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Amazon AIF-C01 Exam Syllabus Topics:

Topic
Details

Topic 1

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.

Topic 2

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 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

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 5

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.

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Amazon AWS Certified AI Practitioner Sample Questions (Q86-Q91):

NEW QUESTION # 86
A company wants to use a large language model (LLM) on Amazon Bedrock for sentiment analysis. The company wants to classify the sentiment of text passages as positive or negative.
Which prompt engineering strategy meets these requirements?

A. Provide the new text passage with a few examples of unrelated tasks, such as text summarization or question answering.
B. Provide a detailed explanation of sentiment analysis and how LLMs work in the prompt.
C. Provide the new text passage to be classified without any additional context or examples.
D. Provide examples of text passages with corresponding positive or negative labels in the prompt followed by the new text passage to be classified.

Answer: D

Explanation:
Providing examples of text passages with corresponding positive or negative labels in the prompt followed by the new text passage to be classified is the correct prompt engineering strategy for using a large language model (LLM) on Amazon Bedrock for sentiment analysis.
* Example-Driven Prompts:
* This strategy, known as few-shot learning, involves giving the model examples of input-output pairs (e.g., text passages with their sentiment labels) to help it understand the task context.
* It allows the model to learn from these examples and apply the learned pattern to classify new text passages correctly.
* Why Option A is Correct:
* Guides the Model: Providing labeled examples teaches the model how to perform sentiment analysis effectively, increasing accuracy.
* Contextual Relevance: Aligns the model's responses to the specific task of classifying sentiment.
* Why Other Options are Incorrect:
* B. Detailed explanation of sentiment analysis: Unnecessary for the model's operation; it requires examples, not explanations.
* C. New text passage without context: Provides no guidance or learning context for the model.
* D. Unrelated task examples: Would confuse the model and lead to inaccurate results.

NEW QUESTION # 87
Which option is a use case for generative AI models?

A. Creating photorealistic images from text descriptions for digital marketing
B. Improving network security by using intrusion detection systems
C. Analyzing financial data to forecast stock market trends
D. Enhancing database performance by using optimized indexing

Answer: A

Explanation:
Generative AI models are used to create new content based on existing data. One common use case is generating photorealistic images from text descriptions, which is particularly useful in digital marketing, where visual content is key to engaging potential customers.
Option B (Correct): "Creating photorealistic images from text descriptions for digital marketing": This is the correct answer because generative AI models, like those offered by Amazon Bedrock, can create images based on text descriptions, making them highly valuable for generating marketing materials.
Option A: "Improving network security by using intrusion detection systems" is incorrect because this is a use case for traditional machine learning models, not generative AI.
Option C: "Enhancing database performance by using optimized indexing" is incorrect as it is unrelated to generative AI.
Option D: "Analyzing financial data to forecast stock market trends" is incorrect because it typically involves predictive modeling rather than generative AI.
AWS AI Practitioner References:
Use Cases for Generative AI Models on AWS: AWS highlights the use of generative AI for creative content generation, including image creation, text generation, and more, which is suited for digital marketing applications.

NEW QUESTION # 88
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. Create Amazon SageMaker Model Cards with Intended uses and training and inference details.
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. Use AWS A] Service Cards for transparency and understanding models.

Answer: A

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 # 89
Which option describes embeddings in the context of AI?

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

Answer: D

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 # 90
A company deployed a model to production. After 4 months, the model inference quality degraded. The company wants to receive a notification if the model inference quality degrades. The company also wants to ensure that the problem does not happen again.
Which solution will meet these requirements?

A. Retrain the model. Monitor model drift by using Amazon SageMaker Clarify.
B. Retrain the model. Monitor model drift by using Amazon SageMaker Model Monitor.
C. Build a new model. Monitor model drift by using Amazon SageMaker JumpStart.
D. Build a new model. Monitor model drift by using Amazon SageMaker Feature Store.

Answer: B

Explanation:
The company needs to address the degradation in model inference quality after 4 months in production and prevent future occurrences by receiving notifications. Retraining the model can address the current degradation, likely caused by data drift (changes in the data distribution over time). Amazon SageMaker Model Monitor is designed to detect and monitor model drift, alerting the company when inference quality degrades, thus meeting both requirements.
Exact Extract from AWS AI Documents:
From the Amazon SageMaker Developer Guide:
"Amazon SageMaker Model Monitor enables you to monitor machine learning models in production for data drift, model performance degradation, and other quality issues. It can detect drift in feature distributions and inference quality, sending notifications when deviations are detected, allowing you to take corrective actions such as retraining the model." (Source: Amazon SageMaker Developer Guide, Monitoring Models with SageMaker Model Monitor) Detailed Explanation:
* Option A: Retrain the model. Monitor model drift by using Amazon SageMaker Clarify.
SageMaker Clarify is used for bias detection and explainability, not for monitoring model drift or inference quality in production. This option does not fully meet the requirements.
* Option B: Retrain the model. Monitor model drift by using Amazon SageMaker Model Monitor.
This is the correct answer. Retraining addresses the current degradation, and SageMaker Model Monitor can detect future drift in inference quality, sending notifications to prevent recurrence, as required.
* Option C: Build a new model. Monitor model drift by using Amazon SageMaker Feature Store.
SageMaker Feature Store is for managing and sharing features, not for monitoring model drift or inference quality. Building a new model may not be necessary if retraining can address the issue.
* Option D: Build a new model. Monitor model drift by using Amazon SageMaker JumpStart.
SageMaker JumpStart provides pre-trained models and solutions for quick deployment, but it does not offer specific tools for monitoring model drift or inference quality in production.
References:
Amazon SageMaker Developer Guide: Monitoring Models with SageMaker Model Monitor (https://docs.aws.
amazon.com/sagemaker/latest/dg/model-monitor.html)
AWS AI Practitioner Learning Path: Module on Model Monitoring and Maintenance AWS Documentation: Addressing Model Drift in Production (https://aws.amazon.com/sagemaker/)

NEW QUESTION # 91
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