AIF-C01 Practice Test

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.

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.

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.

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.

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.