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Category Breakdown Category Number of Questions AI and ML Concepts 4 AWS AI/ML Services and Tools 3 Generative AI and LLMs 2 Responsible AI and Governance 1 TOTAL 10 Question #:1 - [AI and ML Concepts] A company wants to build an ML model by using Amazon SageMaker. The company needs to share and manage variables for model development across multiple teams. Which SageMaker feature meets these requirements? Amazon SageMaker Feature Store Amazon SageMaker Data Wrangler Amazon SageMaker Clarify Amazon SageMaker Model Cards Answer: A Explanation Amazon SageMaker Feature Store is the correct solution for sharing and managing variables (features) across multiple teams during model development. Amazon SageMaker Feature Store: A fully managed repository for storing, sharing, and managing machine learning features across different teams and models. It enables collaboration and reuse of features, ensuring consistent data usage and reducing redundancy. Why Option A is Correct: Centralized Feature Management: Provides a central repository for managing features, making it easier to share them across teams. Collaboration and Reusability: Improves efficiency by allowing teams to reuse existing features instead of creating them from scratch. Why Other Options are Incorrect: B. SageMaker Data Wrangler: Helps with data preparation and analysis but does not provide a centralized feature store. Amazon Web Services - AIF-C01 Pass Guaranteed 2 of 10 Only Solution2Pass for Any Exam A. B. C. D. A. B. C. C. SageMaker Clarify: Used for bias detection and explainability, not for managing variables across teams. D. SageMaker Model Cards: Provide model documentation, not feature management. Question #:2 - [AI and ML Concepts] A medical company wants to develop an AI application that can access structured patient records, extract relevant information, and generate concise summaries. Which solution will meet these requirements? Use Amazon Comprehend Medical to extract relevant medical entities and relationships. Apply rule- based logic to structure and format summaries. Use Amazon Personalize to analyze patient engagement patterns. Integrate the output with a general purpose text summarization tool. Use Amazon Textract to convert scanned documents into digital text. Design a keyword extraction system to generate summaries. Implement Amazon Kendra to provide a searchable index for medical records. Use a template-based system to format summaries. Answer: A Explanation Amazon Comprehend Medical is designed for processing medical records and extracting key clinical entities, useful for summaries. Per the AWS Comprehend Medical documentation: “Amazon Comprehend Medical enables extraction of relevant medical information from unstructured clinical text such as medications, conditions, and relationships, making it ideal for summarization tasks.” Question #:3 - [AI and ML Concepts] A company wants to use large language models (LLMs) with Amazon Bedrock to develop a chat interface for the company's product manuals. The manuals are stored as PDF files. Which solution meets these requirements MOST cost-effectively? Use prompt engineering to add one PDF file as context to the user prompt when the prompt is submitted to Amazon Bedrock. Use prompt engineering to add all the PDF files as context to the user prompt when the prompt is submitted to Amazon Bedrock. Use all the PDF documents to fine-tune a model with Amazon Bedrock. Use the fine-tuned model to process user prompts. Amazon Web Services - AIF-C01 Pass Guaranteed 3 of 10 Only Solution2Pass for Any Exam D. A. B. C. D. Upload PDF documents to an Amazon Bedrock knowledge base. Use the knowledge base to provide context when users submit prompts to Amazon Bedrock. Answer: A Explanation Using Amazon Bedrock with large language models (LLMs) allows for efficient utilization of AI to answer queries based on context provided in product manuals. To achieve this cost-effectively, the company should avoid unnecessary use of resources. Option A (Correct): "Use prompt engineering to add one PDF file as context to the user prompt when the prompt is submitted to Amazon Bedrock": This is the most cost-effective solution. By using prompt engineering, only the relevant content from one PDF file is added as context to each query. This approach minimizes the amount of data processed, which helps in reducing costs associated with LLMs' computational requirements. Option B: "Use prompt engineering to add all the PDF files as context to the user prompt when the prompt is submitted to Amazon Bedrock" is incorrect. Including all PDF files would increase costs significantly due to the large context size