AWS Certified AI Practitioner Exam Practice Test

Answer: B
ExplanationThe goal is to prevent a fine-tuned large language model (LLM) on Amazon Bedrock from revealing private customer data. Let’s analyze the options:A. Amazon Bedrock Guardrails: Guardrails in Amazon Bedrock allow users to define policies to filter harmful or sensitive content in model inputs and outputs. While useful for real-time content moderation, they do not address the risk of private data being embedded in the model during fine-tuning, as the model could still memorize sensitive information.B. Remove personally identifiable information (PII) from the customer data before fine-tuning the LLM: Removing PII (e.g., names, addresses, account numbers) from the training dataset ensures that the model does not learn or memorize sensitive customer data, reducing the risk of data leakage. This is a proactive and effective approach to data privacy during model training.C. Increase the Top-K parameter of the LLM: The Top-K parameter controls the randomness of the model’s output by limiting the number of tokens considered during generation. Adjusting this parameter affects output diversity but does not address the privacy of customer data embedded in the model.D. Store customer data in Amazon S3. Encrypt the data before fine-tuning the LLM: Encrypting data in Amazon S3 protects data at rest and in transit, but during fine-tuning, the data is decrypted and used to train the model. If PII is present, the model could still learn and potentially expose it, so encryption alone does not solve the problem.Exact Extract Reference: AWS emphasizes data privacy in AI/ML workflows, stating, “To protect sensitive data, you can preprocess datasets to remove personally identifiable information (PII) before using them for model training. This reduces the risk of models inadvertently learning or exposing sensitive information.” (Source: AWS Best Practices for Responsible AI, https://aws.amazon.com/machine-learning/responsible-ai/). Additionally, the Amazon Bedrock documentation notes that users are responsible for ensuring compliance with data privacy regulations during fine-tuning (https://docs.aws.amazon.com/bedrock/latest/userguide/model-customization.html).Removing PII before fine-tuning is the most direct and effective way to prevent the model from revealing private customer data, making B the correct answer.References:AWS Bedrock Documentation: Model Customization (https://docs.aws.amazon.com/bedrock/latest/userguide/model-customization.html)AWS Responsible AI Best Practices (https://aws.amazon.com/machine-learning/responsible-ai/)AWS AI Practitioner Study Guide (emphasis on data privacy in LLM fine-tuning

Answer: C
ExplanationSentiment analysis is the task of determining the emotional tone or intent behind a body of text (positive, negative, neutral).This falls under Natural Language Processing (NLP) because it deals with understanding and processing human language.Computer vision relates to images, robotics to autonomous machines, and time series forecasting to predicting values from sequential data.# Reference:AWS ML Glossary – NLP

Answer: A
ExplanationThe correct answer is A because adversarial prompting is a defensive technique used to identify and protect against prompt injection attacks in large language models (LLMs). In adversarial prompting, developers intentionally test the model with manipulated or malicious prompts to evaluate how it behaves under attack and to harden the system by refining prompts, filters, and validation logic.From AWS documentation:"Adversarial prompting is used to evaluate and defend generative AI models against harmful or manipulative inputs (prompt injections). By testing with adversarial examples, developers can identify vulnerabilities and apply safeguards such as Guardrails or context filtering to prevent model misuse."Prompt injection occurs when an attacker tries to override system or developer instructions within a prompt, leading the model to disclose restricted information or behave undesirably. Adversarial prompting helps uncover and mitigate these risks before deployment.Explanation of other options:B. Zero-shot prompting provides no examples and does not protect against injection attacks.C. Least-to-most prompting is a reasoning technique used to break down complex problems step-by-step, not a security measure.D. Chain-of-thought prompting encourages detailed reasoning by the model but can actually increase exposure to prompt injection if not properly constrained.Referenced AWS AI/ML Documents and Study Guides:AWS Responsible AI Practices – Prompt Injection and Safety TestingAmazon Bedrock Developer Guide – Secure Prompt Design and EvaluationAWS Generative AI Security Whitepaper – Adversarial Testing and Guardrails

Answer: D
ExplanationAmazon SageMaker Canvas is a visual, no-code machine learning interface that allows users to build machine learning models without having any coding experience or knowledge of machine learning algorithms. It enables users to analyze internal and external data, and make predictions using a guided interface.Option D (Correct): "Import the data into Amazon SageMaker Canvas. Build ML models and demand forecast predictions by selecting the values in the data from SageMaker Canvas": This is the correct answer because SageMaker Canvas is designed for users without coding experience, providing a visual interface to build predictive models with ease.Option A: "Store the data in Amazon S3 and use SageMaker built-in algorithms" is incorrect because it requires coding knowledge to interact with SageMaker's built-in algorithms.Option B: "Import the data into Amazon SageMaker Data Wrangler" is incorrect. Data Wrangler is primarily for data preparation and not directly focused on creating ML models without coding.Option C: "Use Amazon Personalize Trending-Now recipe" is incorrect as Amazon Personalize is for building recommendation systems, not for general demand forecasting.AWS AI Practitioner References:Amazon SageMaker Canvas Overview: AWS documentation emphasizes Canvas as a no-code solution for building machine learning models, suitable for business analysts and users with no coding experience.

Answer: C
ExplanationThe correct answer is C, because Amazon Nova Micro is a smaller, lower-cost foundation model that is textonly, while Nova Lite is a more capable multimodal model that supports images, videos, and text. According to AWS Bedrock documentation, the Nova model family includes variants that differ in capability and cost. Nova Micro is optimized for lightweight text-based tasks, including summarization, question answering, and basic reasoning. This makes it cheaper to operate and well-suited for cost-sensitive workloads. Nova Lite, on the other hand, is a multimodal FM that can analyze documents, screenshots, photographs, charts, and videos, making it ideal for media companies requiring cross-format understanding. AWS clarifies that both Micro and Lite use transformer-based architectures, and run on managed infrastructure that abstracts hardware considerations. Therefore, the main differentiator is capability—and Nova Micro being text-only is the more cost-effective option. Nova Lite is appropriate only when image or video analysis is required.Referenced AWS Documentation:Amazon Bedrock – Nova Model Family OverviewAWS Generative AI Model Selection Guide