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NEW QUESTION # 32
A hospital developed an AI system to provide personalized treatment recommendations for patients. The AI system must provide the rationale behind the recommendations and make the insights accessible to doctors and patients.
Which human-centered design principle does this scenario present?
Answer: A
NEW QUESTION # 33
A company wants to display the total sales for its top-selling products across various retail locations in the past 12 months.
Which AWS solution should the company use to automate the generation of graphs?
Answer: C
NEW QUESTION # 34
An AI practitioner performed continued pre-training on a foundation model (FM). After model deployment, the AI practitioner discovered that the model was exposing sensitive company information that was inadvertently included in the training data.
Which security risk does this scenario represent?
Answer: C
Explanation:
The verified answer is B. Data leakage. The model exposes sensitive company information because that information was inadvertently included in training data. AWS guidance warns that using sensitive data to train models can cause unintended disclosure of data from model output, including data leakage during inference. This is exactly the scenario: sensitive information entered the training process and later appeared in model output after deployment.
AWS security reference guidance for generative AI model customization identifies sensitive information disclosure as a threat where a model trained on datasets containing PII leaks sensitive information during inference. Although the question says company information rather than PII specifically, the same security principle applies: protected data included in training or continued pre-training can be memorized or reproduced by the model later.
Jailbreaking is incorrect because jailbreaking refers to attempts to bypass model safety controls through adversarial prompts. The problem here is not that a user bypassed safety controls; the sensitive information was already embedded through training data.
Contextual grounding is incorrect because grounding is a technique for improving answer accuracy by connecting model responses to trusted context, such as RAG sources. It is not a security risk.
Prompt injection is incorrect because prompt injection involves malicious input that attempts to override or manipulate model behavior at inference time. The question does not describe a malicious prompt. It describes accidental exposure from training data.
Therefore, the correct security risk is data leakage.
NEW QUESTION # 35
A company is building a chatbot to improve user experience. The company is using a large language model (LLM) from Amazon Bedrock for intent detection. The company wants to use few-shot learning to improve intent detection accuracy.
Which additional data does the company need to meet these requirements?
Answer: B
Explanation:
Few-shot learning involves providing a model with a few examples (shots) to learn from. For improving intent detection accuracy in a chatbot using a large language model (LLM), the data should consist of pairs of user messages and their corresponding correct intents.
* Few-shot Learning for Intent Detection:
* Few-shot learning aims to enable the model to learn from a small number of examples. For intent detection, the model needs to understand the relationship between user messages and the intended action or meaning.
* Providing examples of user messages and the correct user intents allows the model to learn patterns in the phrasing or language that corresponds to each intent.
* Why Option C is Correct:
* User Messages and Intents: These examples directly teach the model how to map a user's input to the appropriate intent, which is the goal of intent detection in chatbots.
* Improves Accuracy: By using few-shot learning with these examples, the model can generalize better from limited data, improving intent detection.
* Why Other Options are Incorrect:
* A. Pairs of chatbot responses and correct user intents: Incorrect because it does not focus on user input but rather on outputs.
* B. Pairs of user messages and correct chatbot responses: This would be useful for response generation, not intent detection.
* D. Pairs of user intents and correct chatbot responses: Again, this is not aligned with detecting intents but with generating responses.
NEW QUESTION # 36
An AI practitioner has built a deep learning model to classify the types of materials in images. The AI practitioner now wants to measure the model performance.
Which metric will help the AI practitioner evaluate the performance of the model?
Answer: B
Explanation:
A confusion matrix is the correct metric for evaluating the performance of a classification model, such as the deep learning model built to classify types of materials in images.
* Confusion Matrix:
* It is a table used to describe the performance of a classification model by comparing the actual and predicted classifications.
* Provides detailed insights into the model's performance, including true positives, true negatives, false positives, and false negatives.
* Why Option A is Correct:
* Performance Measurement: Helps measure various performance metrics like accuracy, precision, recall, and F1-score, which are critical for evaluating a classification model.
* Comprehensive Evaluation: Allows for a thorough analysis of where the model is making errors and the types of errors being made.
* Why Other Options are Incorrect:
* B. Correlation matrix: Used to identify relationships between variables, not for evaluating classification performance.
* C. R2 score: Used for regression models, not classification.
* D. Mean squared error (MSE): Also a metric for regression, measuring the average of the squares of the errors.
NEW QUESTION # 37
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