Amazon Web Services AIF-C01 - AWS Certified AI Practitioner Exam

The best solution is Amazon Bedrock Knowledge Bases, which allows for the seamless integration of structured and unstructured internal documents—such as PDFs, CSVs, and extracted image text—into a retrieval-augmented generation (RAG) pipeline. According to AWS documentation, Bedrock Knowledge Bases offer a no-code or low-code setup to link your enterprise data with foundation models for context-aware responses, without needing to fine-tune or retrain models. The system indexes documents in an Amazon S3 bucket, creates embeddings, and stores them in a vector store. At inference time, the model retrieves relevant context and incorporates it into its response. This approach provides dynamic and up-to-date responses while maintaining data privacy, with minimal operational overhead. Unlike fine-tuning or building a model from scratch in SageMaker, which requires considerable compute resources and model management, Bedrock Knowledge Bases are serverless and easy to configure. It is designed exactly for internal knowledge AI assistants.

Referenced AWS AI/ML Documents and Study Guides:

Amazon Bedrock Developer Guide – Knowledge Bases

AWS Generative AI Best Practices – RAG Patterns for Enterprise Search

AWS CloudTrail is a service that enables governance, compliance, and operational and risk auditing of your AWS account. It tracks API calls and identifies unauthorized access attempts to AWS resources, including Amazon Bedrock.

AWS CloudTrail:

Provides detailed logs of all API calls made within an AWS account, including those to Amazon Bedrock.

Can identify unauthorized access attempts by logging and monitoring the API calls, which helps in setting appropriate IAM policies and roles.

Why Option B is Correct:

Monitoring and Security: CloudTrail logs all access requests and helps detect unauthorized access attempts.

Auditing and Compliance: The logs can be used to audit user activity and enforce security measures.

Why Other Options are Incorrect:

A. AWS Audit Manager: Used for automating audit preparation, not for tracking real-time unauthorized access attempts.

C. Amazon Fraud Detector: Designed to detect fraudulent online activities, not unauthorized access to AWS services.

D. AWS Trusted Advisor: Provides best practice recommendations for AWS resources, not access monitoring.

Thus, B is the correct answer for identifying unauthorized users attempting to access Amazon Bedrock.

The ecommerce company’s chatbot, powered by AI, automates customer order submissions and is accessible 24/7 via the website. Prompt injection is an AI system input vulnerability where malicious users craft inputs to manipulate the chatbot’s behavior, such as bypassing safeguards or accessing unauthorized information. This vulnerability must be resolved before the chatbot is made available to ensure security.

Exact Extract from AWS AI Documents:

From the AWS Bedrock User Guide:

"Prompt injection is a vulnerability in AI systems, particularly chatbots, where malicious inputs can manipulate the model’s behavior, potentially leading to unauthorized actions or harmful outputs. Implementing guardrails and input validation can mitigate this risk."

(Source: AWS Bedrock User Guide, Security Best Practices)

Detailed Explanation:

Option A: Data leakageData leakage refers to the unintended exposure of sensitive data during model training or inference, not an input vulnerability affecting a chatbot’s operation.

Option B: Prompt injectionThis is the correct answer. Prompt injection is a critical input vulnerability for chatbots, where malicious prompts can exploit the AI to produce harmful or unauthorized responses, a risk that must be addressed before launch.

Option C: Large language model (LLM) hallucinationsLLM hallucinations refer to the model generating incorrect or ungrounded responses, which is an output issue, not an input vulnerability.

Option D: Concept driftConcept drift occurs when the data distribution changes over time, affecting model performance. It is not an input vulnerability but a long-term performance issue.

Unsupervised learning involves training a model on unlabeled data, letting it find patterns or groupings on its own, without explicit outputs provided. Clustering is a primary unsupervised learning technique.

Option A is correct: Grouping customers based on purchase history (without predefined categories) is clustering, a classic unsupervised task.

B and C are supervised learning (classification and regression, respectively).

D is reinforcement learning, not unsupervised learning.

"Unsupervised learning involves training on data without labels and is often used for clustering or dimensionality reduction."

(Reference: AWS Certified AI Practitioner Official Study Guide, AWS ML Concepts)

Amazon SageMaker Serverless Inference is the correct solution for deploying an ML model to production in a way that allows a web application to use the model without the need to manage the underlying infrastructure.

Amazon SageMaker Serverless Inference provides a fully managed environment for deploying machine learning models. It automatically provisions, scales, and manages the infrastructure required to host the model, removing the need for the company to manage servers or other underlying infrastructure.

Why Option A is Correct:

No Infrastructure Management: SageMaker Serverless Inference handles the infrastructure management for deploying and serving ML models. The company can simply provide the model and specify the required compute capacity, and SageMaker will handle the rest.