processed by the model. Option C: "Use all the PDF documents to fine-tune a model with Amazon Bedrock" is incorrect. Fine-tuning a model is more expensive than using prompt engineering, especially if done for multiple documents. Option D: "Upload PDF documents to an Amazon Bedrock knowledge base" is incorrect because Amazon Bedrock does not have a built-in knowledge base feature for directly managing and querying PDF documents. AWS AI Practitioner References: Prompt Engineering for Cost-Effective AI: AWS emphasizes the importance of using prompt engineering to minimize costs when interacting with LLMs. By carefully selecting relevant context, users can reduce the amount of data processed and save on expenses. Question #:4 - [AWS AI/ML Services and Tools] A retail store wants to predict the demand for a specific product for the next few weeks by using the Amazon SageMaker DeepAR forecasting algorithm. Which type of data will meet this requirement? Text data Image data Time series data Binary data Answer: C Amazon Web Services - AIF-C01 Pass Guaranteed 4 of 10 Only Solution2Pass for Any Exam A. B. C. D. Explanation Amazon SageMaker's DeepAR is a supervised learning algorithm designed for forecasting scalar (one- dimensional) time series data. Time series data consists of sequences of data points indexed in time order, typically with consistent intervals between them. In the context of a retail store aiming to predict product demand, relevant time series data might include historical sales figures, inventory levels, or related metrics recorded over regular time intervals (e.g., daily or weekly). By training the DeepAR model on this historical time series data, the store can generate forecasts for future product demand. This capability is particularly useful for inventory management, staffing, and supply chain optimization. Other data types, such as text, image, or binary data, are not suitable for time series forecasting tasks and would not be appropriate inputs for the DeepAR algorithm. Reference: Amazon SageMaker DeepAR Algorithm Question #:5 - [AWS AI/ML Services and Tools] Which component of Amazon Bedrock Studio can help secure the content that AI systems generate? Access controls Function calling Guardrails Knowledge bases Answer: C Explanation Amazon Bedrock Studio provides tools to build and manage generative AI applications, and the company needs a component to secure the content generated by AI systems. Guardrails in Amazon Bedrock are designed to ensure safe and responsible AI outputs by filtering harmful or inappropriate content, making them the key component for securing generated content. Exact Extract from AWS AI Documents: From the AWS Bedrock User Guide: "Guardrails in Amazon Bedrock provide mechanisms to secure the content generated by AI systems by filtering out harmful or inappropriate outputs, such as hate speech, violence, or misinformation, ensuring responsible AI usage." (Source: AWS Bedrock User Guide, Guardrails for Responsible AI) Detailed Explanation: Option A: Access controlsAccess controls manage who can use or interact with the AI system but do not directly secure the content generated by the system. Amazon Web Services - AIF-C01 Pass Guaranteed 5 of 10 Only Solution2Pass for Any Exam Option B: Function callingFunction calling enables AI models to interact with external tools or APIs, but it is not related to securing generated content. Option C: GuardrailsThis is the correct answer. Guardrails in Amazon Bedrock secure generated content by filtering out harmful or inappropriate material, ensuring safe outputs. Option D: Knowledge basesKnowledge bases provide data for AI models to generate responses but do not inherently secure the content that is generated. References: AWS Bedrock User Guide: Guardrails for Responsible AI (https://docs.aws.amazon.com/bedrock/latest /userguide/guardrails.html) AWS AI Practitioner Learning Path: Module on Responsible AI and Model Safety Amazon Bedrock Developer Guide: Securing AI Outputs (https://aws.amazon.com/bedrock/) Question #:6 - [AWS AI/ML Services and Tools] A company is using Amazon SageMaker to develop AI models. Select the correct SageMaker feature or resource from the following list for each step in the AI model lifecycle workflow. Each SageMaker feature or resource should be selected one time or not at all. (Select TWO.) SageMaker Clarify SageMaker Model Registry SageMaker Serverless Inference Amazon Web Services - AIF-C01 Pass Guaranteed 6 of 10 Only Solution2Pass for Any Exam Answer: Explanation SageMaker Model Registry, SageMaker Serverless interference This question requires selecting the appropriate Amazon SageMaker feature for two distinct steps in the AI model