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SAMPLE QUESTIONS
Question # 1
A bank is fine-tuning a large language model (LLM) on Amazon Bedrock to assist customers with questions about their loans. The bank wants to ensure that the model does not reveal any private customer data.Which solution meets these requirements?
A. Use Amazon Bedrock Guardrails. B. Remove personally identifiable information (PII) from the customer data before fine-tuning the LLM. C. Increase the Top-K parameter of the LLM. D. Store customer data in Amazon S3. Encrypt the data before fine-tuning the LLM.
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
Question # 2
Sentiment analysis is a subset of which broader field of AI?
A. Computer vision B. Robotics C. Natural language processing (NLP) D. Time series forecasting
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
Question # 3
Which prompting technique can protect against prompt injection attacks?
A. Adversarial prompting B. Zero-shot prompting C. Least-to-most prompting D. Chain-of-thought prompting
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
Question # 4
A digital devices company wants to predict customer demand for memory hardware. The company does not have coding experience or knowledge of ML algorithms and needs to develop a data-driven predictive model. The company needs to perform analysis on internal data and external data.Which solution will meet these requirements?
A. Store the data in Amazon S3. Create ML models and demand forecast predictions by using Amazon
SageMaker built-in algorithms that use the data from Amazon S3. B. Import the data into Amazon SageMaker Data Wrangler. Create ML models and demand forecast
predictions by using SageMaker built-in algorithms. C. Import the data into Amazon SageMaker Data Wrangler. Build ML models and demand forecast
predictions by using an Amazon Personalize Trending-Now recipe.
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.
Question # 5
A company that streams media is selecting an Amazon Nova foundation model (FM) to process documents and images. The company is comparing Nova Micro and Nova Lite. The company wants to minimize costs.
A. Nova Micro uses transformer-based architectures. Nova Lite does not use transformer-based
architectures. B. Nova Micro supports only text data. Nova Lite is optimized for numerical data. C. Nova Micro supports only text. Nova Lite supports images, videos, and text. D. Nova Micro runs only on CPUs. Nova Lite runs only on GPUs.
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
Question # 6
A company is building an AI application to summarize books of varying lengths. During testing, the application fails to summarize some books. Why does the application fail to summarize some books?
A. The temperature is set too high. B. The selected model does not support fine-tuning. C. The Top P value is too high. D. The input tokens exceed the model's context size.
Answer: D ExplanationFoundation models have a context window (max tokens), which limits the size of the input text (prompt + instructions).If the input (e.g., a very long book) exceeds this limit, the model cannot process it, causing failure.Temperature (A) and Top P (C) control randomness, not input size.Fine-tuning (B) is irrelevant to input truncation failures.# Reference:AWS Documentation – Amazon Bedrock Model Parameters (context size limits
Question # 7
A company wants to identify harmful language in the comments section of social media posts by using an ML model. The company will not use labeled data to train the model. Which strategy should the company use to identify harmful language?
A. Use Amazon Rekognition moderation. B. Use Amazon Comprehend toxicity detection. C. Use Amazon SageMaker AI built-in algorithms to train the model. D. Use Amazon Polly to monitor comments.
Answer: B ExplanationAmazon Comprehend toxicity detection is a managed NLP service that can analyze text for harmful or toxic language using pre-trained models—no need for labeled data or custom training.B is correct: Comprehend’s toxicity detection API is designed for this use case, works out-of-the-box, and requires no data labeling or model training.A (Rekognition) is for image and video content moderation.C would require labeled data for training.D (Polly) is for text-to-speech, not content moderation.“Amazon Comprehend can detect toxicity in text with pre-trained models, requiring no labeled training data.”(Reference: Amazon Comprehend Toxicity Detection, AWS AI Practitioner Official Guide)
Question # 8
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 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.
Question # 9
A company that uses multiple ML models wants to identify changes in original model quality so that the company can resolve any issues.Which AWS service or feature meets these requirements?
A. Amazon SageMaker JumpStart B. Amazon SageMaker HyperPod C. Amazon SageMaker Data Wrangler D. Amazon SageMaker Model Monitor
Answer: D ExplanationAmazon SageMaker Model Monitor is specifically designed to automatically detect and alert on changes in model quality, such as data drift, prediction drift, or other anomalies in model performance once deployed.D is correct:"Amazon SageMaker Model Monitor continuously monitors the quality of machine learning models in production. It automatically detects concept drift, data drift, and other quality issues, enabling teams to take corrective actions."(Reference: Amazon SageMaker Model Monitor Documentation, AWS Certified AI Practitioner Study Guide)A (JumpStart) provides prebuilt solutions and models, not monitoring.B (HyperPod) is for large-scale training, not model monitoring.C (Data Wrangler) is for data preparation, not ongoing model quality monitoring
Question # 10
A company wants to use a pre-trained generative AI model to generate content for its marketing campaigns. The company needs to ensure that the generated content aligns with the company's brand voice and messaging requirements.Which solution meets these requirements?
A. Optimize the model's architecture and hyperparameters to improve the model's overall performance. B. Increase the model's complexity by adding more layers to the model's architecture. C. Create effective prompts that provide clear instructions and context to guide the model's generation. D. Select a large, diverse dataset to pre-train a new generative model.
Answer: C ExplanationCreating effective prompts is the best solution to ensure that the content generated by a pre-trained generative AI model aligns with the company's brand voice and messaging requirements.Effective Prompt Engineering:Involves crafting prompts that clearly outline the desired tone, style, and content guidelines for the model.By providing explicit instructions in the prompts, the company can guide the AI to generate content that matches the brand’s voice and messaging.Why Option C is Correct:Guides Model Output: Ensures the generated content adheres to specific brand guidelines by shaping the model's response through the prompt.Flexible and Cost-effective: Does not require retraining or modifying the model, which is more resourceefficient.Why Other Options are Incorrect:A. Optimize the model's architecture and hyperparameters: Improves model performance but does not specifically address alignment with brand voice.B. Increase model complexity: Adding more layers may not directly help with content alignment.D. Pre-training a new model: Is a costly and time-consuming process that is unnecessary if the goal is content alignment.