Cost-Effectiveness: The serverless inference option is ideal for applications with intermittent or unpredictable traffic, as the company only pays for the compute time consumed while handling requests.

Integration with Web Applications: This solution allows the model to be easily accessed by web applications via RESTful APIs, making it an ideal choice for hosting the model and serving predictions.

Why Other Options are Incorrect:

B. Use Amazon CloudFront to deploy the model: CloudFront is a content delivery network (CDN) service for distributing content, not for deploying ML models or serving predictions.

C. Use Amazon API Gateway to host the model and serve predictions: API Gateway is used for creating, deploying, and managing APIs, but it does not provide the infrastructure or the required environment to host and run ML models.

D. Use AWS Batch to host the model and serve predictions: AWS Batch is designed for running batch computing workloads and is not optimized for real-time inference or hosting machine learning models.

Thus, A is the correct answer, as it aligns with the requirement of deploying an ML model without managing any underlying infrastructure.

The context window determines how much information can fit into a single prompt when using a large language model (LLM) like those on Amazon Bedrock.

Context Window:

The context window is the maximum amount of text (measured in tokens) that a language model can process in a single pass.

For LLM applications, the size of the context window limits how much input data, such as text for sentiment analysis, can be fed into the model at once.

Why Option B is Correct:

Determines Prompt Size: The context window size directly informs how much information (e.g., words or sentences) can fit in one prompt.

Model Capacity: The larger the context window, the more information the model can consider for generating outputs.

Why Other Options are Incorrect:

A. Temperature: Controls randomness in model outputs but does not affect the prompt size.

C. Batch size: Refers to the number of training samples processed in one iteration, not the amount of information in a prompt.

D. Model size: Refers to the number of parameters in the model, not the input size for a single prompt.

Amazon Bedrock Agents provide the capability to automate repetitive tasks and orchestrate complex workflows using generative AI models. This is particularly beneficial for customer support inquiries, where quick and efficient processing is crucial.

Option B (Correct): "Automation of repetitive tasks and orchestration of complex workflows": This is the correct answer because Bedrock Agents can automate common customer service tasks and streamline complex processes, improving response times and efficiency.

Option A: "Generation of custom foundation models (FMs) to predict customer needs" is incorrect as Bedrock agents do not create custom models.

Option C: "Automatically calling multiple foundation models (FMs) and consolidating the results" is incorrect because Bedrock agents focus on task automation rather than combining model outputs.

Option D: "Selecting the foundation model (FM) based on predefined criteria and metrics" is incorrect as Bedrock agents are not designed for selecting models.

AWS AI Practitioner References:

Amazon Bedrock Documentation: AWS explains that Bedrock Agents automate tasks and manage complex workflows, making them ideal for customer support automation.

Amazon S3 is the optimal choice for storing and uploading datasets used for machine learning model validation and training. It offers scalable, durable, and secure storage, making it ideal for holding datasets required by Amazon Bedrock for validation purposes.

Option A (Correct): "Amazon S3": This is the correct answer because Amazon S3 is widely used for storing large datasets that are accessed by machine learning models, including those in Amazon Bedrock.

Option B: "Amazon Elastic Block Store (Amazon EBS)" is incorrect because EBS is a block storage service for use with Amazon EC2, not for directly storing datasets for Amazon Bedrock.

Option C: "Amazon Elastic File System (Amazon EFS)" is incorrect as it is primarily used for file storage with shared access by multiple instances.

Option D: "AWS Snowcone" is incorrect because it is a physical device for offline data transfer, not suitable for directly providing data to Amazon Bedrock.

AWS AI Practitioner References:

Storing and Managing Datasets on AWS for Machine Learning: AWS recommends using S3 for storing and managing datasets required for ML model training and validation.

AWS References:

Amazon Bedrock – Retrieval Augmented Generation (RAG)

AWS Generative AI Guide – Zero-shot & Few-shot learning

BLEU (Bilingual Evaluation Understudy) is a metric used to evaluate the accuracy of machine-generated translations by comparing them against reference translations. It is commonly used for translation tasks to measure how close the generated output is to professional human translations.

Option A (Correct): "Bilingual Evaluation Understudy (BLEU)": This is the correct answer because BLEU is specifically designed to evaluate the quality of translations, making it suitable for the company's use case.

Option B: "Root mean squared error (RMSE)" is incorrect because RMSE is used for regression tasks to measure prediction errors, not translation quality.

Option C: "Recall-Oriented Understudy for Gisting Evaluation (ROUGE)" is incorrect as it is used to evaluate text summarization, not translation.

Option D: "F1 score" is incorrect because it is typically used for classification tasks, not for evaluating translation accuracy.

AWS AI Practitioner References:

Model Evaluation Metrics on AWS: AWS supports various metrics like BLEU for specific use cases, such as evaluating machine translation models.