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Correct Answer: B

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.

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Model Training Best Practices on AWS:AWS suggests adjusting training parameters, like the number of epochs, to improve model performance.

Correct Answer: B

Partial dependence plots (PDPs) are visual tools used to show the relationship between a feature (or a set of features) in the data and the predicted outcome of a machine learning model. They are highly effective for providing transparency

and explainability of the model's behavior to stakeholders by illustrating how different input variables impact the model's predictions.

Option B (Correct): "Partial dependence plots (PDPs)":This is the correct answer because PDPs help to interpret how the model's predictions change with varying values of input features, providing stakeholders with a clearer understanding of

the model's decision-making process.

Option A:"Code for model training" is incorrect because providing the raw code for model training may not offer transparency or explainability to non-technical stakeholders.

Option C:"Sample data for training" is incorrect as sample data alone does not explain how the model works or its decision-making process. Option D:"Model convergence tables" is incorrect. While convergence tables can show the training

process, they do not provide insights into how input features affect the model's predictions.

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Explainability in AWS Machine Learning:AWS provides various tools for model explainability, such as Amazon SageMaker Clarify, which includes PDPs to help explain the impact of different features on the model's predictions.

Correct Answer: A

Amazon Q Developer is a tool designed to assist developers in increasing productivity by generating code snippets, managing reference tracking, and handling open-source license tracking. These features help developers by automating

parts of the software development process.

Option A (Correct): "Create software snippets, reference tracking, and open- source license tracking":This is the correct answer because these are key features that help developers streamline and automate tasks, thus improving productivity.

Option B:"Run an application without provisioning or managing servers" is incorrect as it refers to AWS Lambda or AWS Fargate, not Amazon Q Developer. Option C:"Enable voice commands for coding and providing natural language

search" is incorrect because this is not a function of Amazon Q Developer. Option D:"Convert audio files to text documents by using ML models" is incorrect as this refers to Amazon Transcribe, not Amazon Q Developer.

AWS AI Practitioner References:

Amazon Q Developer Features:AWS documentation outlines how Amazon Q Developer supports developers by offering features that reduce manual effort and improve efficiency.

Correct Answer: B

Accuracy is the most appropriate metric to measure the performance of an image classification model. It indicates the percentage of correctly classified images out of the total number of images. In the context of classifying plant diseases from

images, accuracy will help the company determine how well the model is performing by showing how many images were correctly classified.

Option B (Correct): "Accuracy":This is the correct answer because accuracy measures the proportion of correct predictions made by the model, which is suitable for evaluating the performance of a classification model. Option A:"R-squared

score" is incorrect as it is used for regression analysis, not classification tasks.

Option C:"Root mean squared error (RMSE)" is incorrect because it is also used for regression tasks to measure prediction errors, not for classification accuracy. Option D:"Learning rate" is incorrect as it is a hyperparameter for training, not a

performance metric.

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References:

Evaluating Machine Learning Models on AWS:AWS documentation emphasizes the use of appropriate metrics, like accuracy, for classification tasks.

Correct Answer: B

Adding a role description to the prompt context is a straightforward way to instruct the generative AI model to adjust its response style based on the user's age range. This method requires minimal implementation effort as it does not involve

additional training or complex logic.

Option B (Correct): "Add a role description to the prompt context that instructs the model of the age range that the response should target":This is the correct answer because it involves the least implementation effort while effectively guiding

the model to tailor responses according to the age range. Option A:"Fine-tune the model by using additional training data" is incorrect because it requires significant effort in gathering data and retraining the model. Option C:"Use chain-ofthought reasoning" is incorrect as it involves complex reasoning that may not directly address the need to adjust response style based on age.

Option D:"Summarize the response text depending on the age of the user" is incorrect because it involves additional processing steps after generating the initial response, increasing complexity.

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References:

Prompt Engineering Techniques on AWS:AWS recommends using prompt context effectively to guide generative models in providing tailored responses based on specific user attributes.