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NEW QUESTION # 198
A company trained an ML model on Amazon SageMaker to predict customer credit risk. The model shows 90% recall on training data and 40% recall on unseen testing data.
Which conclusion can the company draw from these results?
Answer: D
Explanation:
The ML model shows 90% recall on training data but only 40% recall on unseen testing data, indicating a significant performance drop. This discrepancy suggests the model has learned the training data too well, including noise and specific patterns that do not generalize to new data, which is a classic sign of overfitting.
Exact Extract from AWS AI Documents:
From the Amazon SageMaker Developer Guide:
"Overfitting occurs when a model performs well on training data but poorly on unseen test data, as it has learned patterns specific to the training set, including noise, that do not generalize. A large gap between training and testing performance metrics, such as recall, is a common indicator of overfitting." (Source: Amazon SageMaker Developer Guide, Model Evaluation and Overfitting) Detailed Option A: The model is overfitting on the training data.This is the correct answer. The significant drop in recall from 90% (training) to 40% (testing) indicates the model is overfitting, as it performs well on training data but fails to generalize to unseen data.
Option B: The model is underfitting on the training data.Underfitting occurs when the model performs poorly on both training and testing data due to insufficient learning. With 90% recall on training data, the model is not underfitting.
Option C: The model has insufficient training data.Insufficient training data could lead to poor performance, but the high recall on trainingdata (90%) suggests the model has learned the training data well, pointing to overfitting rather than a lack of data.
Option D: The model has insufficient testing data.Insufficient testing data might lead to unreliable test metrics, but it does not explain the large performance gap between training and testing, which is more indicative of overfitting.
Reference:
Amazon SageMaker Developer Guide: Model Evaluation and Overfitting (https://docs.aws.amazon.com/sagemaker/latest/dg/model-evaluation.html) AWS AI Practitioner Learning Path: Module on Model Performance and Evaluation AWS Documentation: Understanding Overfitting and Underfitting (https://aws.amazon.com/machine-learning/)
NEW QUESTION # 199
A company wants to detect unauthorized API actions for foundation models (FMs) in Amazon Bedrock.
Which solution meets these requirements?
Answer: B
Explanation:
AWS CloudTrail provides auditing and monitoring capabilities by recording API activity across AWS services, including Amazon Bedrock.
CloudTrail events can include details such as the requesting identity, API operation, request timestamp, source IP address, and relevant error information.
These records help security teams investigate unauthorized or suspicious attempts to access Amazon Bedrock resources.
CloudTrail captures Bedrock management events, while certain data events require additional logging configuration. Organizations can analyze these events and integrate monitoring workflows with other AWS security services.
AWS Trusted Advisor provides infrastructure optimization and security recommendations but does not directly audit Bedrock model inputs and outputs.
Amazon QuickSight provides business intelligence capabilities, while Amazon Personalize generates personalized recommendations.
Therefore, AWS CloudTrail is the appropriate service for monitoring API activity and investigating unauthorized access attempts involving foundation models.
NEW QUESTION # 200
A company is using Amazon Bedrock to build a customer-facing AI assistant. The company wants to filter out harmful and offensive content. The company also wants to block customer personally identifiable information (PII) from appearing in the AI assistant ' s responses.
Which AWS solution will meet these requirements with the LEAST operational overhead?
Answer: D
Explanation:
Amazon Bedrock Guardrails provides managed safety and privacy controls for generative AI applications.
The service supports content filters that identify potentially harmful or offensive model inputs and outputs.
These filters can detect categories such as hate, insults, violence, and misconduct.
Amazon Bedrock Guardrails also provides sensitive information filters that identify personally identifiable information, including names, addresses, telephone numbers, and other supported identifiers.
Organizations can configure these filters to block content or mask detected sensitive information.
Amazon Macie identifies sensitive information primarily within Amazon S3. Amazon Comprehend provides natural language processing capabilities, while Amazon Inspector identifies software vulnerabilities.
Neither alternative provides the same integrated generative AI safety controls.
Therefore, Amazon Bedrock Guardrails satisfies both requirements while minimizing operational overhead.
NEW QUESTION # 201
A company has multiple datasets that contain historical data. The company wants to use ML technologies to process each dataset.
Select the correct ML technology from the following list for each dataset. Select each ML technology one time or not at all. (Select THREE.) Computer vision Natural language processing (NLP) Reinforcement learning Time series forecasting
Answer:
Explanation:
Dataset 1: A dataset that contains text-based customer reviews # Natural language processing (NLP) NLP is designed for analyzing text (sentiment analysis, text classification, etc.).
Dataset 2: A dataset that contains images of animals labeled with their species names # Computer vision Computer vision models classify or detect objects in images.
Dataset 3: A dataset that contains daily sales volumes for products # Time series forecasting Time series forecasting predicts future values based on historical sequential data (like sales, demand, stock prices).
NEW QUESTION # 202
HOTSPOT
A company is training its employees on how to structure prompts for foundation models.
Select the correct prompt engineering technique from the following list for each prompt template. Each prompt engineering technique should be selected onetime. (SelectTHREE.)
* Chain-of-thought reasoning
* Few-shot learning
* Zero-shot learning
Answer:
Explanation:
Explanation:
Zero-shot learning is when the model is asked to perform a task without being given any examples. The prompt simply describes the task and relies on the model's pre-trained knowledge.
(Reference: Amazon Bedrock Prompt Engineering Guide)
Few-shot learning provides a few examples (shots) in the prompt to show the model how to solve the task before asking it to complete a similar task.
(Reference: Amazon Bedrock Prompt Engineering Guide)
Chain-of-thought reasoning encourages the model to reason step by step and explain its thinking for more complex or logical tasks.
(Reference: AWS Chain-of-Thought Prompting)
NEW QUESTION # 203
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