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A company has installed a security camera. The company uses an ML model to evaluate the security camera footage for potential thefts. The company has discovered that the model disproportionately flags people who are members of a specific ethnic group. Which type of bias is affecting the model output?
A. Measurement bias
B. Sampling bias
C. Observer bias
D. Confirmation bias
Explanation:
Sampling bias is the correct type of bias affecting the model output when it disproportionately flags people from a specific ethnic group.
Sampling Bias:
Occurs when the training data is not representative of the broader population, leading to skewed model outputs.
In this case, if the model disproportionately flags people from a specific ethnic group, it likely indicates that the training data was not adequately balanced or representative.
Why Option B is Correct:
Reflects Data Imbalance:A biased sample in the training data could result in unfair outcomes, such as disproportionately flagging a particular group.
Common Issue in ML Models:Sampling bias is a known problem that can lead to unfair or inaccurate model predictions.
Why Other Options are Incorrect:
A. Measurement bias:Involves errors in data collection or measurement, not sampling.
C. Observer bias:Refers to bias introduced by researchers or data collectors, not the model's output.
D. Confirmation bias:Involves favoring information that confirms existing beliefs, not relevant to model output bias.
Which option is a benefit of ongoing pre-training when fine-tuning a foundation model (FM)?
A. Helps decrease the model's complexity
B. Improves model performance over time
C. Decreases the training time requirement
D. Optimizes model inference time
Explanation:
Ongoing pre-training when fine-tuning a foundation model (FM) improves model performance over time by continuously learning from new data.
Ongoing Pre-Training:
Involves continuously training a model with new data to adapt to changing patterns, enhance generalization, and improve performance on specific tasks.
Helps the model stay updated with the latest data trends and minimize drift over time.
Why Option B is Correct:
Performance Enhancement:Continuously updating the model with new data improves its accuracy and relevance.
Adaptability:Ensures the model adapts to new data distributions or domain-specific nuances.
Why Other Options are Incorrect:
A. Decrease model complexity:Ongoing pre-training typically enhances complexity by learning new patterns, not reducing it.
C. Decreases training time requirement:Ongoing pre-training may increase the time needed for training.
D. Optimizes inference time:Does not directly affect inference time; rather, it affects model performance.
A company wants to use AI to protect its application from threats. The AI solution needs to check if an IP address is from a suspicious source.
Which solution meets these requirements?
A. Build a speech recognition system.
B. Create a natural language processing (NLP) named entity recognition system.
C. Develop an anomaly detection system.
D. Create a fraud forecasting system.
A company needs to choose a model from Amazon Bedrock to use internally. The company must identify a model that generates responses in a style that the company's employees prefer.
What should the company do to meet these requirements?
A. Evaluate the models by using built-in prompt datasets.
B. Evaluate the models by using a human workforce and custom prompt datasets.
C. Use public model leaderboards to identify the model.
D. Use the model InvocationLatency runtime metrics in Amazon CloudWatch when trying models.
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?
A. Confusion matrix
B. Correlation matrix
C. R2 score
D. Mean squared error (MSE)
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