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Reliable AIF-C01 Test Cost - AIF-C01 Intereactive Testing Engine

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Reliable AIF-C01 Test Cost - AIF-C01 Intereactive Testing Engine

Posted at yesterday 16:07      View：14 | Replies：0        Print      Only Author   [Copy Link] 1#
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Amazon AIF-C01 Exam Syllabus Topics:
TopicDetails
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
  • 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 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
  • 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.

Amazon AWS Certified AI Practitioner Sample Questions (Q100-Q105):NEW QUESTION # 100
A company wants to label training datasets by using human feedback to fine-tune a foundation model (FM). The company does not want to develop labeling applications or manage a labeling workforce. Which AWS service or feature meets these requirements?
  • A. Amazon SageMaker Data Wrangler
  • B. Amazon Macie
  • C. Amazon SageMaker Ground Truth Plus
  • D. Amazon Transcribe
Answer: C
Explanation:
Comprehensive and Detailed
Amazon SageMaker Ground Truth Plus provides a fully managed data labeling service where AWS manages the workforce, tools, and processes.
Data Wrangler is for data preparation and transformation.
Transcribe is for speech-to-text.
Macie is for sensitive data discovery, not labeling.
Reference:
AWS Documentation - SageMaker Ground Truth Plus

NEW QUESTION # 101
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. Increase the Top-K parameter of the LLM.
  • B. Use Amazon Bedrock Guardrails.
  • C. Remove personally identifiable information (PII) from the customer data before fine-tuning the LLM.
  • D. Store customer data in Amazon S3. Encrypt the data before fine-tuning the LLM.
Answer: C
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 # 102
A company deploys a custom ML model on Amazon SageMaker AI. The company uses the model to build a generative AI application for a healthcare recommendation system.
The company tests the application and finds a potential bias issue. The application consistently recommends different treatment approaches for patients who have identical medical conditions based on patient demographic information.
The company needs a solution to ensure that the application does not generate biased recommendations.
Which solution will meet this requirement?
  • A. Apply content filtering by using Amazon Comprehend to remove potentially biased recommendations before they reach users.
  • B. Use SageMaker Clarify to detect bias patterns. Collect and use additional balanced training data. Use the data to retrain the model.
  • C. Create separate foundation model (FM) endpoints for each demographic group to provide specialized care recommendations.
  • D. Implement prompt engineering techniques to explicitly instruct the model to provide fair recommendations regardless of demographics.
Answer: B
Explanation:
Comprehensive and Detailed Explanation (AWS AI documents):
AWS Responsible AI best practices emphasize that bias should be detected, measured, mitigated, and monitored throughout the ML lifecycle, especially for sensitive domains such as healthcare. When biased outcomes are observed, AWS guidance recommends addressing bias at the data and model level, not only at the output level.
Using Amazon SageMaker Clarify aligns directly with AWS Responsible AI principles because it is designed to:
* Detect and quantify bias in datasets and model predictions across sensitive attributes such as demographic groups
* Provide pre-training and post-training bias metrics, allowing practitioners to identify where bias originates
* Support data-centric mitigation, including improving dataset balance and representativeness After identifying bias with SageMaker Clarify, collecting additional balanced training data and retraining the model helps ensure that:
* The model learns from a more representative dataset
* Disparate treatment recommendations based on demographics are reduced
* Fairness is improved while maintaining clinical accuracy
Why the other options are not sufficient or aligned with AWS best practices:
* B. Prompt engineering can influence outputs but does not address underlying data or model bias and is not sufficient for regulated, high-risk domains like healthcare.
* C. Content filtering removes outputs after generation but does not prevent biased decision-making by the model itself.
* D. Separate FM endpoints by demographic group increases the risk of reinforcing bias and violates fairness principles rather than mitigating them.
AWS AI Study Guide References:
* AWS Responsible AI principles: Fairness and Governance
* Amazon SageMaker Clarify: bias detection and mitigation
* AWS best practices for ML in high-risk domains such as healthcare

NEW QUESTION # 103
A company is developing an ML model to predict customer churn.
Which evaluation metric will assess the model's performance on a binary classification task such as predicting chum?
  • A. R-squared
  • B. F1 score
  • C. Mean squared error (MSE)
  • D. Time used to train the model
Answer: B
Explanation:
The company is developing an ML model to predict customer churn, a binary classification task (churn or no churn). The F1 score is an evaluation metric that balances precision and recall, making it suitable for assessing the performance of binary classification models, especially when dealing with imbalanced datasets, which is common in churn prediction.
Exact Extract from AWS AI Documents:
From the Amazon SageMaker Developer Guide:
"The F1 score is a metric for evaluating binary classification models, combining precision and recall into a single value. It is particularly useful for tasks like churn prediction, where class imbalance may exist, ensuring the model performs well on both positive and negative classes." (Source: Amazon SageMaker Developer Guide, Model Evaluation Metrics) Detailed Explanation:
Option A: F1 scoreThis is the correct answer. The F1 score is ideal for binary classification tasks like churn prediction, as it measures the model's ability to correctly identify both churners and non-churners.
Option B: Mean squared error (MSE)MSE is used for regression tasks to measure the average squared difference between predicted and actual values, not for binary classification.
Option C: R-squaredR-squared is a metric for regression models, indicating how well the model explains the variability of the target variable. It is not applicable to classification tasks.
Option D: Time used to train the modelTraining time is not an evaluation metric for model performance; it measures the duration of training, not the model's accuracy or effectiveness.
References:
Amazon SageMaker Developer Guide: Model Evaluation Metrics (https://docs.aws.amazon.com/sagemaker
/latest/dg/model-evaluation.html)
AWS AI Practitioner Learning Path: Module on Model Performance and Evaluation AWS Documentation: Metrics for Classification (https://aws.amazon.com/machine-learning/)

NEW QUESTION # 104
A company needs to build its own large language model (LLM) based on only the company's private dat a. The company is concerned about the environmental effect of the training process.
Which Amazon EC2 instance type has the LEAST environmental effect when training LLMs?
  • A. Amazon EC2 P series
  • B. Amazon EC2 G series
  • C. Amazon EC2 C series
  • D. Amazon EC2 Trn series
Answer: D
Explanation:
The Amazon EC2 Trn series (Trainium) instances are designed for high-performance, cost-effective machine learning training while being energy-efficient. AWS Trainium-powered instances are optimized for deep learning models and have been developed to minimize environmental impact by maximizing energy efficiency.
Option D (Correct): "Amazon EC2 Trn series": This is the correct answer because the Trn series is purpose-built for training deep learning models with lower energy consumption, which aligns with the company's concern about environmental effects.
Option A: "Amazon EC2 C series" is incorrect because it is intended for compute-intensive tasks but not specifically optimized for ML training with environmental considerations.
Option B: "Amazon EC2 G series" (Graphics Processing Unit instances) is optimized for graphics-intensive applications but does not focus on minimizing environmental impact for training.
Option C: "Amazon EC2 P series" is designed for ML training but does not offer the same level of energy efficiency as the Trn series.
AWS AI Practitioner Reference:
AWS Trainium Overview: AWS promotes Trainium instances as their most energy-efficient and cost-effective solution for ML model training.

NEW QUESTION # 105
......
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Posted at yesterday 20:46        Only Author  2#
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