Correct Answer: A

Creating a prompt template that teaches the LLM to detect attack patterns is the most effective way to reduce the risk of the model being manipulated through prompt engineering.

Correct Answer: D

AWS Glue is the correct service for transforming petabytes of unstructured data into a structured format suitable for machine learning tasks.

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.

AWS AI Practitioner

References:

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

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.

Correct Answer: B

To determine which model generates responses in a style that the company's employees prefer, the best approach is to use a human workforce to evaluate the models with custom prompt datasets. This method allows for subjective

evaluation based on the specific stylistic preferences of the company's employees, which cannot be effectively assessed through automated methods or pre-built datasets. Option B (Correct): "Evaluate the models by using a human workforce

and custom prompt datasets":This is the correct answer as it directly involves human judgment to evaluate the style and quality of the responses, aligning with employee preferences.

Option A:"Evaluate the models by using built-in prompt datasets" is incorrect because built-in datasets may not capture the company's specific stylistic requirements.

Option C:"Use public model leaderboards to identify the model" is incorrect as leaderboards typically measure model performance on standard benchmarks, not on stylistic preferences.

Option D:"Use the model InvocationLatency runtime metrics in Amazon CloudWatch" is incorrect because latency metrics do not provide any information about the style of the model's responses.

AWS AI Practitioner

References:

Model Evaluation Techniques on AWS:AWS suggests using human evaluators to assess qualitative aspects of model outputs, such as style and tone, to ensure alignment with organizational preferences

Correct Answer: B

Sampling bias is the correct type of bias affecting the model output when it disproportionately flags people from a specific ethnic group.

Correct Answer: B

Domain adaptation fine-tuning involves training a foundation model (FM) further using a specific dataset that includes domain-specific terminology and content, such as scientific terms in research papers. This process allows the model to better understand and handle complex terminology, improving its performance on specialized tasks. Option B (Correct): "Use domain adaptation fine-tuning to adapt the FM to complex scientific terms":This is the correct answer because fine-tuning the model on domain-specific data helps it learn and adapt to the specific language and terms used in the research papers, resulting in better performance. Option A:"Use few-shot prompting to define how the FM can answer the questions" is incorrect because while few-shot prompting can help in certain scenarios, it is less effective than fine-tuning for handling complex domain-specific terms. Option C:"Change the FM inference parameters" is incorrect because adjusting inference parameters will not resolve the issue of the model's lack of understanding of complex scientific terminology. Option D:"Clean the research paper data to remove complex scientific terms" is incorrect because removing the complex terms would result in the loss of important information and context, which is not a viable solution. AWS AI Practitioner References: Domain Adaptation in Amazon Bedrock:AWS recommends fine-tuning models with domain-specific data to improve their performance on specialized tasks involving unique terminology.

Correct Answer: A

Batch transform in Amazon SageMaker is designed for offline processing of large datasets. It is ideal for scenarios where immediate predictions are not required, and the inference can be done on large datasets that are multiple gigabytes in

size. This method processes data in batches, making it suitable for analyzing archived data without the need for real- time access to predictions.

Option A (Correct): "Batch transform":This is the correct answer because batch transform is optimized for handling large datasets and is suitable when immediate access to predictions is not required.

Option B:"Real-time inference" is incorrect because it is used for low-latency, real- time prediction needs, which is not required in this case. Option C:"Serverless inference" is incorrect because it is designed for small-scale, intermittent

inference requests, not for large batch processing. Option D:"Asynchronous inference" is incorrect because it is used when immediate predictions are required, but with high throughput, whereas batch transform is more suitable for very large

datasets.

AWS AI Practitioner

References:

Batch Transform on AWS SageMaker:AWS recommends using batch transform for large datasets when real-time processing is not needed, ensuring cost- effectiveness and scalability.

Correct Answer: A

Tokens in generative AI models are the smallest units that the model processes, typically representing words, subwords, or characters. They are essential for the model to understand and generate language, breaking down text into

manageable parts for processing.

Option A (Correct): "Tokens are the basic units of input and output that a generative AI model operates on, representing words, subwords, or other linguistic units":This is the correct definition of tokens in the context of generative AI models.

Option B:"Mathematical representations of words" describes embeddings, not tokens.

Option C:"Pre-trained weights of a model" refers to the parameters of a model, not tokens.

Option D:"Prompts or instructions given to a model" refers to the queries or commands provided to a model, not tokens.

AWS AI Practitioner

References:

Understanding Tokens in NLP:AWS provides detailed explanations of how tokens are used in natural language processing tasks by AI models, such as in Amazon Comprehend and other AWS AI services.

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.

AWS AI Practitioner

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.