Question # 1
A company wants to create a chatbot by using a foundation model (FM) on Amazon Bedrock. The FM needs to access encrypted data that is stored in an Amazon S3 bucket. The data is encrypted with Amazon S3 managed keys (SSE-S3). The FM encounters a failure when attempting to access the S3 bucket data. Which solution will meet these requirements?
| A. Ensure that the role that Amazon Bedrock assumes has permission to decrypt data with the correct encryption key.
| B. Set the access permissions for the S3 buckets to allow public access to enable access over the internet.
| C. Use prompt engineering techniques to tell the model to look for information in Amazon S3.
| D. Ensure that the S3 data does not contain sensitive information.
|
A. Ensure that the role that Amazon Bedrock assumes has permission to decrypt data with the correct encryption key.
Explanation:
Amazon Bedrock needs the appropriate IAM role with permission to access and decrypt data stored in Amazon S3. If the data is encrypted with Amazon S3 managed keys (SSE-S3), the role that Amazon Bedrock assumes must have the required permissions to access and decrypt the encrypted data.
Option A (Correct): "Ensure that the role that Amazon Bedrock assumes has permission to decrypt data with the correct encryption key":This is the correct solution as it ensures that the AI model can access the encrypted data securely without changing the encryption settings or compromising data security.
Option B:"Set the access permissions for the S3 buckets to allow public access" is incorrect because it violates security best practices by exposing sensitive data to the public.
Option C:"Use prompt engineering techniques to tell the model to look for information in Amazon S3" is incorrect as it does not address the encryption and permission issue.
Option D:"Ensure that the S3 data does not contain sensitive information" is incorrect because it does not solve the access problem related to encryption.
AWS AI Practitioner References:
Managing Access to Encrypted Data in AWS:AWS recommends using proper IAM roles and policies to control access to encrypted data stored in S3.
Question # 2
A social media company wants to use a large language model (LLM) for content moderation. The company wants to evaluate the LLM outputs for bias and potential discrimination against specific groups or individuals. Which data source should the company use to evaluate the LLM outputs with the LEAST administrative effort?
| A. User-generated content
| B. Moderation logs
| C. Content moderation guidelines
| D. Benchmark datasets
|
D. Benchmark datasets
Explanation:
Benchmark datasets are pre-validated datasets specifically designed to evaluate machine learning models for bias, fairness, and potential discrimination. These datasets are the most efficient tool for assessing an LLM’s performance against known standards with minimal administrative effort.
Option D (Correct): "Benchmark datasets":This is the correct answer because using standardized benchmark datasets allows the company to evaluate model outputs for bias with minimal administrative overhead.
Option A:"User-generated content" is incorrect because it is unstructured and would require significant effort to analyze for bias.
Option B:"Moderation logs" is incorrect because they represent historical data and do not provide a standardized basis for evaluating bias.
Option C:"Content moderation guidelines" is incorrect because they provide qualitative criteria rather than a quantitative basis for evaluation.
AWS AI Practitioner References:
Evaluating AI Models for Bias on AWS:AWS supports using benchmark datasets to assess model fairness and detect potential bias efficiently.
Question # 3
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
|
B. Sampling 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.
Question # 4
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
|
B. Improves model performance over 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.
Question # 5
A company wants to use a large language model (LLM) on Amazon Bedrock for sentiment analysis. The company needs the LLM to produce more consistent responses to the same input prompt. Which adjustment to an inference parameter should the company make to meet these requirements?
| A. Decrease the temperature value
| B. Increase the temperature value
| C. Decrease the length of output tokens
| D. Increase the maximum generation length
|
A. Decrease the temperature value
Explanation:
The temperature parameter in a large language model (LLM) controls the randomness of the model's output. A lower temperature value makes the output more deterministic and consistent, meaning that the model is less likely to produce different results for the same input prompt.
Option A (Correct): "Decrease the temperature value":This is the correct answer because lowering the temperature reduces the randomness of the responses, leading to more consistent outputs for the same input.
Option B:
"Increase the temperature value" is incorrect because it would make the output more random and less consistent.
Option C:
"Decrease the length of output tokens" is incorrect as it does not directly affect the consistency of the responses.
Option D:"Increase the maximum generation length" is incorrect because this adjustment affects the output length, not the consistency of the model’s responses.
AWS AI Practitioner References:
Understanding Temperature in Generative AI Models:AWS documentation explains that adjusting the temperature parameter affects the model’s output randomness, with lower values providing more consistent outputs.
Question # 6
An AI practitioner is building a model to generate images of humans in various professions. The AI practitioner discovered that the input data is biased and that specific attributes affect the image generation and create bias in the model. Which technique will solve the problem?
| A. Data augmentation for imbalanced classes
| B. Model monitoring for class distribution
| C. Retrieval Augmented Generation (RAG)
| D. Watermark detection for images
|
A. Data augmentation for imbalanced classes
Explanation:
Data augmentation for imbalanced classes is the correct technique to address bias in input data affecting image generation.
Data Augmentation for Imbalanced Classes:
Involves generating new data samples by modifying existing ones, such as flipping, rotating, or cropping images, to balance the representation of different classes.
Helps mitigate bias by ensuring that the training data is more representative of diverse characteristics and scenarios.
Why Option A is Correct:
Balances Data Distribution:Addresses class imbalance by augmenting underrepresented classes, which reduces bias in the model.
Improves Model Fairness:Ensures that the model is exposed to a more diverse set of training examples, promoting fairness in image generation.
Why Other Options are Incorrect:
B. Model monitoring for class distribution:Helps identify bias but does not actively correct it.
C. Retrieval Augmented Generation (RAG):Involves combining retrieval and generation but is unrelated to mitigating bias in image generation.
D. Watermark detection for images:Detects watermarks in images, not a technique for addressing bias.
Question # 7
A company is training a foundation model (FM). The company wants to increase the accuracy of the model up to a specific acceptance level. Which solution will meet these requirements?
| A. Decrease the batch size.
| B. Increase the epochs.
| C. Decrease the epochs.
| D. Increase the temperature parameter.
|
B. Increase the epochs.
Explanation:
Increasing the number of epochs during model training allows the model to learn from the data over more iterations, potentially improving its accuracy up to a certain point. This is a common practice when attempting to reach a specific level of accuracy.
Option B (Correct): "Increase the epochs":This is the correct answer because increasing epochs allows the model to learn more from the data, which can lead to higher accuracy.
Option A:"Decrease the batch size" is incorrect as it mainly affects training speed and may lead to overfitting but does not directly relate to achieving a specific accuracy level.
Option C:"Decrease the epochs" is incorrect as it would reduce the training time, possibly preventing the model from reaching the desired accuracy.
Option D:"Increase the temperature parameter" is incorrect because temperature affects the randomness of predictions, not model accuracy.
AWS AI Practitioner References:
Model Training Best Practices on AWS:AWS suggests adjusting training parameters, like the number of epochs, to improve model performance.
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