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Exam AIF-C01 Vce Format Reliable IT Certifications | AIF-C01: AWS Certified AI Practitioner

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Amazon AIF-C01 Exam Syllabus Topics:

Topic
Details

Topic 1

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 2

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 3

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 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

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 (Q141-Q146):

NEW QUESTION # 141
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?

A. Neural networks
B. Logistic regression
C. Linear regression
D. Decision trees

Answer: D

Explanation:
Decision trees are an interpretable machine learning algorithm that clearly documents the decision-making process by showing how each input feature affects the output. This transparency is particularly useful when explaining how the model arrives at a certain decision, making it suitable for classifying genes into categories.
* Option A (Correct): "Decision trees": This is the correct answer because decision trees provide a clear and interpretable representation of how input features influence the model's output, making it ideal for understanding the inner mechanisms affecting predictions.
* Option B: "Linear regression" is incorrect because it is used for regression tasks, not classification.
* Option C: "Logistic regression" is incorrect as it does not provide the same level of interpretability in documenting decision-making processes.
* Option D: "Neural networks" is incorrect because they are often considered "black boxes" and do not easily explain how they arrive at their outputs.
AWS AI Practitioner References:
* Interpretable Machine Learning Models on AWS: AWS supports using interpretable models, such as decision trees, for tasks that require clear documentation of how input data affects output decisions.

NEW QUESTION # 142
A manufacturing company uses AI to inspect products and find any damages or defects.
Which type of AI application is the company using?

A. Computer vision
B. Recommendation system
C. Natural language processing (NLP)
D. Image processing

Answer: A

Explanation:
The manufacturing company uses AI to inspect products for damages or defects, which involves analyzing visual data (e.g., images or videos of products). This task falls under computer vision, a type of AI application that enables machines to interpret and understand visual information, such as identifying defects in manufacturing.
Exact Extract from AWS AI Documents:
From the AWS AI Practitioner Learning Path:
"Computer vision enables machines to interpret and understand visual data from the world, such as images or videos. Common applications include defect detection in manufacturing, where AI models analyze product images to identify damages or anomalies." (Source: AWS AI Practitioner Learning Path, Module on AI Concepts) Detailed Explanation:
* Option A: Recommendation systemRecommendation systems suggest items or actions based on user preferences (e.g., product recommendations). They are not relevant for inspecting products for defects.
* Option B: Natural language processing (NLP)NLP focuses on processing and understanding text or speech, not visual data like product images. This option is incorrect.
* Option C: Computer visionThis is the correct answer. Computer vision is used for tasks like defect detection in manufacturing by analyzing visual data to identify damages or defects.
* Option D: Image processingWhile image processing involves manipulating images (e.g., filtering, resizing), it is a lower-level technique, not an AI application. Computer vision, which often uses image processing as a component, is the broader AI application here.
References:
AWS AI Practitioner Learning Path: Module on AI Concepts
Amazon Rekognition Developer Guide: Image Analysis (https://docs.aws.amazon.com/rekognition/latest/dg
/what-is.html)
AWS Documentation: Introduction to Computer Vision (https://aws.amazon.com/computer-vision/)

NEW QUESTION # 143
A bank is fine-tuning a large language model (LLM) on Amazon Bedrock to assist customers with questions about their loans. The bank wants to ensure that the model does not reveal any private customer data.
Which solution meets these requirements?

A. Use Amazon Bedrock Guardrails.
B. Remove personally identifiable information (PII) from the customer data before fine-tuning the LLM.
C. Increase the Top-K parameter of the LLM.
D. Store customer data in Amazon S3. Encrypt the data before fine-tuning the LLM.

