standard feature in AWS documentation. This option appears to be a distractor and is not a valid solution.
* Option C: Create Amazon SageMaker Model Cards with Intended uses and training and inference details.This is the correct answer. SageMaker Model Cards are specifically designed to document model details for auditing, transparency, and collaboration, meeting the team's requirements.
* Option D: Create model training scripts. Commit the model training scripts to a Git repository.
Sharing training scripts in a Git repository provides access to code but does not offer a structured auditing mechanism for model details like intended uses or inference performance.
References:
Amazon SageMaker Developer Guide: SageMaker Model Cards (https://docs.aws.amazon.com/sagemaker
/latest/dg/model-cards.html)
AWS AI Practitioner Learning Path: Module on Model Governance and Auditing AWS Documentation: Responsible AI with SageMaker (https://aws.amazon.com/sagemaker/)

NEW QUESTION # 66
A company is building a mobile app for users who have a visual impairment. The app must be able to hear what users say and provide voice responses.
Which solution will meet these requirements?

A. Use generative AI summarization to generate human-like text.
B. Build ML models to search for patterns in numeric data.
C. Use a deep learning neural network to perform speech recognition.
D. Build custom models for image classification and recognition.

Answer: C

Explanation:
The mobile app for users with visual impairment needs to hear user speech and provide voice responses, requiring speech-to-text (speech recognition) and text-to-speech capabilities. Deep learning neural networks are widely used for speech recognition tasks, as they can effectively process and transcribe spoken language. AWS services like Amazon Transcribe, which uses deep learning for speech recognition, can fulfill this requirement by converting user speech to text, and Amazon Polly can generate voice responses.
Exact Extract from AWS AI Documents:
From the AWS Documentation on Amazon Transcribe:
"Amazon Transcribe uses deep learning neural networks to perform automatic speech recognition (ASR), converting spoken language into text with high accuracy. This is ideal for applications requiring voice input, such as accessibility features for visually impaired users." (Source: Amazon Transcribe Developer Guide, Introduction to Amazon Transcribe) Detailed Option A: Use a deep learning neural network to perform speech recognition.This is the correct answer. Deep learning neural networks are the foundation of modern speech recognition systems, as used in AWS services like Amazon Transcribe. They enable the app to hear and transcribe user speech, and a service like Amazon Polly can handle voice responses, meeting the requirements.
Option B: Build ML models to search for patterns in numeric data.This option is irrelevant, as the task involves processing speech (audio data) and generating voice responses, not analyzing numeric data patterns.
Option C: Use generative AI summarization to generate human-like text.Generative AI summarization focuses on summarizing text, not processing speech orgenerating voice responses. This option does not address the core requirement of speech recognition.
Option D: Build custom models for image classification and recognition.Image classification and recognition are unrelated to processing speech or generating voice responses, making this option incorrect for an app focused on audio interaction.
Reference:
Amazon Transcribe Developer Guide: Introduction to Amazon Transcribe (https://docs.aws.amazon.com/transcribe/latest/dg/what-is.html) Amazon Polly Developer Guide: Text-to-Speech Overview (https://docs.aws.amazon.com/polly/latest/dg/what-is.html) AWS AI Practitioner Learning Path: Module on Speech Recognition and Synthesis

NEW QUESTION # 67
An AI practitioner is using an Amazon Bedrock base model to summarize session chats from the customer service department. The AI practitioner wants to store invocation logs to monitor model input and output data.
Which strategy should the AI practitioner use?

A. Configure AWS CloudTrail as the logs destination for the model.
B. Configure model invocation logging in Amazon EventBridge.
C. Enable invocation logging in Amazon Bedrock.
D. Configure AWS Audit Manager as the logs destination for the model.

Answer: C

Explanation:
Amazon Bedrock provides an option to enable invocation logging to capture and store the input and output data of the models used. This is essential for monitoring and auditing purposes, particularly when handling customer data.
* Option B (Correct): "Enable invocation logging in Amazon Bedrock": This is the correct answer as it directly enables the logging of all model invocations, ensuring transparency and traceability.
* Option A: "Configure AWS CloudTrail" is incorrect because CloudTrail logs API calls but does not provide specific logging for model inputs and outputs.
* Option C: "Configure AWS Audit Manager" is incorrect as Audit Manager is used for compliance reporting, not specific invocation logging for AI models.
* Option D: "Configure model invocation logging in Amazon EventBridge" is incorrect as EventBridge is for event-driven architectures, not specifically designed for logging AI model inputs and outputs.
AWS AI Practitioner References:
* Amazon Bedrock Logging Capabilities: AWS emphasizes using built-in logging features in Bedrock to maintain data integrity and transparency in model operations.

NEW QUESTION # 68
A retail store wants to predict the demand for a specific product for the next few weeks by using the Amazon SageMaker DeepAR forecasting algorithm.
Which type of data will meet this requirement?

A. Binary data
B. Image data
C. Time series data
D. Text data

Answer: C

Explanation:
Amazon SageMaker's DeepAR is a supervised learning algorithm designed for forecasting scalar (one- dimensional) time series data. Time series data consists of sequences of data points indexed in time order, typically with consistent intervals between them. In the context of a retail store aiming to predict product demand, relevant time series data might include historical sales figures, inventory levels, or related metrics recorded over regular time intervals (e.g., daily or weekly). By training the DeepAR model on this historical time series data, the store can generate forecasts for future product demand. This capability is particularly useful for inventory management, staffing, and supply chain optimization. Other data types, such as text, image, or binary data, are not suitable for time series forecasting tasks and would not be appropriate inputs for the DeepAR algorithm.
Reference: Amazon SageMaker DeepAR Algorithm

NEW QUESTION # 69
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Alan Cook Alan Cook

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