lifecycle. Let’s break down each step and evaluate the options: Step 1: Managing different versions of the model The goal here is to identify a SageMaker feature that supports version control and management of machine learning models. Let’s analyze the options: SageMaker Clarify : This feature is used to detect bias in models and explain model predictions, helping with fairness and interpretability. It does not provide functionality for managing model versions. SageMaker Model Registry : This is a centralized repository in Amazon SageMaker that allows users to catalog, manage, and track different versions of machine learning models. It supports model versioning, approval workflows, and deployment tracking, making it ideal for managing different versions of a model. SageMaker Serverless Inference : This feature enables users to deploy models for inference without managing servers, automatically scaling based on demand. It is focused on inference (predictions), not on managing model versions. Conclusion for Step 1 : The is the correct choice for managing different SageMaker Model Registry versions of the model. Amazon Web Services - AIF-C01 Pass Guaranteed 7 of 10 Only Solution2Pass for Any Exam Exact Extract Reference : According to the AWS SageMaker documentation, “The SageMaker Model Registry allows you to catalog models for production, manage model versions, associate metadata, and manage approval status for deployment.” (Source: AWS SageMaker Documentation - Model Registry, https://docs.aws.amazon.com/sagemaker/latest/dg/model-registry.html). Step 2: Using the current model to make predictions The goal here is to identify a SageMaker feature that facilitates making predictions (inference) with a deployed model. Let’s evaluate the options: SageMaker Clarify : As mentioned, this feature focuses on bias detection and explainability, not on performing inference or making predictions. SageMaker Model Registry : While the Model Registry helps manage and catalog models, it is not used directly for making predictions. It can store models, but the actual inference process requires a deployment mechanism. SageMaker Serverless Inference : This feature allows users to deploy models for inference without managing infrastructure. It automatically scales based on traffic and is specifically designed for making predictions in a cost-efficient, serverless manner. Conclusion for Step 2 : is the correct choice for using the current model to SageMaker Serverless Inference make predictions. Exact Extract Reference : The AWS documentation states, “SageMaker Serverless Inference is a deployment option that allows you to deploy machine learning models for inference without configuring or managing servers. It automatically scales to handle inference requests, making it ideal for workloads with intermittent or unpredictable traffic.” (Source: AWS SageMaker Documentation - Serverless Inference, https://docs.aws. amazon.com/sagemaker/latest/dg/serverless-inference.html). Why Not Use the Same Feature Twice? The question specifies that each SageMaker feature or resource should be selected one time or not at all. Since is used for version management and is used SageMaker Model Registry SageMaker Serverless Inference for predictions, each feature is selected exactly once. is not applicable to either step, so it SageMaker Clarify is not selected at all, fulfilling the question’s requirements. : AWS SageMaker Documentation: Model Registry (https://docs.aws.amazon.com/sagemaker/latest/dg/model- registry.html) AWS SageMaker Documentation: Serverless Inference (https://docs.aws.amazon.com/sagemaker/latest/dg /serverless-inference.html) AWS AI Practitioner Study Guide (conceptual alignment with SageMaker features for model lifecycle management and inference) Let’s format this question according to the specified structure and provide a detailed, verified answer based on AWS AI Practitioner knowledge and official AWS documentation. The question focuses on selecting an Amazon Web Services - AIF-C01 Pass Guaranteed 8 of 10 Only Solution2Pass for Any Exam A. B. C. D. A. B. C. D. A. B. AWS database service that supports storage and queries of embeddings as vectors, which is relevant to generative AI applications. Question #:7 - [Generative AI and LLMs] A company needs an automated solution to group Its customers Into multiple categories. The company does not want to manually define the categories. Which ML technique should the company use? Classification Linear regression Logistic regression Clustering Answer: D Question #:8 - [AI and ML Concepts] A company needs to choose a model from Amazon Bedrock to use internally. The company must identify a model that generates responses