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Amazon Web Services AIF-C01 Exam Sample Questions

Question # 1

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

Answer

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.

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Question # 2

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

Answer

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.

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Question # 3

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

Answer

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.

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Question # 4

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

Answer

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.

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Question # 5

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.

Answer

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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