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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI/ML Fundamentals | 24% | - Understand the AI/ML lifecycle
|
| Topic 2: Responsible AI | 20% | - Understand governance and compliance requirements
|
| Topic 3: AI Application Development | 30% | - Evaluate and optimize AI applications
|
| Topic 4: AWS AI Services Overview | 26% | - Describe Amazon Bedrock capabilities
|
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NEW QUESTION # 341
A law firm wants to use Amazon Bedrock foundation models (FMs) to review and negotiate business contracts. The law firm requires customer data and business information to be protected.
Which option is a potential legal risk of this use case?
Answer: C
Explanation:
Incorrect contract analysis represents a significant legal and operational risk when generative AI is used to review or negotiate business agreements.
Foundation models can generate plausible but inaccurate interpretations of contractual language. These errors may result in incorrect obligations, overlooked clauses, misrepresented liabilities, or inappropriate negotiation recommendations.
Such outcomes can expose a law firm and its clients to contractual disputes, financial losses, and potential professional liability.
AWS responsible AI principles emphasize accuracy, reliability, human oversight, and appropriate evaluation of AI-generated outputs, particularly in high-stakes applications.
Option B describes encryption, which protects sensitive information. Option C provides private connectivity through AWS PrivateLink. Option D improves inference performance and potentially reduces costs.
These are protective or optimization mechanisms rather than inherent legal risks.
Therefore, incorrect contract analysis is the correct answer.
NEW QUESTION # 342
A company wants to develop an educational game where users answer questions such as the following: "A jar contains six red, four green, and three yellow marbles. What is the probability of choosing a green marble from the jar?" Which solution meets these requirements with the LEAST operational overhead?
Answer: B
Explanation:
The problem involves a simple probability calculation that can be handled efficiently by straightforward mathematical rules and computations. Using machine learning techniques would introduce unnecessary complexity and operational overhead.
* Option C (Correct): "Use code that will calculate probability by using simple rules and computations": This is the correct answer because it directly solves the problem with minimal overhead, using basic probability rules.
* Option A: "Use supervised learning to create a regression model" is incorrect as it overcomplicates the solution for a simple probability problem.
* Option B: "Use reinforcement learning to train a model" is incorrect because reinforcement learning is not needed for a simple probability calculation.
* Option D: "Use unsupervised learning to create a model" is incorrect as unsupervised learning is not applicable to this task.
AWS AI Practitioner References:
* Choosing the Right Solution for AI Tasks: AWS recommends using the simplest and most efficient approach to solve a given problem, avoiding unnecessary machine learning techniques for straightforward tasks.
NEW QUESTION # 343
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?
Answer: B
Explanation:
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 # 344
A retail company wants to build an ML model to recommend products to customers. The company wants to build the model based on responsible practices. Which practice should the company apply when collecting data to decrease model bias?
Answer: D
Explanation:
The retail company wants to build an ML model for product recommendations using responsible practices to decrease model bias. Collecting balanced and diverse data ensures the model does not favor specific groups, reducing bias and promoting fairness, a key responsible AI practice.
Exact Extract from AWS AI Documents:
From the AWS AI Practitioner Learning Path:
"To reduce model bias, it is critical to collect balanced and diverse data that represents various demographics and user groups. This practice ensures fairness and prevents the model from disproportionately favoring certain populations." (Source: AWS AI Practitioner Learning Path, Module on Responsible AI) Detailed Explanation:
Option A: Use data from only customers who match the demography of the company's overall customer base.
Limiting data to a specific demographic may reinforce existing biases, failing to address underrepresented groups and increasing bias.
Option B: Collect data from customers who have a past purchase history.Focusing only on customers with purchase history may exclude new users, potentially introducing bias, and does not address diversity.
Option C: Ensure that the data is balanced and collected from a diverse group.This is the correct answer. A balanced and diverse dataset reduces bias by ensuring the model learns from a representative sample, aligning with responsible AI practices.
Option D: Ensure that the data is from a publicly available dataset.Public datasets may not be diverse or representative of the company's customer base and could introduce unrelated biases, failing to address fairness.
References:
AWS AI Practitioner Learning Path: Module on Responsible AI
Amazon SageMaker Developer Guide: Bias and Fairness in ML (https://docs.aws.amazon.com/sagemaker
/latest/dg/clarify-bias.html)
AWS Documentation: Responsible AI Practices (https://aws.amazon.com/machine-learning/responsible-ai/)
NEW QUESTION # 345
A company wants to build a lead prioritization application for its employees to contact potential customers.
The application must give employees the ability to view and adjust the weights assigned to different variables in the model based on domain knowledge and expertise.
Which ML model type meets these requirements?
Answer: C
Explanation:
The company needs an ML model for a lead prioritization application where employees can view and adjust the weights assigned to different variables based on domain knowledge. Logistic regression is a linear model that assigns interpretable weights to input features, making it easy for users to understand and modify these weights. This interpretability and adjustability make it suitable for the requirements.
Exact Extract from AWS AI Documents:
From the AWS AI Practitioner Learning Path:
"Logistic regression is a supervised learning algorithm used for classification tasks. It is highly interpretable, as it assigns weights to each feature, allowing users to understand and adjust the importance of different variables based on domain expertise." (Source: AWS AI Practitioner Learning Path, Module on Machine Learning Algorithms) Detailed Explanation:
* Option A: Logistic regression modelThis is the correct answer. Logistic regression provides interpretable coefficients (weights) for each feature, enabling employees to view and adjust them based on domain knowledge, meeting the application's requirements.
* Option B: Deep learning model built on principal componentsDeep learning models, even when using principal components, are complex and lack interpretability. The weights in such models are not easily adjustable by users, making this option unsuitable.
* Option C: K-nearest neighbors (k-NN) modelk-NN is a non-parametric model that does not assign explicit weights to features. It relieson distance metrics, which are not easily adjustable based on domain knowledge, so it does not meet the requirements.
* Option D: Neural networkNeural networks are highly complex and lack interpretability, as their weights are not directly tied to input features in a human-understandable way. Adjusting weights based on domain knowledge is impractical, making this option incorrect.
References:
AWS AI Practitioner Learning Path: Module on Machine Learning Algorithms Amazon SageMaker Developer Guide: Logistic Regression (https://docs.aws.amazon.com/sagemaker/latest/dg
/algos.html)
AWS Documentation: Interpretable Machine Learning Models (https://aws.amazon.com/machine-learning/)
NEW QUESTION # 346
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