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NEW QUESTION # 122
An AI practitioner is building an ML model. The AI practitioner wants to provide model transparency and explainability to stakeholders.
Which solution will meet these requirements?
Answer: D
Explanation:
The verified answer is A. Present the model Shapley values. AWS documentation states that SageMaker Clarify provides feature attributions based on Shapley values. These values can determine the contribution each feature made to model predictions and can be provided for individual predictions or at a global level for the model as a whole. That is exactly what stakeholders need when they ask for transparency and explainability: they need to understand which features influenced the model's decisions and how strongly those features contributed.
AWS also explains that SageMaker Clarify helps explain how ML models make predictions and that transparency is critical for consumers and regulators. Clarify's explainability uses a model-agnostic feature attribution approach and includes a scalable implementation of SHAP. These explanations answer practical stakeholder questions such as why the model made a prediction, how it makes predictions, and which features have the largest influence on model behavior.
Option B is incorrect because accuracy measures predictive correctness, not explainability. A model can be highly accurate while still being opaque. Accuracy alone does not show why a specific prediction was made.
Option C is incorrect because a confusion matrix summarizes classification performance by showing true positives, false positives, true negatives, and false negatives. It is useful for evaluation, but it does not explain individual feature influence or model reasoning.
Option D is incorrect because a secure inference endpoint protects access to the deployed model. Security is important, but it does not provide transparency or explainability.
Therefore, presenting Shapley values is the correct solution because it directly explains feature contributions to model predictions.
NEW QUESTION # 123
HOTSPOT
Select the correct AI term from the following list for each statement. Each AI term should be selected one time. (Select THREE.)
* AI
* Deep learning
* ML
Answer:
Explanation:
NEW QUESTION # 124
A company wants to develop a solution that uses generative AI to create content for product advertisements, Including sample images and slogans.
Select the correct model type from the following list for each action. Each model type should be selected one time. (Select THREE.)
* Diffusion model
* Object detection model
* Transformer-based model
Answer:
Explanation:
Explanation:
Diffusion models are state-of-the-art generative models for creating high-quality, realistic images from textual prompts or other forms of conditioning. These are the foundational technology behind tools like Amazon Bedrock Titan Image Generator and other generative image models.
Reference: AWS Generative AI Overview, Diffusion Models Explained - AWS Blog Transformer-based models (such as GPT or Amazon Titan Text) are designed for generating and understanding natural language. These models can generate coherent, contextually relevant slogans based on product information.
Reference: AWS Generative AI on Bedrock, Transformers Explained - AWS
Object detection models are designed to identify and locate objects within images, which makes them suitable for verifying that specific brand elements (like logos or products) are correctly positioned in the generated content.
Reference: AWS Rekognition Object Detection, Object Detection Overview - AWS
NEW QUESTION # 125
Which statement presents an advantage of using Retrieval Augmented Generation (RAG) for natural language processing (NLP) tasks?
Answer: D
Explanation:
* Retrieval-Augmented Generation (RAG) integrates external knowledge sources (databases, vector stores, document repositories) with LLMs, enabling them to generate contextually accurate and up-to- date responses without retraining.
* B is incorrect: RAG does not speed up training; it improves inference results.
* C is incorrect: speech recognition is not an RAG use case.
* D is incorrect: computer vision augmentation is unrelated to RAG.
# Reference:
AWS Documentation - Knowledge Bases for RAG in Amazon Bedrock
NEW QUESTION # 126
A company is training ML models on datasets. The datasets contain some classes that have more examples than other classes. The company wants to measure how well the model balances detecting and labeling the classes.
Which metric should the company use?
Answer: C
Explanation:
The correct answer is D - F1 score, which is the harmonic mean of precision and recall. AWS documentation states that the F1 score is the preferred metric when evaluating ML models trained on imbalanced datasets, where one class appears significantly more often than others. Accuracy becomes misleading in these situations because a model can achieve high accuracy simply by predicting the majority class. Precision alone measures correctness of positive predictions, while recall measures completeness-neither fully captures class balancing performance. The F1 score provides a combined measurement that reflects how well the model identifies minority classes while avoiding excessive false positives. AWS's ML Specialty Guide recommends using F1 for classification tasks where "class distribution is uneven or where both false negatives and false positives matter." This metric provides a fairer assessment of overall model performance across all class categories.
Referenced AWS Documentation:
* AWS Certified Machine Learning Specialty Guide - Metrics for Imbalanced Classes
* Amazon SageMaker Developer Guide - Evaluating Classification Models
NEW QUESTION # 127
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