Answer: B

Explanation:
The goal is to prevent a fine-tuned large language model (LLM) on Amazon Bedrock from revealing private customer data. Let's analyze the options:
* A. Amazon Bedrock Guardrails: Guardrails in Amazon Bedrock allow users to define policies to filter harmful or sensitive content in model inputs and outputs. While useful for real-time content moderation, they do not address the risk of private data being embedded in the model during fine- tuning, as the model could still memorize sensitive information.
* B. Remove personally identifiable information (PII) from the customer data before fine-tuning the LLM: Removing PII (e.g., names, addresses, account numbers) from the training dataset ensures that the model does not learn or memorize sensitive customer data, reducing the risk of data leakage.
This is a proactive and effective approach to data privacy during model training.
* C. Increase the Top-K parameter of the LLM: The Top-K parameter controls the randomness of the model's output by limiting the number of tokens considered during generation. Adjusting this parameter affects output diversity but does not address the privacy of customer data embedded in the model.
* D. Store customer data in Amazon S3. Encrypt the data before fine-tuning the LLM: Encrypting data in Amazon S3 protects data at rest and in transit, but during fine-tuning, the data is decrypted and used to train the model. If PII is present, the model could still learn and potentially expose it, so encryption alone does not solve the problem.
Exact Extract Reference: AWS emphasizes data privacy in AI/ML workflows, stating, "To protect sensitive data, you can preprocess datasets to remove personally identifiable information (PII) before using them for model training. This reduces the risk of models inadvertently learning or exposing sensitive information." (Source: AWS Best Practices for Responsible AI, https://aws.amazon.com/machine-learning/responsible-ai/).
Additionally, the Amazon Bedrock documentation notes that users are responsible for ensuring compliance with data privacy regulations during fine-tuning (https://docs.aws.amazon.com/bedrock/latest/userguide
/model-customization.html).
Removing PII before fine-tuning is the most direct and effective way to prevent the model from revealing private customer data, making B the correct answer.
:
AWS Bedrock Documentation: Model Customization (https://docs.aws.amazon.com/bedrock/latest/userguide
/model-customization.html)
AWS Responsible AI Best Practices (https://aws.amazon.com/machine-learning/responsible-ai/) AWS AI Practitioner Study Guide (emphasis on data privacy in LLM fine-tuning)

NEW QUESTION # 144
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?

A. Develop a multi-language translation system.
B. Develop a summarization chatbot.
C. Create a recommendation engine.
D. Build an automatic named entity recognition system.

Answer: B

Explanation:
A summarization chatbot is ideal for extracting key points from legal documents. Large language models (LLMs) can be used to summarize complex texts, such as legal documents, making them more accessible and understandable.
* Option C (Correct): "Develop a summarization chatbot": This is the correct answer because a summarization chatbot uses LLMs to condense and extract key information from text, which is precisely the requirement for reading and summarizing legal documents.
* Option A: "Build an automatic named entity recognition system" is incorrect because it focuses on identifying specific entities, not summarizing documents.
* Option B: "Create a recommendation engine" is incorrect as it is used to suggest products or content, not summarize text.
* Option D: "Develop a multi-language translation system" is incorrect because translation is unrelated to summarizing text.
AWS AI Practitioner References:
* Using LLMs for Text Summarization on AWS: AWS supports developing summarization tools using its AI services, including Amazon Bedrock.

NEW QUESTION # 145
A student at a university is copying content from generative AI to write essays.
Which challenge of responsible generative AI does this scenario represent?

A. Plagiarism
B. Privacy
C. Toxicity
D. Hallucinations

Answer: A

Explanation:
The scenario where a student copies content from generative AI to write essays represents the challenge of plagiarism in responsible AI use.
* Plagiarism:
* Occurs when someone uses content generated by AI (or any source) without proper attribution, claiming it as their own.
* This is a key challenge with generative AI models, which can produce human-like text that might be misused for academic or other purposes.
* Why Option C is Correct:
* Represents Unauthorized Use: Copying content directly from AI without attribution is a clear case of plagiarism.
* Ethical Concern: Highlights the ethical considerations around using AI-generated content responsibly.
* Why Other Options are Incorrect:
* A. Toxicity: Refers to harmful or offensive content generation, not content copying.
* B. Hallucinations: When AI generates incorrect or nonsensical information, not plagiarism.
* D. Privacy: Involves the misuse or exposure of personal information, not copying content.

NEW QUESTION # 146
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