in a style that the company's employees prefer. What should the company do to meet these requirements? Evaluate the models by using built-in prompt datasets. Evaluate the models by using a human workforce and custom prompt datasets. Use public model leaderboards to identify the model. Use the model InvocationLatency runtime metrics in Amazon CloudWatch when trying models. Answer: B Question #:9 - [Responsible AI and Governance] A hospital is developing an AI system to assist doctors in diagnosing diseases based on patient records and medical images. To comply with regulations, the sensitive patient data must not leave the country the data is located in. Which data governance strategy will ensure compliance and protect patient privacy? Data residency Amazon Web Services - AIF-C01 Pass Guaranteed 9 of 10 Only Solution2Pass for Any Exam B. C. D. A. B. C. D. Data quality Data discoverability Data enrichment Answer: A Explanation Data residency is the principle and practice of ensuring that data remains within a specific geographic location or jurisdiction, often to comply with local regulations and privacy laws (such as HIPAA, GDPR, or national healthcare laws). Data residency policies prevent sensitive data (such as patient records) from being transferred or accessed outside the designated country, thus protecting privacy and ensuring regulatory compliance. A is correct: “Data residency refers to where data is stored geographically, and often organizations need to ensure that certain data does not leave a particular country or region to comply with legal or regulatory requirements.” (Reference: AWS Data Residency Whitepaper, AWS Responsible AI & Data Privacy) B (data quality) refers to the accuracy and reliability of data, not its location. C (data discoverability) is about being able to find and access data, not restricting its movement. D (data enrichment) is about enhancing data with additional information. “Maintaining data residency is critical in healthcare and regulated industries to ensure data does not leave the prescribed jurisdiction.” (Reference: AWS Data Residency) Question #:10 - [Generative AI and LLMs] Which scenario represents a practical use case for generative AI? Using an ML model to forecast product demand Employing a chatbot to provide human-like responses to customer queries in real time Using an analytics dashboard to track website traffic and user behavior Implementing a rule-based recommendation engine to suggest products to customers Answer: B Explanation Amazon Web Services - AIF-C01 Pass Guaranteed 10 of 10 Only Solution2Pass for Any Exam Generative AI is a type of AI that creates new content, such as text, images, or audio, often mimicking human- like outputs. A practical use case for generative AI is employing a chatbot to provide human-like responses to customer queries in real time, as it leverages the ability of large language models (LLMs) to generate natural language responses dynamically. Exact Extract from AWS AI Documents: From the AWS Bedrock User Guide: "Generative AI enables applications like chatbots to produce human-like text responses in real time, enhancing customer support by providing natural and contextually relevant answers to user queries." (Source: AWS Bedrock User Guide, Introduction to Generative AI) Detailed Explanation: Option A: Using an ML model to forecast product demandForecasting product demand typically involves predictive analytics using supervised learning (e.g., regression models), not generative AI, which focuses on creating new content. Option B: Employing a chatbot to provide human-like responses to customer queries in real timeThis is the correct answer. Generative AI, particularly LLMs, is commonly used to power chatbots that generate human- like responses, making this a practical use case. Option C: Using an analytics dashboard to track website traffic and user behaviorAn analytics dashboard involves data visualization and analysis, not generative AI, which is about creating new content. Option D: Implementing a rule-based recommendation engine to suggest products to customersA rule-based recommendation engine relies on predefined rules, not generative AI. Generative AI could be used for more dynamic recommendations, but this scenario does not describe such a case. References: AWS Bedrock User Guide: Introduction to Generative AI (https://docs.aws.amazon.com/bedrock/latest /userguide/what-is-bedrock.html) AWS AI Practitioner Learning Path: Module on Generative AI Applications AWS Documentation: Generative AI Use Cases (https://aws.amazon.com/generative-ai/) About solution2pass.com solution2pass.com was founded in 2007. 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